2023 Research Projects
Projects are posted below; new projects will continue to be posted. To learn more about the type of research conducted by undergraduates, view the archived symposium booklets and search the past SURF projects.
This is a list of research projects that may have opportunities for undergraduate students. Please note that it is not a complete list of every SURF project. Undergraduates will discover other projects when talking directly to Purdue faculty.
You can browse all the projects on the list or view only projects in the following categories:
All Research Projects (155)
360 degree video streaming research testbed
Description:
360 degree video streaming is a new form of video streaming where video is captured using a 360 degree video camera, and users may select which perspectives are of interest to them. Using traditional approaches to video streaming can result in 5X-6X bandwidth overhead. It is desirable to only show a user the portion of the video relevant to her, but user motion can lead to stalls, or degraded experience. We are developing new algorithms and solutions for 360 degree streaming with smooth user experience while achieving significant bandwidth savings.
In the summer project, we are looking to build a complete end to end prototype, where users can engage in 360 streaming from mobile devices and Oculus headsets. We would like to work with real content producers (e.g., Purdue commencement) to perform field trials of our 360 video streaming system. Doing so can also allow for A/B testing of different algorithms and provide data on how users engage with such content.
In the summer project, we are looking to build a complete end to end prototype, where users can engage in 360 streaming from mobile devices and Oculus headsets. We would like to work with real content producers (e.g., Purdue commencement) to perform field trials of our 360 video streaming system. Doing so can also allow for A/B testing of different algorithms and provide data on how users engage with such content.
Research categories:
Human Factors, Mobile Computing, Other
Preferred major(s):
- Computer Engineering
Desired experience:
The main skillsets are: (i) strong Computer Engineering skills, especially in systems and software building; and (ii) an interest and passion in building working real-world research prototypes and evaluations among real users. Strong background in C++ is preferred, and an ability to develop code for mobile devices, and devices such as Oculus headsets will be required.
Desired course work include: Operating Systems, and Computer Networking.
Other requirements include C++/Java (ECE 30862/39595) and Data Structures (ECE 368).
School/Dept.:
Electrical and Computer Engineering
Professor:
Sanjay
Rao
More information: https://engineering.purdue.edu/~isl/
3D Hand and Object Interaction with Machine Learning and Human-Computer-Interaction Techniques
Description:
The student will be studying the basic concepts of programming techniques, as well as Machine Learning and Human-Computer-Interaction techniques. Once the knowledge is well obtained, the student will be involved in a research project working on the topic of 3D reconstruction of Hand and Object interaction, utilizing the skill learned. The final stage of this project will be an academic publication and a detailed report on the topics being discussed over the semester. Three credit hours are to be registered, and the student is expected to work 10-12 hours per week on this project.
Research categories:
Deep Learning, Human Factors
Preferred major(s):
- No Major Restriction
School/Dept.:
Electrical & Computer Engineering
Professor:
Alex
Quinn
3D Printing Multi-material Ceramics
Description:
This project is funded by NASA Marshall Space Flight Center and we will be directly working with experts from there. The goal is to use 3D printing to create multi-material structures that can be applied to solid rocket nozzles and thermal protection systems. The researcher will apply material science and manufacturing science principles to the project. The work will be done at Zucrow Laboratories.
Research categories:
Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Basic 3D printing experience
Exposure to material science
School/Dept.:
Mechanical Engineering
Professor:
Monique
McClain
More information: https://mcclain.team/
4-dimensional ultrasound assessment of cardiac remodeling during pregnancy and postpartum lactation
Description:
While the effect of lactation on the health of neonates is commonly studied, its consequence on maternal health is still ambiguous. Previous work has suggested that complications of pregnancy are often associated with increased risk of cardiovascular disease, further complicating the long-lasting effects of pregnancy. Conversely, other research suggests that longer lactation periods may decrease the risk of cardiovascular diseases. Thus, we aim here to understand how pregnancy and lactation affect cardiovascular remodeling and if these changes could be attributed to altered risk of cardiovascular diseases. This project aims to better understand the cardiac remodeling process throughout normal pregnancy and lactation during the post-partum period. Four-dimensional ultrasound scans of the heart are acquired at several timepoints throughout gestation and post-partum. These scans will be used to quantify left ventricular geometry and function in normal pregnancies and during lactation. Image analysis of the ultrasound images will be performed using a custom MATLAB GUI. We will compare the postpartum cardiovascular remodeling that occurs in lactating and non-lactating mice. Analysis of the cardiac scans will provide information relating to the left ventricle volume, ejection fraction, and LV wall thickness. These metrics will provide conclusions and recommendations for further research in this area.
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging
Preferred major(s):
- Biomedical Engineering
- Computer Engineering
School/Dept.:
BME
Professor:
Craig
Goergen
More information: https://engineering.purdue.edu/cvirl
AAMP-UP Project 10: Detonation Wave Shaping
Description:
This research topic seeks to explore the interaction with detonation waves with inclusions (voids, inerts, other explosives). It will involve advanced sample preparation, including microscale machining of energetic materials, as well as high rate experiments including high-speed imaging including streak imaging. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 11: Post Detonation Fireballs and Explosive Driven Shock Waves
Description:
This research topic seeks to explore the reactions and induced gas shocks occurring after a detonation. This includes It will involve advanced sample preparation of the explosives and added metal fuels or other additibes, and dynamic experiments. This project will focus on applying advanced laser diagnostics to both the induced shock and post-detonation reactions. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 12: Computational study of energetic materials
Description:
Overarching Goal: Enable discovery of the physical mechanisms driving impact-induced reactions in explosives (such as interparticle relative velocities and contact pressures) and their correlation with material properties, formulation, and processing.
Project Description: Energetic materials used in the propulsion industry, and particle-binder composites in general, consist of (explosive) crystals in a binder matrix. The mechanical properties and performance of these materials are strongly correlated to their microstructure. Specifically, the mechanical response under weak impact loads is characterized by long-range microstructural correlations (typical of granular materials) that need to be resolved with high statistical significance in order to understand how complex phenomena (such as detonation) emerges from local simpler interactions. The interplay between these long-range interactions and local mechanisms (such as deformation, failure and, ultimately, hot spot formation) is responsible for the sensitivity of the material.
A mesoscale modeling methodology, capable of modeling the dynamic response of large energetic composites (>50,000 crystals) by analytically upscaling complex crystal-binder deformation and failure mechanics into closed-form contact laws, will be used to build time-dependent multivariate probability distribution functions of microstructural features.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Project Description: Energetic materials used in the propulsion industry, and particle-binder composites in general, consist of (explosive) crystals in a binder matrix. The mechanical properties and performance of these materials are strongly correlated to their microstructure. Specifically, the mechanical response under weak impact loads is characterized by long-range microstructural correlations (typical of granular materials) that need to be resolved with high statistical significance in order to understand how complex phenomena (such as detonation) emerges from local simpler interactions. The interplay between these long-range interactions and local mechanisms (such as deformation, failure and, ultimately, hot spot formation) is responsible for the sensitivity of the material.
A mesoscale modeling methodology, capable of modeling the dynamic response of large energetic composites (>50,000 crystals) by analytically upscaling complex crystal-binder deformation and failure mechanics into closed-form contact laws, will be used to build time-dependent multivariate probability distribution functions of microstructural features.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Mechanical Engineering
Professor:
Marcial
Gonzalez
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 13: Conducting Polymer Energetic Binders
Description:
The overarching objective of this project is to create polymeric binders that have robust electrical and mechanical properties. This will be achieved by modifying commercially-available materials as well as synthesizing next-generation conducting polymers. By developing the appropriate structure-property-processing relationships, we will develop, and eventually deploy, binders with electronically-triggerable properties. Specifically, the student associated with this project will focus on the design and mechanical testing of polymers and polymer-based binders for energetic materials applications.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Chemical Engineering
Professor:
Bryan
Boudouris
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 14: Direct Visualization of Stress Fields in Model Composite Systems
Description:
When a polymer matrix composite is deformed, the stresses in the matrix near the reinforcing phase can vary significantly from those in the bulk. Utilizing molecular force sensors known as mechanophores, we have developed a method to measure the local stress field in a deformed composite comprised of a rigid spherical particle in a silicone matrix. This REU project will continue this research by investigating the local stress field that develops around two particles in close proximity to one another. Understanding the changes in the stress field between two particles will allow us to develop more comprehensive models of composite deformation modes. The student will be responsible for fabricating samples and performing micromechanical samples over a fluorescent microscope. The student will learn mechanical testing methods and analysis, laser scanning confocal microscopy, and image analysis techniques.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Materials Engineering
Professor:
Chelsea
Davis
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 15: In-situ Diagnosis of Additive Manufacturing with 3D Vision Sensor
Description:
This project aims at designing a 3D printer that can incorporate a high-end customized 3D vision sensor for close-loop controls. Undergrads will closely work with graduate students on both software and/or hardware development depending upon interest. Hardware work involves component identification, system design, fabrication, and integration. Software work includes algorithms for fault diagnosis and closed-loop control strategies.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Mechanical Engineering
Professor:
Song
Zhang
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 16: Additive Manufacturing
Description:
This research project seeks to additively manufacture (3D print) highly viscous materials using a novel 3D-printing method: Vibration Assisted Printing (VAP). This technique uses high frequency vibrations concentrated at the tip of the printing nozzle to enable flow of viscous materials at low pressures and temperatures. VAP has the potential to create next-generation munitions with more precision, customizability, and safety than traditional additive manufacturing methods. The objective of this project is to design formulations which are capable of being vibration-assisted printed, maintain energetic performance, and retain desirable mechanical properties after printing. The REU student would be mentored by graduate students and work within a team to design experiments, perform experiments, analyze data, and disseminate the results. The REU student will have the opportunity to present the findings in regular meetings, poster sessions, formal presentations, and papers.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Mechanical Engineering
Professor:
Jeff
Rhoads
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 17: Computational Studies of Polymer Stability
Description:
The students working on this project will learn how to use state-of-the-art computational tools for characterizing the degradation mechanisms of polymers. Students will use these tools to understand structure-stability relationships of new polymers. Depending on progress, the data generated from these studies will be used to train machine learning models to accelerate predictions on new systems.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Chemical Engineering
Professor:
Brett
Savoie
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 18: Polymer Derived Ceramics Additive Material Systems for SHM
Description:
Additive manufacturing and real-world applications have been seen in the past as two
separate entities that are increasing coalescing over the years. With more recent advancements in 3D-printing technology, the two paths have now come to an intersection. Specifically, in the case of resin 3D-printing, the amount of detail possible allows for practical components to
be manufactured from advanced engineering materials for a wide variety of applications. In
this particular work, the focus has been is to define manufacturing methods that allow for the
embedding of sensors into ceramic media as well as show the feasibility of doing so.
The ADAMs lab is currently exploring techniques to create multi-functional material systems utilizing SLA. Candidate projects include embedded piezoelectric actuator for sensing applications and shape memory alloy sheets to create localized structural changes in a ceramic structure. The created systems will be tested to see if they can detect real time ballistic events.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
separate entities that are increasing coalescing over the years. With more recent advancements in 3D-printing technology, the two paths have now come to an intersection. Specifically, in the case of resin 3D-printing, the amount of detail possible allows for practical components to
be manufactured from advanced engineering materials for a wide variety of applications. In
this particular work, the focus has been is to define manufacturing methods that allow for the
embedding of sensors into ceramic media as well as show the feasibility of doing so.
The ADAMs lab is currently exploring techniques to create multi-functional material systems utilizing SLA. Candidate projects include embedded piezoelectric actuator for sensing applications and shape memory alloy sheets to create localized structural changes in a ceramic structure. The created systems will be tested to see if they can detect real time ballistic events.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Mechanical Engineering
Professor:
James
Gibert
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 19: A Design of Experiments (DOE) Approach to Obtain Repeatedly Homogeneous Propellant Formulations
Description:
The United States mixes propellants the same way they did in WWII, i.e., with bladed batch mixers. This project optimizes the mixing settings for a safer, bladeless, Dual Asymmetric Centrifuge (DAC) mixer with remote operation capabilities. Specifically, a Design of Experiments (DOE) approach is applied to identify process settings to uniformly mix a mock propellant formulation. Response variables measured include temperature, viscosity, concentration, and mixer motor torque. This study establishes an efficient FlackTek Speedmixer (DAC mixer) procedure for mixing modified double-base propellants.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Chemical Unit Operations, Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Chemical Engineering
Professor:
Stephen
Beaudoin
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 1: Dynamic mechanical properties of mock polymer bonded explosives systems
Description:
Polymer bonded explosives contain a high percentage of explosive particulates bound in a polymer binder, which allows for increased formability and machinability. Understanding how these systems fail during mechanical shock is paramount for continued safe use. This research area explores the behavior of cast and additively manufactured polymer bonded explosives systems through the use of 3 point bend testing. During this project, the student will complete a literature review researching particle based composites, 3D printing of them, and three-point bending tests. The student will work with graduate student advisors to design samples and the testing series, with potential augmentation including preliminary micro X-ray computed tomography for 3-D sample internal visualization, in situ high speed imaging of fracture, and post experimentation failure analysis.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Material Processing and Characterization, Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Materials Engineering
Professor:
Weinong
Chen
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 20: Understanding Adhesion of Energetic Particles
Description:
This project is focused on quantifying the van der Waals adhesion of energetic particles to surfaces of interest. Better understanding how energetic particles adhere to surfaces can improve explosive detection systems and help enhance the performance of polymer-bonded explosives. Atomic force microscopy is used to directly measure the adhesion between energetic particles and binders.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis, Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Chemical Engineering
Professor:
Stephen
Beaudoin
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 21: Energetic Particle Adhesion via Enhanced Centrifuge Method
Description:
Composite solid propellants, consisting of energetic particles embedded in a polymeric binder, are utilized extensively in projectile devices. Additive manufacturing of these propellants is a promising method to enhance their reliability and effectiveness; however, such materials often fail during launch due to insufficient adhesion between components. Hence, it is of utmost importance to maintain a high adhesive force between the particles and the surrounding binder, which would ensure that the required combustion reactions take place even as the projectile moves at high speeds. Thus, we seek to quantify how the adhesive behavior of the particles changes using the enhanced centrifuge method, which implements experimental and computational techniques in order to map apparent centrifugal forces to intermolecular van der Waals forces.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis, Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Chemical Engineering
Professor:
Stephen
Beaudoin
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 2: In-situ nanoindentation of 3D printed vs cast particulate composites
Description:
Polymer bonded explosives contain a high percentage of explosive particulates bound in a polymer binder, which allows for increased formability and machinability. Understanding how these systems fail during mechanical shock is paramount for continued safe use. This research area explores the behavior of cast and additively manufactured polymer bonded explosives systems through the use of nanoindentation and bulk hardness. During this project, the student will complete a literature review researching particle based composites, 3D printing of them, and hardness testing. The student will work with graduate student advisors to design samples and the testing series, with potential augmentation including pre and post experiment micro X-ray computed tomography for 3-D sample internal visualization, Characterization of surface quality of samples, and post experimentation failure analysis.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Material Processing and Characterization, Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Materials Engineering
Professor:
Weinong
Chen
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 3: Machine Learning and Data Collection
Description:
Machine learning (ML) tools are playing an increasingly important role in science and engineering, revealing patterns and providing predictive capabilities not achievable otherwise. This research area explores the utility of machine learning algorithms in the design, development, and characterization of various energetic material systems. Particular emphasis is placed on bringing a data science formalism to the field, with an eye toward both future capability development and more intelligent (and appreciably faster) material formulation and system design. The REU student would work closely with a Research Scientist and graduate student to gather data, analyze it using ML tools, and share these results.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Big Data/Machine Learning, Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 4: Advanced High-Density Fuels in Energetic Materials
Description:
High density fuels, typically metals, are commonly added to propellants and explosives to improve their performance, as well as other factors such as sensitivity and toxicity. This research topic explores the development, small-scale manufacturing, and characterization of high-density fuels in energetic materials. Particular emphasis is placed on emergent material systems, such as aluminum-lithium alloys, oxide-free coated nano-aluminum, and mechanically activated (MA) fuels. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 5: Detonation Dynamics
Description:
This research topic seeks to explore the high-rate mechanics of detonating energetic materials, or under impact or shock. It will involve advanced sample preparation, including microscale machining of energetic materials, as well as high rate experiments. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 6: Reactive Wires to Tailor Propellant Burning Rate
Description:
Of the many techniques that have been employed to increase burning rates, embedding thermally-conductive and/or reactive wires appears to be the approach to do so without increasing sensitivity. We are utilizing our additive manufacturing capabilities, including vibration assisted printing (VAP), to produce both the wires and the propellant. These “wires” may not actually be metals, but include thermally conductive materials such as graphene. The objective of this project is to use both fused deposition modeling (FDM) and direct writing 3D printing techniques to tailor the surface area of propellants dynamically using conductive and reactive wire deposition. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 7: Piezoelectric Energetic Materials
Description:
Piezoelectric energetic materials (piezoenergetics or PEMs) offer the potential for a new generation of smart propellants and pyrotechnics with multifunctional capabilities that can be actively controlled via external stimuli. However, the fundamental physics and chemistry governing energy transfer, energy repartitioning, and chemical reactions/kinetics resulting from external stimulation of PEMs are not well understood. It is envisioned that, by coupling piezoelectric behavior and nanoenergetics, truly smart and switchable materials can result. Specifically, we envision reactive piezoelectric materials with multifunctional properties with reactivity and microstructure that can be controlled and altered by external stimuli including stress, temperature, or electromagnetic fields; while enabling integrated in situ sensing. The REU student would be mentored by two graduate students and would design experiments, perform those experiments, collect data and present/share those results.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 8: Characterization of Material for 3D Printing
Description:
The objective of this project would be to determine the similarity of mass flow rate for a variety of inert and reactive materials, including Ammonium Perchlorate (AP) based propellants. The undergraduate would help 3D print and test these inert mixtures. The undergraduate student would gain experience researching relevant literature, mixing samples, designing experiments, and analyzing the data for the mock materials as well as assisting with the same tests using energetic materials.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Material Processing and Characterization, Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
AAMP-UP Project 9: Advanced Solid Fuels for Hypersonic or RDE Propulsion
Description:
Solid fuels have some potential applications in both hypersonic and rotating detonation engine (RDE) application. Material fabrication, as well as some small-scale testing will be performed. Particular emphasis is placed on emergent material systems, such as materials that can produce the desired gaseous fuels. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share them.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2023. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required.
No summer classes are allowed.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/Energetics/AAMP-UP/index_html
Adhesives at the Beach
Description:
The oceans are home to a diverse collection of animals producing intriguing materials. Mussels, barnacles, oysters, starfish, and kelp are examples of the organisms generating adhesive matrices for affixing themselves to the sea floor. Our laboratory is characterizing these biological materials, designing synthetic polymer mimics, and developing applications. Synthetic mimics of these bioadhesives begin with the chemistry learned from characterization studies and incorporate the findings into bulk polymers. For example, we are mimicking the cross-linking of DOPA-containing adhesive proteins by placing monomers with pendant catechols into various polymer backbones. Adhesion strengths of these new polymers can rival that of the cyanoacrylate “super glues.” Underwater bonding is also appreciable. Future efforts are planned in two different areas: A) Using biobased and biomimetic adhesives as the basis for making new plastic materials, such as systems like carbon fiber reinforced polymers, but with all components sourced sustainably. B) Developing new adhesive systems that function completely underwater.
Research categories:
Biological Characterization and Imaging, Composite Materials and Alloys, Material Processing and Characterization, Medical Science and Technology
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Jonathan
Wilker
Ag-DOST: A friendly and Intelligent Chatbot for Farmers
Description:
The use of an intelligent conversational computer system (chatbot) is gaining acceptability among many industries to provide virtual assistance to customers. Advancements in large language models (LLM) have enabled the development of chatbots like ChatGPT (by OpenAI) and LaMDA (by Google). This project aims to utilize an existing LLM-based chatbot to develop a conversational AI to engage with farmers in a near-human context and convert the conversation into aids for farm decision-making. In this project, one student will work with a Ph.D. student to help with the tasks identified below:
Student Task List:
1. Survey current literature and write a critical synthesis
2. Utilize publicly available APIs/datasets comprising a multitude of media: images, text, videos, and numerical data
3. Design and develop an interactive interface using Optical Character Recognition (OCR)
4. Present weekly progress in the form of PowerPoint presentations
5. Prepare a final report in the form of a research paper
Student Task List:
1. Survey current literature and write a critical synthesis
2. Utilize publicly available APIs/datasets comprising a multitude of media: images, text, videos, and numerical data
3. Design and develop an interactive interface using Optical Character Recognition (OCR)
4. Present weekly progress in the form of PowerPoint presentations
5. Prepare a final report in the form of a research paper
Research categories:
IoT for Precision Agriculture, Mobile Computing
Preferred major(s):
- Agricultural Engineering,
- Agricultural Systems Management
- Computer and Information Technology
- Computer Science
- Computer Engineering
- Electrical Engineering
- Agronomy (multiple concentrations)
Desired experience:
Relevant coursework in Python programming and introductory machine learning, equivalent courses from online platforms, or relevant work experience. Motivation for higher studies and an ability to work independently are desirable.
School/Dept.:
Agricultural and Biological Engineering
Professor:
Dharmendra
Saraswat
More information: https://dad.saraswat.rcac.purdue.edu/
Air Purification with Photocatalysis and Acoustic Filtering
Description:
There are two related projects, both focused on making air safe, including from bioaersols like COVID.
1) Photocatalysis for Air Purification: Photocatalysis is one method for helping degrade harmful airborne particles, like COVID-19, which our lab is investigating in a partnership with a start-up company. Undergraduates interested in designing experimental setups and microbiological experiments are well-suited for this project. Candidates with experience in culturing microorganism/relevant wet lab experience is preferred.
2) Acoustic removal of aerosols: Sound waves can interact with small particles like aerosols, and be used to manipulate their motion. In this project, we aim to invent the first system that can make air safe with sound waves.
1) Photocatalysis for Air Purification: Photocatalysis is one method for helping degrade harmful airborne particles, like COVID-19, which our lab is investigating in a partnership with a start-up company. Undergraduates interested in designing experimental setups and microbiological experiments are well-suited for this project. Candidates with experience in culturing microorganism/relevant wet lab experience is preferred.
2) Acoustic removal of aerosols: Sound waves can interact with small particles like aerosols, and be used to manipulate their motion. In this project, we aim to invent the first system that can make air safe with sound waves.
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Energy and Environment, Engineering the Built Environment, Fluid Modelling and Simulation, Material Modeling and Simulation, Material Processing and Characterization, Nanotechnology
Preferred major(s):
- No Major Restriction
Desired experience:
All applicants should have an interest in photochemistry, microbiology, aerosol sciences, and experimental research. In addition to the required skills mentioned in the points above, applicants with additional experience with some of the following programs are preferred: Python and Adobe Illustrator.
What experience will you gain?
• Hands on research experience and potential co-authorship in high impact journals
• Application of engineering fundamentals to important societal problems
• Research credit hours (and potential opportunities for financial compensation in the summer)
• Networking opportunities with academic and industry leaders
School/Dept.:
Mechanical Engineering
Professor:
David
Warsinger
More information: www.warsinger.com
Artificial Intelligence for Industrial Systems
Description:
Undergraduate researchers enthusiastic about applying artificial intelligence and machine learning (AI/ML) algorithms for a wide variety of data science and engineering-based tasks within industry including data masking, synthetic data generation, cybersecurity, additive manufacturing, software security, and intrusion detection.
Research categories:
Big Data/Machine Learning, Deep Learning, Internet of Things (IoT)
Preferred major(s):
- No Major Restriction
Desired experience:
- Basic programming experience in Python/MATLAB
- enthusiasm for practical applications of linear algebra, statistics, and computer science-based application is preferred.
- strong background in physics and mathematics
School/Dept.:
Nuclear Engineering
Professor:
Hany
Abdel-Khalik
More information: https://engineering.purdue.edu/CYNICS
Artificial Intelligence for Manufacturing in Practice
Description:
The student will work with a group of researchers at Purdue, Harvard and Tuskegee University on an NSF Future Manufacturing project focused on internet of things (IoT) edge devices and artificial intelligence (AI) for manufacturing applications. The IoT devices will be deployed at local manufacturing companies and their data will be used to improve operations.
Research categories:
Big Data/Machine Learning, Deep Learning, Internet of Things (IoT)
Preferred major(s):
- Electrical Engineering
- Mechanical Engineering
- Industrial Engineering
- Computer Science
- Computer Engineering
School/Dept.:
Electrical and Computer Engineering
Professor:
Ali
Shakouri
Artificial Intelligence for Music and Art
Description:
This project will use deep learning models to analyze sequences of data (such as music). The analysis results will trigger a generative model to create visual art (image or video). Different styles of music (such as class, jazz, and rock) will be used as the input. The music will have different tempos. The computer models analyzes the style and tempo of the music and sets the parameters to generate the visual art. Faster music produces fast moving video. The SURF student will evaluate the existing (open source) computer models for music analysis and visual art generation, integrate them, and provide proof-of-concept demonstrations.
Research categories:
Big Data/Machine Learning, Deep Learning, Human Factors
Preferred major(s):
- Computer Engineering
- Computer and Information Technology
- Computer Science
- Music
- Data Science
Desired experience:
Required: At least one course on computer programming. Desired: Knowledge about machine learning and music.
School/Dept.:
Electrical and Computer Engineering
Professor:
Yung-Hsiang
Lu
Biofilms in Hydroponics Systems
Description:
Controlled environment agriculture methods like hydroponics allow for the growth of crops indoors, providing a stable and controlled conditions for year-round food production, even in urban areas. Despite the high level of control, the growth of microbes can be difficult to control and threatens crop viability. Biofilms develop on system surfaces, and can harbor pathogens harmful to plant or human health.
In this project, biofilm development will be investigated in piped systems using flow cytommetry, imaging, and molecular biology methods. Students will grow plants with hydroponics systems and investigate the factors that control biofilm growth. Since biofilms can develop similarly in any piped system, students will also operate a variety of piped systems with controlled conditions. Students will learn a variety of environmental characterization methods and design and develop controlled experiments.
In this project, biofilm development will be investigated in piped systems using flow cytommetry, imaging, and molecular biology methods. Students will grow plants with hydroponics systems and investigate the factors that control biofilm growth. Since biofilms can develop similarly in any piped system, students will also operate a variety of piped systems with controlled conditions. Students will learn a variety of environmental characterization methods and design and develop controlled experiments.
Research categories:
Biological Characterization and Imaging, Cellular Biology, Ecology and Sustainability, Engineering the Built Environment
Preferred major(s):
- No Major Restriction
Desired experience:
While no background is required, a student with biology and/or biology lab experience and background is preferred.
School/Dept.:
Environmental and Ecological Engineering
Professor:
Caitlin
Proctor
Bionic Interfaces Prototyping: Soft Actuator Arrays
Description:
Soft materials (rubber) enable large deformations, creating new opportunities for devices that transform their shape on demand. We are developing methods to control arrays of actuators in a scalable way. These actuator arrays could have future applications in virtual reality devices, miniaturized tunable optics, and devices that control sound waves on demand.
This is a collaborative project that can include work on several topics including machine learning algorithms, programming microcontrollers, designing control electronics, and fabricating robotic devices.
This is a collaborative project that can include work on several topics including machine learning algorithms, programming microcontrollers, designing control electronics, and fabricating robotic devices.
Research categories:
Fabrication and Robotics, Material Processing and Characterization, Microelectronics
Preferred major(s):
- No Major Restriction
Desired experience:
No previous training is necessary, but experience with CAD, polymer processing, and simple control electronics (Arduino, NI DAQ) is an asset.
School/Dept.:
Mechanical Engineering
Professor:
Alex
Chortos
More information: http://chortoslab.com/
Bone Fracture and Microscale Deformation Processes
Description:
We seek to modify the deformation characteristics of bone through a pharmacological treatment. This project would demonstrate such a concept using animal bone. Treated and untreated bone will be made available for the interrogation of bone by x-rays. Students will be engaged in the data interpretation of x-ray scattering experiments on bone, not subjected to mechanical loads or subjected to mechanical loads.
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Material Modeling and Simulation, Material Processing and Characterization, Other
Desired experience:
Materials Characterization, X-ray techniques; Experience in lab work
School/Dept.:
School of Mechanical Engineering
Professor:
Thomas
Siegmund
More information: https://engineering.purdue.edu/MYMECH
CISTAR - Decarbonization of the High-Carbon Intensive and High-Volume Commodity Chemicals Production through Renewable Electrification
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue
Electrification of industrial processes is being frequently mentioned as an option to reduce greenhouse gas emissions from energy-intensive industries. Electricity is a versatile energy carrier which presents a variety of electrification options. The increasing availability of cheap renewable electricity provides an opportunity to decarbonize energy intensive processes. As part of this decarbonization effort, the commodity chemical industry is an important target due to its large energy requirements and greenhouse gas emissions. One potential paradigm for electrification involves replacing the use of steam, generated by burning fossil fuels, as a source of heat in chemical processes to processes with direct electrical heating using renewable energy sources. This project aims to identify and quantify areas where energy is currently transferred by steam can be efficiently transferred by renewable electrification. The target commodity chemicals are ammonia, ethylene, propylene, and methanol.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
More information: https://cistar.us/
Electrification of industrial processes is being frequently mentioned as an option to reduce greenhouse gas emissions from energy-intensive industries. Electricity is a versatile energy carrier which presents a variety of electrification options. The increasing availability of cheap renewable electricity provides an opportunity to decarbonize energy intensive processes. As part of this decarbonization effort, the commodity chemical industry is an important target due to its large energy requirements and greenhouse gas emissions. One potential paradigm for electrification involves replacing the use of steam, generated by burning fossil fuels, as a source of heat in chemical processes to processes with direct electrical heating using renewable energy sources. This project aims to identify and quantify areas where energy is currently transferred by steam can be efficiently transferred by renewable electrification. The target commodity chemicals are ammonia, ethylene, propylene, and methanol.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
More information: https://cistar.us/
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis, Energy and Environment
Preferred major(s):
- Chemical Engineering
School/Dept.:
Chemical Engineering
Professor:
Cornelius
Masuku
More information: https://cistar.us/ewd/undergrad_overview/research-experience-for-undergraduates-reu-program
CISTAR - High temperature catalysts for conversion of ethylene and propylene to gasoline and diesel fuel
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
CISTAR's vision is to convert natural gas liquids, for example, ethane and propane, to fuels and chemicals by two catalytic steps. The first requires dehydrogenation of alkanes to olefins, which are subsequently converted to final products. This project investigates a new class of catalyst for conversion of ethylene and propylene to higher molecular weight hydrocarbons suitable for blending into gasoline or diesel fuels. These reactions occur at high temperature and pressure in a fixed bed reactor. The research plan is to synthesize catalysts and test these to determine the rates, selectivity and stability.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
CISTAR's vision is to convert natural gas liquids, for example, ethane and propane, to fuels and chemicals by two catalytic steps. The first requires dehydrogenation of alkanes to olefins, which are subsequently converted to final products. This project investigates a new class of catalyst for conversion of ethylene and propylene to higher molecular weight hydrocarbons suitable for blending into gasoline or diesel fuels. These reactions occur at high temperature and pressure in a fixed bed reactor. The research plan is to synthesize catalysts and test these to determine the rates, selectivity and stability.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Research categories:
Chemical Catalysis and Synthesis
Desired experience:
None, but reaction engineering is desirable.
School/Dept.:
Chemical Engineering
Professor:
Jeff
Miller
More information: https://cistar.us/
CISTAR - Synthesis of Alloy Nanoparticles for Selective Catalysis
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
In this project, students will develop precise colloidal and impregnation-based syntheses for supported metal alloy nanoparticles. These materials will then be utilized as heterogeneous catalysts in thermal and solution-phase hydrogenation and dehydrogenation reactions. A particular focus will be placed on controlling the ensemble geometry and electronic properties of the alloy surface in order achieve highly selective catalysis.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
In this project, students will develop precise colloidal and impregnation-based syntheses for supported metal alloy nanoparticles. These materials will then be utilized as heterogeneous catalysts in thermal and solution-phase hydrogenation and dehydrogenation reactions. A particular focus will be placed on controlling the ensemble geometry and electronic properties of the alloy surface in order achieve highly selective catalysis.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Research categories:
Chemical Catalysis and Synthesis
Preferred major(s):
- Chemistry
- Chemical Engineering
Desired experience:
General chemistry, organic chemistry
School/Dept.:
Indiana
Professor:
Christina
Li
More information: https://cistar.us/
CISTAR - Synthesis of tailored carbon supports for non-oxidative methane conversion
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
Methane is the major component of shale gas, and more research is needed to develop non-oxidative conversion routes to higher olefins and aromatics. Carbon-based catalysts have been shown to be effective at non-oxidative methane conversion to these products, but the active sites and reaction mechanisms remain unclear. This project will work on developing synthesis methods to alter the surface areas and active sites in porous carbon materials for methane conversion, and studying their catalytic reactivity and selectivity.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Methane is the major component of shale gas, and more research is needed to develop non-oxidative conversion routes to higher olefins and aromatics. Carbon-based catalysts have been shown to be effective at non-oxidative methane conversion to these products, but the active sites and reaction mechanisms remain unclear. This project will work on developing synthesis methods to alter the surface areas and active sites in porous carbon materials for methane conversion, and studying their catalytic reactivity and selectivity.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Research categories:
Chemical Catalysis and Synthesis
Preferred major(s):
- Chemical Engineering
School/Dept.:
IN
Professor:
Rajamani
Gounder
More information: https://cistar.us/
CISTAR - Synthesis of zeolite catalysts with tailored diffusion properties
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
Olefin oligomerization is a key step in shale gas upgrading routes to heavier molecular weight products. Acidic zeolites are an important class of materials to catalyze oligomerization reactions, but reaction rates and selectivities are influenced by coupled reaction-transport phenomena. This project will focus on synthesizing zeolite crystallites with tailored diffusion properties (e.g., crystal size and morphology, acid site distributions) to influence the rates and selectivities of olefin oligomerization.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Olefin oligomerization is a key step in shale gas upgrading routes to heavier molecular weight products. Acidic zeolites are an important class of materials to catalyze oligomerization reactions, but reaction rates and selectivities are influenced by coupled reaction-transport phenomena. This project will focus on synthesizing zeolite crystallites with tailored diffusion properties (e.g., crystal size and morphology, acid site distributions) to influence the rates and selectivities of olefin oligomerization.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Research categories:
Chemical Catalysis and Synthesis
Preferred major(s):
- Chemical Engineering
School/Dept.:
IN
Professor:
Rajamani
Gounder
More information: https://cistar.us/
Cell division in confining microenvironments
Description:
Cell division is essential for life, underlying the development of mammals from embryo to full-grown adult, regenerative processes, such as wound healing, and diseases such as cancer. The intracellular aspects of mammalian cell division have been revealed through two-dimensional culture studies, where cells simply grow and then release from the substrate to divide in an unrestricted manner. However, physiologically, many cells divide in mechanically confining microenvironments, including dense extracellular matrices (ECMs) with distinct viscoelastic, viscoplastic, and nonlinear elastic characteristics, often surrounded by other cells, as in tumors. In this project, we will illuminate how cells modulate extracellular forces to facilitate and sustain cell division in confining microenvironments, using a computational model.
Research categories:
Biological Simulation and Technology, Cellular Biology
Preferred major(s):
- Biomedical Engineering
- Mechanical Engineering
Desired experience:
Intermediate/Proficient C coding skills
Sufficient experiences in MATLAB coding
Basic knowledge of cell biology (optional)
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Taeyoon
Kim
More information: https://engineering.purdue.edu/mct
Cellular basis of meristem development in Ceratopteris gametophytes
Description:
Students will perform microscopic studies to understand meristem cell division and growth in Ceratotperis gametophytes in response to phytohormones, environmental signals, and mechanical perturbations. This study will help reveal the cellular basis of stem cell proliferation and meristem development in plants.
Research categories:
Biological Characterization and Imaging, Cellular Biology
Preferred major(s):
- Plant Science
- Biology
- Cell Molecular and Developmental Biology
- Biochemistry
School/Dept.:
Botany and Plant Pathology
Professor:
Yun
Zhou
Characterizing Infant Exposure to Chemical Contaminants in Indoor Dust
Description:
Our project is funded by the U.S. Environmental Protection Agency (EPA) and involves an interdisciplinary collaboration between engineers, chemists, and psychologists at Purdue University and New York University (NYU). We will elucidate determinants of indoor dust ingestion in 6- to 24-month-old infants (age range for major postural and locomotor milestones). Specific objectives are to test: (1) whether the frequency and characteristics of indoor dust and non-dust mouthing events change with age and motor development stage for different micro-environments; (2) how home characteristics and demographic factors affect indoor dust mass loading and dust toxicant concentration; (3) how dust transfer between surfaces is influenced by dust properties, surface features, and contact dynamics; and (4) contributions of developmental, behavioral, and socio-environmental factors to dust and toxicant-resolved dust ingestion rates. In addition, the project will (5) create a shared corpus of video, dust, toxicant, and ingestion rate data to increase scientific transparency and speed progress through data reuse by the broader exposure science community.
Our transdisciplinary work will involve: (1) parent report questionnaires and detailed video coding of home observations of infant mouthing and hand-to-floor/object behaviors; (2) physical and chemical analyses of indoor dust collected through home visits and a citizen-science campaign; (3) surface-to-surface dust transfer experiments with a robotic platform; (4) dust mass balance modeling to determine distributions in and determinants of dust and toxicant-resolved dust ingestion rates; and (5) open sharing of curated research videos and processed data in the Databrary digital library and a public website with geographic and behavioral information for participating families.
The project will provide improved estimates of indoor dust ingestion rates in pre-sitting to independently walking infants and characterize inter-individual variability based on infant age, developmental stage, home environment, and parent behaviors. Dust transport experiments and modeling will provide new mechanistic insights into the factors that affect the migration of dust from the floor to mouthed objects to an infant’s mouth. The shared corpus will enable data reuse to inform future research on how dust ingestion contributes to infants’ total exposure to environmental toxicants.
U.S. EPA project overview: https://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract_id/11194
Our transdisciplinary work will involve: (1) parent report questionnaires and detailed video coding of home observations of infant mouthing and hand-to-floor/object behaviors; (2) physical and chemical analyses of indoor dust collected through home visits and a citizen-science campaign; (3) surface-to-surface dust transfer experiments with a robotic platform; (4) dust mass balance modeling to determine distributions in and determinants of dust and toxicant-resolved dust ingestion rates; and (5) open sharing of curated research videos and processed data in the Databrary digital library and a public website with geographic and behavioral information for participating families.
The project will provide improved estimates of indoor dust ingestion rates in pre-sitting to independently walking infants and characterize inter-individual variability based on infant age, developmental stage, home environment, and parent behaviors. Dust transport experiments and modeling will provide new mechanistic insights into the factors that affect the migration of dust from the floor to mouthed objects to an infant’s mouth. The shared corpus will enable data reuse to inform future research on how dust ingestion contributes to infants’ total exposure to environmental toxicants.
U.S. EPA project overview: https://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract_id/11194
Research categories:
Biological Characterization and Imaging, Ecology and Sustainability, Engineering the Built Environment, Environmental Characterization, Human Factors
Preferred major(s):
- No Major Restriction
Desired experience:
We are seeking students passionate about studying environmental contaminants and infant exposure to chemicals in the indoor environment. Preferred skills: experience with MATLAB, Python, or R. Coursework: environmental science and chemistry, microbiology, physics, thermodynamics, heat/mass transfer, fluid mechanics, developmental psychology.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Brandon
Boor
More information: www.brandonboor.com
Cognitive State Modeling of Human Machine Interactions in a Level II Driving Simulator
Description:
Effective human-automation interaction (HAI) holds great promise for improved safety, performance, and efficiency across a variety of domains, ranging from transportation to healthcare to manufacturing. These settings have in common a vision of shared autonomy between human and automation. However, realizing the promise of HAI rests on first addressing the fundamental challenge of enabling automation to be aware of, and responsive to, the human with whom it is interacting. Dr. Jain’s research group is thus trying to develop a mathematical control-oriented model for human cognitive states that include trust, self-confidence, mental workload and perceived risk that will allow level II autonomous vehicles to appropriately calibrate these states so that a human’s reliance behavior on the automation is consistent with the reliability of the automation. The goal of this SURF project will be to develop a level II self-driving car simulator using Unreal Engine 5 that will be used as an experimental testbed. The most important criteria for consideration for this position will be prior game development experience using Unreal Engine. Applicants will be presented with a detailed document outlining the functionality requirements of the simulator if their applications are under consideration for the position. Pending the student’s interest, there will be opportunities for the student to contribute the experiment ideation and data analysis.
Research categories:
Human Factors, Other
Preferred major(s):
Desired experience:
Exceptional programming skills, with experience in video game development (preferably) in Unreal Engine.
School/Dept.:
School of Mechanical Engineering
Professor:
Neera
Jain
More information: https://engineering.purdue.edu/JainResearchLab/
Computational modeling of electric coupling between neurons
Description:
This project will involve developing models for coupling computational E-field dosimetry tools to neuron solvers. As part of this project students will learn to develop Finite Element Method (FEM) and neuron modeling tools. The student will implement cable model solvers for predicting response of neurons to E-fields.
Research categories:
Biological Simulation and Technology, Medical Science and Technology
Preferred major(s):
- Electrical Engineering
- Biomedical Engineering
- Computer Engineering
Desired experience:
Knowledge of Electromagnetics, Matlab, and ODEs is desired.
School/Dept.:
Electrical and Computer Engineering
Professor:
Luis
Gomez
Conducting Polymers for Bioelectronic Applications
Description:
This project involves the synthesis, characterization, and device application of conducting plastics for bioelectronic applications. In conjunction with a graduate student mentor, the SURF student will focus on synthesizing new polymers and characterizing the molecular, thermal, structural, electronic, and magnetic properties of the materials. As many carbon-based polymers are electronically and magnetically inactive, this line of research examines previously unexamined materials for next-generation applications, including their inclusion as the active layer materials in biomedical sensors. Thus, the SURF student will fabricate and test electronic and magnetic devices along these lines.
Research categories:
Material Processing and Characterization, Nanotechnology
Preferred major(s):
- No Major Restriction
Desired experience:
Students who are earlier in their careers (i.e., just completing their first year or sophomore year of study) are preferred. An interest in polymer chemistry and electronic devices is desired.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Bryan
Boudouris
More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=71151
Control and Learning of Autonomous Blimps (C-LAB)
Description:
The student will build a small blimp from scratch with additional control board, then design control algorithms for the blimp to auonomously navigate from A to B, and also area exploration based on AI algorithms. The autonomous blimp will also be able to coordinate with human operators, i.e. integrate human's preference/correction
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
basic mathematics; dynamics and control; some previous research experience involving UAVs and human.
School/Dept.:
AAE
Professor:
Shaoshuai
Mou
More information: https://engineering.purdue.edu/AIMS
Data Free Model Extraction
Description:
*** Desired experience: Strong coding skills and motivation in research are required. Background in deep learning, security, and natural language processing is not required but a plus.
*** Possible industry involvement: Some of these projects are funded by Meta/Facebook research awards and J.P.Morgan AI research awards. *** We especially encourage applications from women, Aboriginal peoples, and other groups underrepresented in computing.
*** Project 1. Data-Free Model Extraction
Many deployed machine learning models such as ChatGPT and Codex are accessible via a pay-per-query system. It is profitable for an adversary to steal these models for either theft or reconnaissance. Recent model-extraction attacks on Machine Learning as a Service (MLaaS) systems have moved towards data-free approaches, showing the feasibility of stealing models trained with difficult-to-access data. However, these attacks are ineffective or limited due to the low accuracy of extracted models and the high number of queries to the models under attack. The high query cost makes such techniques infeasible for online MLaaS systems that charge per query.
In this project, we will design novel approaches to get higher accuracy and
query efficiency than prior data-free model extraction techniques.
Early work and background can be found here:
https://www.cs.purdue.edu/homes/lintan/publications/disguide-aaai23.pdf
*** Project 2. Language Models for Detecting and Fixing Software Bugs and Vulnerabilities
In this project, we will develop machine learning approaches including code language models to automatically learn bug and vulnerability patterns and fix patterns from historical data to detect and fix software bugs and security vulnerabilities. We will also study and compare general code language models and domain-specific language models.
Early work and background can be found here:
Impact of Code Language Models on Automated Program Repair. ICSE 2023. Forthcoming.
KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair. ICSE 2023. Forthcoming.
https://www.cs.purdue.edu/homes/lintan/publications/cure-icse21.pdf
https://www.cs.purdue.edu/homes/lintan/publications/deeplearn-tse18.pdf
*** Project 3. Inferring Specifications from Software Text for Finding Bugs and Vulnerabilities
A fundamental challenge of detecting or preventing software bugs and vulnerabilities is to know programmers’ intentions, formally called specifications. If we know the specification of a program (e.g., where a lock is needed, what input a deep learning model expects, etc.), a bug detection tool can check if the code matches the specification.
Building upon our expertise on being the first to extract specifications from code comments to automatically detect software bugs and bad comments, in this project, we will analyze various new sources of software textual information (such as API documents and StackOverflow Posts) to extract specifications for bug detection. For example, the API documents of deep learning libraries such as TensorFlow and PyTorch contain a lot of input constraint information about tensors. Language models may be explored.
Early work and background can be found here:
https://www.cs.purdue.edu/homes/lintan/projects.html
*** Project 4. Testing Deep Learning Systems
We will build cool and novel techniques to make deep learning code such as TensorFlow and PyTorch reliable and secure. We will build it on top of our award-winning paper (ACM SIGSOFT Distinguished Paper Award)!
Machine learning systems including deep learning (DL) systems demand reliability and security. DL systems consist of two key components: (1) models and algorithms that perform complex mathematical calculations, and (2) software that implements the algorithms and models. Here software includes DL infrastructure code (e.g., code that performs core neural network computations) and the application code (e.g., code that loads model weights). Thus, for the entire DL system to be reliable and secure, both the software implementation and models/algorithms must be reliable and secure. If software fails to faithfully implement a model (e.g., due to a bug in the software), the output from the software can be wrong even if the model is correct, and vice versa.
This project aims to use novel approaches including differential testing to detect and localize bugs in DL software (including code and data) to address the testing oracle challenge.
Early work and background can be found here: https://www.cs.purdue.edu/homes/lintan/publications/eagle-icse22.pdf https://www.cs.purdue.edu/homes/lintan/publications/fairness-neurips21.pdf https://www.cs.purdue.edu/homes/lintan/publications/variance-ase20.pdf https://www.cs.purdue.edu/homes/lintan/publications/cradle-icse19.pdf
*** Possible industry involvement: Some of these projects are funded by Meta/Facebook research awards and J.P.Morgan AI research awards. *** We especially encourage applications from women, Aboriginal peoples, and other groups underrepresented in computing.
*** Project 1. Data-Free Model Extraction
Many deployed machine learning models such as ChatGPT and Codex are accessible via a pay-per-query system. It is profitable for an adversary to steal these models for either theft or reconnaissance. Recent model-extraction attacks on Machine Learning as a Service (MLaaS) systems have moved towards data-free approaches, showing the feasibility of stealing models trained with difficult-to-access data. However, these attacks are ineffective or limited due to the low accuracy of extracted models and the high number of queries to the models under attack. The high query cost makes such techniques infeasible for online MLaaS systems that charge per query.
In this project, we will design novel approaches to get higher accuracy and
query efficiency than prior data-free model extraction techniques.
Early work and background can be found here:
https://www.cs.purdue.edu/homes/lintan/publications/disguide-aaai23.pdf
*** Project 2. Language Models for Detecting and Fixing Software Bugs and Vulnerabilities
In this project, we will develop machine learning approaches including code language models to automatically learn bug and vulnerability patterns and fix patterns from historical data to detect and fix software bugs and security vulnerabilities. We will also study and compare general code language models and domain-specific language models.
Early work and background can be found here:
Impact of Code Language Models on Automated Program Repair. ICSE 2023. Forthcoming.
KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair. ICSE 2023. Forthcoming.
https://www.cs.purdue.edu/homes/lintan/publications/cure-icse21.pdf
https://www.cs.purdue.edu/homes/lintan/publications/deeplearn-tse18.pdf
*** Project 3. Inferring Specifications from Software Text for Finding Bugs and Vulnerabilities
A fundamental challenge of detecting or preventing software bugs and vulnerabilities is to know programmers’ intentions, formally called specifications. If we know the specification of a program (e.g., where a lock is needed, what input a deep learning model expects, etc.), a bug detection tool can check if the code matches the specification.
Building upon our expertise on being the first to extract specifications from code comments to automatically detect software bugs and bad comments, in this project, we will analyze various new sources of software textual information (such as API documents and StackOverflow Posts) to extract specifications for bug detection. For example, the API documents of deep learning libraries such as TensorFlow and PyTorch contain a lot of input constraint information about tensors. Language models may be explored.
Early work and background can be found here:
https://www.cs.purdue.edu/homes/lintan/projects.html
*** Project 4. Testing Deep Learning Systems
We will build cool and novel techniques to make deep learning code such as TensorFlow and PyTorch reliable and secure. We will build it on top of our award-winning paper (ACM SIGSOFT Distinguished Paper Award)!
Machine learning systems including deep learning (DL) systems demand reliability and security. DL systems consist of two key components: (1) models and algorithms that perform complex mathematical calculations, and (2) software that implements the algorithms and models. Here software includes DL infrastructure code (e.g., code that performs core neural network computations) and the application code (e.g., code that loads model weights). Thus, for the entire DL system to be reliable and secure, both the software implementation and models/algorithms must be reliable and secure. If software fails to faithfully implement a model (e.g., due to a bug in the software), the output from the software can be wrong even if the model is correct, and vice versa.
This project aims to use novel approaches including differential testing to detect and localize bugs in DL software (including code and data) to address the testing oracle challenge.
Early work and background can be found here: https://www.cs.purdue.edu/homes/lintan/publications/eagle-icse22.pdf https://www.cs.purdue.edu/homes/lintan/publications/fairness-neurips21.pdf https://www.cs.purdue.edu/homes/lintan/publications/variance-ase20.pdf https://www.cs.purdue.edu/homes/lintan/publications/cradle-icse19.pdf
Research categories:
Big Data/Machine Learning, Cybersecurity, Deep Learning, Other
Preferred major(s):
- Computer Science
- Computer Engineering
- software engineering
School/Dept.:
https://www.cs.purdue.edu/homes/lintan/
Professor:
Lin
Tan
More information: https://www.cs.purdue.edu/homes/lintan/
Design of Multi-type Locomotive Robot for Planetary Exploration
Description:
The initiative of human exploration missions across the solar system, beginning with the return of humans to the Moon, followed by the journey to deliver astronauts to Mars, brings challenges and needs of advanced technologies to enable successful operations on a planetary surface. To assist human activities under extreme environments, it is essential to develop a versatile exploration system that can be self-adapted and deployed in a wide range of environments, especially for locations inaccessible to traditional rovers.
Existing exploration vehicles deployed on the Lunar or Martian surface are rover type vehicles that have limited access to operating surfaces, which excludes extreme environments such as cliffs, steep craters, highlands rocks, and caves. The rovers have to circumvent these areas due to limited capability and safety concerns. A revolutionary concept that breaks the capability limitations of existing robotic systems is desired to enable integrated functions of multi-type surface exploration. To achieve this goal, this project is to develop a multi-type locomotive robot that can adapt its structure and functionality according to operating surface types/features. The robot is expected to have the capability of rolling on wheels, jumping, and crawling.
Students enrolled in the project is expected to design the mechanism of multi-type locomotive robot to have the functionalities of rolling, jumping, and crawling. In addition, students are expected to manufacture and test the design using lab provided hardware and instruments.
Existing exploration vehicles deployed on the Lunar or Martian surface are rover type vehicles that have limited access to operating surfaces, which excludes extreme environments such as cliffs, steep craters, highlands rocks, and caves. The rovers have to circumvent these areas due to limited capability and safety concerns. A revolutionary concept that breaks the capability limitations of existing robotic systems is desired to enable integrated functions of multi-type surface exploration. To achieve this goal, this project is to develop a multi-type locomotive robot that can adapt its structure and functionality according to operating surface types/features. The robot is expected to have the capability of rolling on wheels, jumping, and crawling.
Students enrolled in the project is expected to design the mechanism of multi-type locomotive robot to have the functionalities of rolling, jumping, and crawling. In addition, students are expected to manufacture and test the design using lab provided hardware and instruments.
Research categories:
Fabrication and Robotics
Preferred major(s):
- Aeronautical and Astronautical Engineering
School/Dept.:
Aeronautics and Astronautics
Professor:
Ran
Dai
More information: https://engineering.purdue.edu/AOL
Development of protein biomarkers from biofluids for non-invasive early detection and monitoring of cancers
Description:
Currently, most cancer diagnosis procedures include a diagnostic imaging process, such as a CT scan followed by a tumor biopsy. Tissue biopsy is an invasive and painful procedure and may pose health risks for patients such as those with kidney diseases. Liquid biopsy, the ability to detect and monitor disease through biofluids, is highly promising and may replace tissue biopsy with an immense potential public health impact. The use of liquid biopsy offers numerous advantages in the clinical setting, including its non-invasive nature, a suitable sample source for longitudinal disease monitoring, a better screenshot of tumor heterogeneity, and lower costs compared to tissue biopsy. Increasing evidence indicates an important cellular function of exosomes and other extracellular vesicle (EV) particles in tumor biology and metastasis, presenting them as intriguing sources for biomarker discovery and disease diagnosis. However, the vast majority of current exosome/EV studies focus on their miRNAs, with few studies on functional proteins such as phosphorylated proteins. As phosphorylation is a major player in cancer and other disease progression, EV phosphoproteins are expected to become actively pursued targets for in vitro disease diagnosis. We were the first group to demonstrate that many phosphoproteins in exosomes and microvesicles could be extracted from a small volume of biofluids, identified by high-resolution mass spectrometry (MS), and verified as potential cancer markers (Chen et al PNAS 2017). In this project, we will focus on non-invasive cancer detection by coupling CT scans with liquid biopsy to eliminate the need for surgery by more than 50%. The IU Urology team led by kidney surgeon Dr. Boris and Dr. Tao’s lab at Purdue University collaborated with prior funding have established specific biosignatures found in low- and high-grade clear cell RCC. An undergraduate student may be involved in the protein sample preparation from biofluids and tissues, maintenance of equipment, and/or bioinformatics analysis.
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Deep Learning, Medical Science and Technology, Nanotechnology
Preferred major(s):
- Computer Science
- Biochemistry
- Biomedical Engineering
- Chemistry
- Biology
Desired experience:
Certain coding skills and biostatistics are highly desirable but not required.
School/Dept.:
Biochemistry AND Chemistry
Professor:
W. Andy
Tao
More information: http://www.protaomics.org/
Development of single cell pathway analysis benchmark
Description:
Single cell pathway analysis refers to the study of biological pathways and processes in individual cells, rather than in bulk tissue samples or cell populations. This approach allows for a deeper understanding of cellular heterogeneity and enables the identification of rare cell types and subpopulations.
Single cell pathway analysis typically involves the use of single cell omics technologies such as single cell transcriptomics (scRNA-seq), single cell proteomics, or single cell epigenetics. These techniques provide a high-throughput and comprehensive view of the molecular changes taking place within individual cells.
Applications of single cell pathway analysis include the study of development, disease, and cellular signaling. For example, it can be used to uncover the complex molecular changes that occur during cell differentiation and the progression of diseases such as cancer. It can also be used to study the effects of drugs and other treatments on individual cells.
there has been multiple methods developed to perform single cell analysis, however, how well these methods perform remains unclear. The aim of this project is developing a benchmark to evaluate various single cell pathway analysis methods.
Single cell pathway analysis typically involves the use of single cell omics technologies such as single cell transcriptomics (scRNA-seq), single cell proteomics, or single cell epigenetics. These techniques provide a high-throughput and comprehensive view of the molecular changes taking place within individual cells.
Applications of single cell pathway analysis include the study of development, disease, and cellular signaling. For example, it can be used to uncover the complex molecular changes that occur during cell differentiation and the progression of diseases such as cancer. It can also be used to study the effects of drugs and other treatments on individual cells.
there has been multiple methods developed to perform single cell analysis, however, how well these methods perform remains unclear. The aim of this project is developing a benchmark to evaluate various single cell pathway analysis methods.
Research categories:
Big Data/Machine Learning, Biological Simulation and Technology
Preferred major(s):
- No Major Restriction
Desired experience:
Computational genomics/bioinformatics.
Knowledge of pathway analysis tools and single cell technologies.
School/Dept.:
BCHM
Professor:
Majid
Kazemian
More information: https://kazemianlab.com
Disaster Response and Recovery for Water Systems
Description:
Undergraduate students supported by this project must be citizens or permanent residents of the United States or its possessions.This project will involve helping the Professor compile all the information associated with damage and recovery from wildfires in an ongoing project. The student will also help support a separate building plumbing testing and decontamination guidance document being created.
Activities will include reading, analyzing documents, writing a report and presenting results at the end of summer SURF symposium. This project may also have a laboratory component to examine the chemical contamination and decontamination of plumbing materials. The main driver of this undergraduate position is helping the Professor pull together and summarize the studies and efforts the team has completed over the past 3 years. Final reports and analyses are due by the end of summer for the externally funded projects.
Experience with technical writing is preferred.
Activities will include reading, analyzing documents, writing a report and presenting results at the end of summer SURF symposium. This project may also have a laboratory component to examine the chemical contamination and decontamination of plumbing materials. The main driver of this undergraduate position is helping the Professor pull together and summarize the studies and efforts the team has completed over the past 3 years. Final reports and analyses are due by the end of summer for the externally funded projects.
Experience with technical writing is preferred.
Research categories:
Other
Preferred major(s):
- No Major Restriction
School/Dept.:
EEE
Professor:
Andrew
Whelton
Drinking Water Microbiology
Description:
Although engineers add disinfectant residual to drinking water to prevent microbial growth, as water travels many miles through distribution pipes this disinfectant is lost. Microbial growth is often unavoidable - including the growth of opportunistic pathogens that can cause disease in immunocompromised populations. The three opportunistic pathogens (OPs) recognized by the scientific community to be of major concern are Legionella pneumophila, Mycobacterium avium, and Pseudomonas aeruginosa. These bacteria often grow in biofilm, a microbiological layer formed along the inner surface of pipes.
This project will investigate the microbial diversity of drinking water bacteria through a variety of molecular biology methods. Opportunistic pathogens will be quantified through qPCR methods within samples from rural drinking water and controlled experiments on Purdue's campus. Additionally, students will help with more advanced molecular methods including sequencing and bioinformatics. Results from this project will provide insight into the dynamics of pathogens within drinking water.
This project will investigate the microbial diversity of drinking water bacteria through a variety of molecular biology methods. Opportunistic pathogens will be quantified through qPCR methods within samples from rural drinking water and controlled experiments on Purdue's campus. Additionally, students will help with more advanced molecular methods including sequencing and bioinformatics. Results from this project will provide insight into the dynamics of pathogens within drinking water.
Research categories:
Biological Characterization and Imaging, Cellular Biology, Engineering the Built Environment, Environmental Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
While no background is required, a student with biology and/or biology lab experience and background is preferred.
School/Dept.:
Environmental and Ecological Engineering
Professor:
Caitlin
Proctor
EMBRIO Institute - (Core Imaging) Development and application of Atomic Force Microscopy (AFM) and imaging tools towards the measurement of oocyte mechanical behavior.
Description:
This project is the development and application of AFM and imaging tools towards the measurement of oocyte mechanical behavior. The student will learn fundamental biology of oocyte fertilization and applications of AFM towards measurement of cell biomechanics. Towards the scientific objectives of determining how calcium signaling and cytoskeletal contraction interact to generate the whole-cell membrane block response, the development of AFM as a precise indentation and measurement tool for assaying oocyte stiffness is necessary. Working with research personnel from the research groups and Thrusts led by Chan (Biomedical Engineering) and Evans (Biology), the undergraduate researcher will prepare cells for experiments, aid in AFM experiments, and analyze AFM data to determine oocyte response to indentation.
Expected learning outcomes the student will gain include: (1) understand fundamental biology of oocyte membrane block, (2) understand engineering mechanics concepts of atomic force microscopy, and (3) apply AFM force spectroscopy to oocyte biomechanics.
Expected learning outcomes the student will gain include: (1) understand fundamental biology of oocyte membrane block, (2) understand engineering mechanics concepts of atomic force microscopy, and (3) apply AFM force spectroscopy to oocyte biomechanics.
Research categories:
Biological Characterization and Imaging
Preferred major(s):
- No Major Restriction
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Deva
Chan
More information: https://www.purdue.edu/research/embrio/research/index.php
EMBRIO Institute - Establish Calcium Reporter Zebrafish Lines
Description:
EMBRIO Institute has an opening in the Zhang lab in the Comparative Pathobiology department for a SURF student to work on establishing calcium reporter zebrafish genetic lines. The student will learn more about the zebrafish model, and prepare for a future career in research. This project is part of the larger effort to determine how tissue-wide organization of connected epithelial sheets emerge.
Research categories:
Cellular Biology, Genetics
Preferred major(s):
- No Major Restriction
Desired experience:
student must be determined.
School/Dept.:
Comparative Pathobiology
Professor:
GuangJun
Zhang
More information: https://vet.purdue.edu/discovery/zhang/?_ga=2.37713315.269020311.1676298437-162191664.1675354198
EMBRIO Institute - High resolution imaging (project 1) and computational modeling (project 2) to test decoding of Ca2+-flux frequency by CaM and CaMKII role in dynamic actin polymerization and dendritic spine morphology.
Description:
Project 1: This summer research project will use high resolution imaging test the hypothesis that decoding of Ca2+-flux frequency by CaM and CaMKII plays a major role in dynamic actin polymerization and dendritic spine morphology. Student will learn basic laboratory skills, primary cell culture, immunohistochemistry, confocal imaging and image analysis.
Project 2: This summer research project will use computational modeling of Ca2+/Calmodulin and CaMKII interactions in dendritic spines to test the hypothesis that decoding of Ca2+-flux frequency by CaM and CaMKII plays a major role in dynamic actin polymerization and dendritic spine morphology. Computational tools that will be used include ordinary and partial differential equations and machine learning techniques to rapid explore model parameter space.
Research Question Overview:
Neuronal synapses are tightly regulated intercellular junctions that rapidly convey information from an upstream pre-synaptic neuron to a downstream post-synaptic neuron. Dynamic strengthening or weakening of synaptic connective strength, known as synaptic plasticity, is a critical feature of neuronal function. The direction of synaptic plasticity (increased connective strength (LTP) versus decreased connective strength (LTD)) depends on the timing of action potentials (AP), which is translated into frequency signals of Ca2+ ion flux through NMDA
receptors (NMDAR) located on dendritic spines (100-500nm mushroom-like protrusions that form the post-synapse).
The timing and direction of synaptic plasticity is also exquisitely regulated by dynamic organization and spatial localization of synaptic adhesion molecules, signaling receptors, ion channels, and the intracellular cytoskeleton within spines. However, it not clear to how these electrical, biochemical, and mechanical cues are integrated to produce robust, repeatable, and highly dynamic synaptic plasticity that lasts over the lifetime of a neuron (decades). Our recent work has shown that competition for CaM-binding can influence the Ca2+ frequency-dependence of protein activation and downstream signaling. In particular, the highly expressed Ca2+/calmodulin-dependent kinase II (CaMKII) plays a key role in synaptic plasticity via two
important aspects of its function: (1) CaMKII is highly involved in Ca2+-dependent signal transduction via phosphorylation of a number of downstream proteins including ion channels, guanine nucleotide exchange factors (GEFs), GTPase activating proteins (GAPs), and transcription factors, and (2) CaMKII acts as a multivalent scaffold that binds multiple proteins simultaneously and localizes them to post-synaptic spines, including both filamentous and monomeric actin and may regulate actin polymerization in the spine.
Project 2: This summer research project will use computational modeling of Ca2+/Calmodulin and CaMKII interactions in dendritic spines to test the hypothesis that decoding of Ca2+-flux frequency by CaM and CaMKII plays a major role in dynamic actin polymerization and dendritic spine morphology. Computational tools that will be used include ordinary and partial differential equations and machine learning techniques to rapid explore model parameter space.
Research Question Overview:
Neuronal synapses are tightly regulated intercellular junctions that rapidly convey information from an upstream pre-synaptic neuron to a downstream post-synaptic neuron. Dynamic strengthening or weakening of synaptic connective strength, known as synaptic plasticity, is a critical feature of neuronal function. The direction of synaptic plasticity (increased connective strength (LTP) versus decreased connective strength (LTD)) depends on the timing of action potentials (AP), which is translated into frequency signals of Ca2+ ion flux through NMDA
receptors (NMDAR) located on dendritic spines (100-500nm mushroom-like protrusions that form the post-synapse).
The timing and direction of synaptic plasticity is also exquisitely regulated by dynamic organization and spatial localization of synaptic adhesion molecules, signaling receptors, ion channels, and the intracellular cytoskeleton within spines. However, it not clear to how these electrical, biochemical, and mechanical cues are integrated to produce robust, repeatable, and highly dynamic synaptic plasticity that lasts over the lifetime of a neuron (decades). Our recent work has shown that competition for CaM-binding can influence the Ca2+ frequency-dependence of protein activation and downstream signaling. In particular, the highly expressed Ca2+/calmodulin-dependent kinase II (CaMKII) plays a key role in synaptic plasticity via two
important aspects of its function: (1) CaMKII is highly involved in Ca2+-dependent signal transduction via phosphorylation of a number of downstream proteins including ion channels, guanine nucleotide exchange factors (GEFs), GTPase activating proteins (GAPs), and transcription factors, and (2) CaMKII acts as a multivalent scaffold that binds multiple proteins simultaneously and localizes them to post-synaptic spines, including both filamentous and monomeric actin and may regulate actin polymerization in the spine.
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Biotechnology Data Insights, Cellular Biology
Preferred major(s):
- No Major Restriction
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Tamara
Kinzer-Ursem
More information: https://www.purdue.edu/research/embrio/research/index.php
EMBRIO Institute - How Reactive Oxygen Species (ROS) and actin cross talk during zebrafish wound healing.
Description:
As part of the EMBRIO Institute, our lab is working on multicellular to organism-wide coordination and emergence research to determine how tissue-wide organization of connected epithelial sheets emerge. Specifically this project will focus on learning how ROS and actin cross talk during zebrafish wound healing.
The student will learn how to handle fish, conduct high-resolution imaging, and engage in basic data analysis, with opportunities to move into modeling of the data.
The student will learn how to handle fish, conduct high-resolution imaging, and engage in basic data analysis, with opportunities to move into modeling of the data.
Research categories:
Biological Characterization and Imaging
Preferred major(s):
- No Major Restriction
Desired experience:
Prefer student from an area of biology that has high degree of interest in learning research techniques.
School/Dept.:
Biology
Professor:
Qing
Deng
More information: https://www.purdue.edu/research/embrio/research/index.php
EMBRIO Institute - Mechanistic models of Calcium signaling and its downstream effects
Description:
The student will work on existing computational models (agent-based models or partial differential equation models), making updates toward adapting existing models to new biological systems. Student will be co-mentored by Elsje Pienaar, BME Dept.
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology
Preferred major(s):
- No Major Restriction
School/Dept.:
Mechanical Engineering
Professor:
Adrian
Buganza Tepole
Earth Dam Damage during Earthquake
Description:
The safety of embankments and earth dams relies on an adequate assessment of seismically-induced deformations. When subjected to earthquake loading, embankments and earth dams may settle, deform laterally and longitudinally, and may exhibit cracking in the longitudinal and/or transverse directions. Cracks are considered one of the most hazardous consequences of strong earthquake shaking on earthen dams, as they can lead to piping failure due to increased seepage and internal erosion through the cracks. Observed cracks in earth dams after an earthquake are most often associated with tensile stresses and strains resulting from earthquake-induced permanent deformations. Current methods to estimate earthquake-induced cracking seem inadequate, given the widespread damage observed in thousands of dams during the 2008 Wenchuan earthquake. The research is geared at advancing our understanding of the cracking processes in embankments and earth dams during an earthquake and at providing improved tools for practitioners. An important outcome of the work is an updated database of case studies, as well as guidelines and protocols for collection of future information.
The work will combine existing information in the form of case studies and empirical recommendations, and outcomes from numerical simulations. It will expand and modernize databases, update and improve empirical methods, and perform detailed dynamic three-dimensional numerical analyses of actual dams, namely Lenihan Dam in California and Gatun Dam in the Panama Canal.
The student is expected to participate in all aspects of the project, with focus on the analysis of current data and providing recommendations to estimate intensity of cracking.
The work will combine existing information in the form of case studies and empirical recommendations, and outcomes from numerical simulations. It will expand and modernize databases, update and improve empirical methods, and perform detailed dynamic three-dimensional numerical analyses of actual dams, namely Lenihan Dam in California and Gatun Dam in the Panama Canal.
The student is expected to participate in all aspects of the project, with focus on the analysis of current data and providing recommendations to estimate intensity of cracking.
Research categories:
Engineering the Built Environment, Other
Preferred major(s):
- Civil Engineering
Desired experience:
one course in soil mechanics preferred
School/Dept.:
Lyles School of Civil Engineering
Professor:
Antonio
Bobet
Energy Efficient Dryer Design and Analysis for Advanced Manufacturing
Description:
In the coming years, countries around the world will make concerted efforts to decarbonize various industries and technologies to help prevent and reverse climate change. Currently, thermal dehydration accounts for 10-20% of all industrial energy consumption and relies heavily on the combustion of fossil fuels. Vapor compression heat pumps, like those used in building air conditioners, offer a high-efficiency, electrically driven heat source for industrial drying applications, however there are many barriers preventing broad implementation. Our team at Purdue has proposed a new thermal drying system concept that employs unique materials and exploits clever thermodynamic design to provide up to 40% energy and emissions savings. As part of this work, we are developing system models/simulations, designing and building prototype systems, and performing advanced materials research, thus providing a breadth of exciting opportunities for aspiring scientists and engineers. This research is also heavily tied to our work on energy efficient thermal systems for buildings and water/energy sustainability, and the student who joins the project will be exposed to many research topics within the Water-Energy Nexus.
Research categories:
Composite Materials and Alloys, Energy and Environment, Engineering the Built Environment, Fluid Modelling and Simulation, Material Modeling and Simulation, Material Processing and Characterization, Microelectronics, Nanotechnology, Thermal Technology
Preferred major(s):
- No Major Restriction
Desired experience:
Applicants should have a general interest in energy and sustainability. Should also have a strong background/interest in thermodynamics, heat transfer, and/or materials science. Applicants with experience in some (not all) of the following are preferred: LabVIEW, Python (Jupyter, Google Colab, etc.) Engineering Equation Solver, MATLAB, 3D-CAD Software, prototype design/manufacturing, and Adobe Illustrator. 2nd semester Sophomores, Juniors, and 1st semester Seniors are preferred.
School/Dept.:
Mechanical Engineering
Professor:
Jim
Braun
More information: www.warsinger.com
Energy storage and propulsion solutions for drones and small UAVs
Description:
Drones and small unmanned aerial vehicles (UAVs) less than 25 kg, have a wide range of applications, such as surveying and mapping, search and rescue, monitoring and inspecting infrastructures, monitoring crops, delivery goods and services, surveillance, and military operations. In fact, advanced aerial mobility is forecast to be a $1.5 trillion market by the year 2040. However, the short flight endurance (operation time, range, and/or payload) of battery-powered drones and small UAVs has been a long-standing bottleneck that restricts their application in many fields. Currently, a small consumer drone can fly for about 10 minutes on a full battery charge and a professional drone with a larger battery capacity can fly for up to 30 minutes. While this duration may be sufficient for hobbyists, it is not enough for extended surveillance and monitoring missions. Therefore, there is an urgent need for drones with longer flight durations. The goal of this project is to explore novel concept of energy storage and propulsion solutions for drones and UAVs to extend its flight duration. The SURF student will work with closely with the faculty advisor and a PhD student on both experimental and theoretical studies on hybrid propulsion systems.
Research categories:
Energy and Environment
Preferred major(s):
- aerospace engineering
- mechanical engineering
- electrical engineering
Desired experience:
We are looking for students who possess design and hands-on experience, preferably with prior research in the area of propulsion. Previous research in this field is highly desirable.
School/Dept.:
School of Aeronautics & Astronautics
Professor:
Li
Qiao
Engineer a synthetic neuron using a bottom-up approach
Description:
Neurons convert biochemical information (through binding of a neurotransmitter) to electrical signal (via action potential) and back to biochemical signal (through the release of neurotransmitters). These distinct and separable processes can be reconstituted in a synthetic neuron by using natural and engineered proteins, and a synthetic neuron platform can be used to understand the rules governing the emergence of the present morphology of a neuron and the architecture of the neuronal system. This project thus aims to construct a synthetic neuron with a modular design and a programmable synthetic neuronal network capable of recapitulating basic functions of a natural neuronal system (e.g., action potential, synaptic communication, and basic computation) and with a long-term vision of incorporating more advanced computation and potentiation.
Research categories:
Biological Characterization and Imaging, Cellular Biology, Genetics, Medical Science and Technology
Preferred major(s):
- No Major Restriction
Desired experience:
GPA > 3.5, BME, ABE and CHE preferred
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Chongli
Yuan
More information: https://cyuangroup.com/
Evaluation of Motor Learning in Response to a Wearable Passive Feedback System
Description:
This project involves the analysis of motion capture data and the development of human motor learning metrics to evaluate a wearable system developed by a company sponsor. Specifically, the undergraduate role is to assist in data collection and analysis. The student must take ethics courses and training to be approved for limited participation in human research and be comfortable interacting with healthy human subjects.
Project is co-advised by Dr Laura Blumenschein and Deva Chan
Project is co-advised by Dr Laura Blumenschein and Deva Chan
Research categories:
Biological Characterization and Imaging, Human Factors, Learning and Evaluation, Medical Science and Technology, Other
Preferred major(s):
- Biomedical Engineering
- Mechanical Engineering
- Kinesiology
- Health Science PreProfessional
- Health and Disease
- Occupational Health Science
- Rehabilitation Engineering
- Pre-physical Therapy
- Applied Exercise and Health (Pre)
Desired experience:
Human subjects, physiology, data analysis, statistics, motor learning
School/Dept.:
Mechanical Engineering
Professor:
Laura
Blumenschein
More information: https://lhblumen.wixsite.com/website-1
Evaluation of Transportation Challenges for Persons with Disabilities
Description:
Accessible, on-demand transportation is unavailable to many persons with travel-limiting disabilities. Professors Duerstock and Brandon Pitts have led a team to look at inclusive design for autonomous transportation. They were recently awarded $1 million 1st prize for the DOT Inclusive Design Challenge to design autonomous vehicles (AV) for passengers with disabilities including those with motor and perceptual impairments. This internship will focus on the analysis of data collected through surveys and participant testing from this competition as well as future investigations of this problem from the standpoint of AV design and transportation infrastructure.
Research categories:
Engineering the Built Environment, Human Factors
Preferred major(s):
- No Major Restriction
Desired experience:
Understanding scientific methods of statistical analysis and data collection from both qualitative and quantitative data sets is a must. Some programming experience is preferred.
School/Dept.:
School of Industrial Engineering
Professor:
Brad
Duerstock
More information: https://engineering.purdue.edu/DuerstockIAS/research/EASIRIDER
Evaluation of cartilage mechanics after ACL rupture in a mouse model of osteoarthritis
Description:
Osteoarthritis affects over 32.5 million American adults, impacting mobility and quality of life, and costs over $16.5 billion in direct medical costs in hospitals within the United States. Osteoarthritis is hallmarked by degradation of the various tissues of the joint, formation of bony structures called osteophytes, and joint pain or stiffness – often in that order of progression. Knee osteoarthritis is most common among these, and approximately 1 in 8 cases of osteoarthritis are considered post-traumatic, meaning that degeneration of the tissues in the joint is precipitated from an injury, such as tearing of the anterior cruciate ligament (ACL). To model this disease process, our lab uses mechanical loading to rupture the ACL. We also use atomic force microscopy to characterize changes to the surface profile and biomechanical behavior of tissues. The student working on this project will use AFM to characterize the changes that occur in mouse articular cartilage after ACL injury.
Research categories:
Medical Science and Technology, Other
Preferred major(s):
- Biomedical Engineering
Desired experience:
Biomechanics, imaging and image processing, data analysis, technical writing and communication
School/Dept.:
Biomedical Engineering
Professor:
Deva
Chan
More information: https://engineering.purdue.edu/ChanLab
Exploring Views of Engineering Ethics
Description:
The project will involve reviewing qualitative data to identify how individuals view or experience ethics in engineering. The SURF student will thus learn to conduct educational research with a concerted focus on applying qualitative research methods. Subjects of this research study include faculty members in biomedical, electrical, or computer engineering. The outcome of this research will include better understandings of views and perceptions on ethics in engineering. The ultimate objectives of this work will be to translate these findings to improve engineering ethics instruction.
Research categories:
Learning and Evaluation, Other
Preferred major(s):
- No Major Restriction
Desired experience:
Prior course work in engineering ethics;
Interest in learning more about education research methods;
Good writing and communication skills
School/Dept.:
Engineering Education
Professor:
Justin
Hess
Fabrication and simulation of the efficient joining of dissimilar materials
Description:
The student will start with the metallographic preparation training and make many samples for the experiments. The student will then fabricate the samples using the patented equipment in the lab. The student will work with graduate students on material characterization. The student will also develop the finite element model to simulate the thermal and stress fields. Except the experiments and simulations, the student is expected to read literatures, make a presentation in the weekly meeting, write progress reports.
Research categories:
Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization, Thermal Technology, Other
Preferred major(s):
- No Major Restriction
School/Dept.:
Nuclear Engineering
Professor:
Yi
Xie
More information: https://engineering.purdue.edu/MINE
Fabrication and testing of advanced materials in harsh environments
Description:
The environmental degradation of structrual and functional materials is a key problem for the sustainability and longevity of advanced energy systems. The project is to investigate the properties and behaviors of innovative materials in the application environments. The student will start with the powder processing and metallographic preparation training and fabricate many samples for the corrosion experiments and thermal measurements. The student will work with graduate students on material characterization and data analysis. The student is expected to read literatures, make presentations in the weekly meeting, and write progress reports.
Research categories:
Composite Materials and Alloys, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
School/Dept.:
Nuclear Engineering
Professor:
Yi
Xie
More information: https://engineering.purdue.edu/MINE
Finding cybersecurity vulnerabilities in IoT/embedded systems
Description:
Embedded systems provide control and operational intelligence for high-value cyber-physical systems such as smart cars, smart tractors, and smart city components. These systems must be secured from adversarial interactions. Vulnerabilities in embedded systems primarily occur in the external-facing components, especially in networking protocol stacks. One vulnerability detection technique that is widely used for IT software, such as web services, is called dynamic analysis (“fuzz testing”). We believe fuzzing will also find vulnerabilities in embedded systems. However, there are many challenges in adapting fuzzers to embedded systems software.
This project will develop new techniques to enable dynamic security analysis of embedded systems. The student will express research ideas in computer software, especially C/C++/Python code. The student will conduct experiments to identify and analyze discovered security vulnerabilities.
This project will develop new techniques to enable dynamic security analysis of embedded systems. The student will express research ideas in computer software, especially C/C++/Python code. The student will conduct experiments to identify and analyze discovered security vulnerabilities.
Research categories:
Cybersecurity, Internet of Things (IoT)
Preferred major(s):
- No Major Restriction
Desired experience:
Strong C/C++ programming skills, Python, familiarity with Linux programming environment (e.g. you are comfortable on the terminal), some knowledge of cybersecurity exploits (e.g. buffer overflows). Knowledge of embedded systems context is a plus.
Successful applicants are likely EE, CompEng, or CS majors.
School/Dept.:
Electrical & Computer Engineering
Professor:
James
Davis
Generating efficient data layouts for distributed programs
Description:
This project is part of a larger effort to generate efficient data structures and data layouts to make applications more efficient. Students who join this project will improve their functional programming skills, as well as their knowledge of compilers.
For this project, the student will be working with the gibbon compiler
infrastructure. She will work on writing some benchmarks in a subset
language of Haskell. These benchmarks would be like microservices
benchmarks, aligned with the future focus of developing a
compiler(gibbon) for microservices.
Benchmarking will involve writing significant code in the gibbon front
end language. It will involve making sure that the program output is
correct and matches a similar compiler such as GHC.
We would also like to get timings of these benchmarks and make sure
that the results are as expected. Knowledge of concepts such as
speedups, Amdahl’s law etc. are useful but can be learned as time
progresses.
Optionally, if the student wishes to debug compiler passes, there are
some open issues in the gibbon compiler that can be worked upon.
This would get the student familiarized with the gibbon pipeline and
how to write compiler passes in gibbon. It would also enhance
programming skills in a functional programming language like
Haskell.
For this project, the student will be working with the gibbon compiler
infrastructure. She will work on writing some benchmarks in a subset
language of Haskell. These benchmarks would be like microservices
benchmarks, aligned with the future focus of developing a
compiler(gibbon) for microservices.
Benchmarking will involve writing significant code in the gibbon front
end language. It will involve making sure that the program output is
correct and matches a similar compiler such as GHC.
We would also like to get timings of these benchmarks and make sure
that the results are as expected. Knowledge of concepts such as
speedups, Amdahl’s law etc. are useful but can be learned as time
progresses.
Optionally, if the student wishes to debug compiler passes, there are
some open issues in the gibbon compiler that can be worked upon.
This would get the student familiarized with the gibbon pipeline and
how to write compiler passes in gibbon. It would also enhance
programming skills in a functional programming language like
Haskell.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
Programming skills in a functional programming language,
preferably Haskell.
• Knowledge of a compiler.
• Understanding of the pipeline of compiling a source program to a
binary.
• Version control, such as, git.
School/Dept.:
Electrical and Computer Engineering
Professor:
Milind
Kulkarni
Harnessing Deep Learning for Suppressing Vibrations in Microscopic Imaging
Description:
We are developing deep learning-based workflows for particle diffusometry. Our algorithms are robust against arbitrary flows and thermal gradients. Even though the algorithms have the potential to make in-lab and in-field testing robust, these have never been tested in the lab or the field. The summer student can do various tasks:
1. Microscopic data acquisition
2. Training neural networks
1. Microscopic data acquisition
2. Training neural networks
Research categories:
Deep Learning
Preferred major(s):
- No Major Restriction
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Jacqueline
Linnes
Heat Treatment for Normalization of EB Welds in Low-Alloy Steel
Description:
Electron Beam Welding is a promising new technology currently being investigated for use in rapid manufacturing for nuclear power applications. This project seeks to investigate new heat treatment procedures in order to remove weld-induced microstructural effects in SA508 steel, with a specific focus on attaining complete austenitization within the weld region. The experiments will be guided by a finite element model, and will be used to validate the accuracy of existing models for the thermal behavior of SA508 steel manufactured both by traditional forging and by powder metallurgy. Students will be involved with the fabrication of samples and carrying out heat treatments, as well as investigation of the resulting microstructures via optical microscopy, scanning electron microscopy (SEM), and nanoindentation.
Research categories:
Material Processing and Characterization
Preferred major(s):
- Materials Engineering
- Mechanical Engineering
- Nuclear Engineering
Desired experience:
MSE 230 or equivalent; eager to learn; able to work in a collaborative group setting
School/Dept.:
Materials Engineering
Professor:
Janelle
Wharry
More information: https://wharryresearchgroup.wordpress.com/
Hetergeneous Metamaterials
Description:
The project is to manufacture programmable heterogeneous digital materials. These materials consist of an engineered heterogeneity in polymer matrix to yield various effective stress strain reactions that are nonlinear and programmable. The student will primarily engage in reviewing the relevant literature in the field and support in performing experiments.
Research categories:
Material Modeling and Simulation
Preferred major(s):
- No Major Restriction
Desired experience:
Matlab, and lab experience, 3D printing
School/Dept.:
Mechanical Engineering
Professor:
James
Gibert
Heterogeneous Integration/ Advanced Packaging: 3D Cryogenic Packaging for Superconducting Computing
Description:
In 2017, a large-scale, 3D integrated quantum processor was demonstrated by MIT Lincoln Laboratory using heterogeneous 3D integration to create an architecture that enables the use of the third dimension without sacrificing qubit performance [D. Rosenberg, et al., Nature 2017]. In these quantum applications, conventional Sn-based solder bumps are not reliable while Indium and bismuth-based solders are promising for 3D integration at low temperatures. In this topic, new cryogenic compatible packaging materials and cryogenic superconducting multi-chip bonding techniques are needed to further explore and investigate the microelectronics devices and packages at low/cryogenic temperatures.
Reference: Rosenberg, D., et al. "3D integrated superconducting qubits." npj quantum information 3.1 (2017): 1-5.)
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Reference: Rosenberg, D., et al. "3D integrated superconducting qubits." npj quantum information 3.1 (2017): 1-5.)
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Research categories:
Advanced Packaging, Microelectronics
Preferred major(s):
- No Major Restriction
School/Dept.:
ME
Professor:
Tiwei
Wei
More information: https://alphalab-purdue.org/
Heterogeneous Integration/ Advanced Packaging: Glass Interposer Development for 3D Heterogenous Integration
Description:
Interposer is one of the most potential solutions for future 3D integration with ultrafine pitch. Silicon interposer has been developed in both industry and academia. However, silicon interposer has limitations, such as low productivity due to limited wafer size, extra expensive semiconductor fabrication processes, and poor electrical properties like insert loss and signal crosstalk. On the contrary, glass can be one kind of promising material as an interposer because of its excellent properties, such as good electrical resistivity, relatively low CTE compared to organic material, and possible high productivity with big panel sizes provided by glass suppliers.
Recent research studies have mainly focused on three challenges in glass interposer technology: (1) formation of the fine pitch via, which is more difficult than through silicon via (TSV) due to the unfavorable etching process ; (2) via metallization and via filling process, which become much more complicated because of the rough morphology of TGV surface, and difficulty to fill the tapered via through Damascus electroplating; (3) reliability concern, which is caused by brittleness and poor mechanical strength of glass.
Through glass via fabrications
Reference: Wei, T. W., Cai J.*, et al. Performance and reliability study of TGV interposer in 3D integration[C]//2014 IEEE 16th Electronics Packaging Technology Conference (EPTC). IEEE, 2014: pp. 601-605.
Recent research studies have mainly focused on three challenges in glass interposer technology: (1) formation of the fine pitch via, which is more difficult than through silicon via (TSV) due to the unfavorable etching process ; (2) via metallization and via filling process, which become much more complicated because of the rough morphology of TGV surface, and difficulty to fill the tapered via through Damascus electroplating; (3) reliability concern, which is caused by brittleness and poor mechanical strength of glass.
Through glass via fabrications
Reference: Wei, T. W., Cai J.*, et al. Performance and reliability study of TGV interposer in 3D integration[C]//2014 IEEE 16th Electronics Packaging Technology Conference (EPTC). IEEE, 2014: pp. 601-605.
Research categories:
Advanced Packaging, Microelectronics
Preferred major(s):
- No Major Restriction
School/Dept.:
ME
Professor:
Tiwei
Wei
More information: https://alphalab-purdue.org/
High Performance Concrete from Hydrogel-Based Superabsorbent Materials
Description:
Concrete that is internally cured by water-swollen superabsorbent polymer (SAP) particles has improved strength and durability. This project will investigate new SAP formulations that have increased absorption capacity in a wider variety of low-carbon concrete materials. The student will conduct swelling tests and optical microscopy of the SAP particles and then perform optical microscopy and mechanical measurements of the SAP-cured concrete. Improvements in concrete strength and durability is a step towards reducing the carbon footprint of our civil infrastructure materials, as production of new cement results in 7-9% of the global CO2 emissions each year.
Research categories:
Composite Materials and Alloys, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Enthusiasm for chemistry and an interest in materials research. Prior experiences with cement and concrete would be a benefit to the project but are not required.
School/Dept.:
Materials Engineering
Professor:
Kendra
Erk
More information: https://soft-material-mechanics.squarespace.com/home/
High Performance Perovskite Solar Cells
Description:
Sunlight is the most abundant renewable energy resource available to human beings, and yet it remains one of the most poorly utilized sources of clean energy. Solar cell modules incorporating single crystalline silicon and gallium arsenide currently provide the highest efficiencies for solar energy conversion to electricity but remain limited due to their high costs.
In the past few years, perovskite solar cell technology has made significant progress, improving in efficiency to ~25%, while maintaining attractive economics due to the use of inexpensive soluble materials coupled with ultra low-cost deposition technologies. However, the real applications of these devices requires new breakthroughs in device performance, large-scale manufacturing, and improved stability. Among these, stability and degradation are among the most significant challenges for perovskite technologies. Perovskite absorber layer and organic charge transport materials can be sensitive to water, oxygen, high temperatures, ultraviolet light, and even electric field, all of which will be encountered during operation. To address these issues, significant efforts have been made, including mixed dimensionality and surface passivation; alternative absorber materials and formulations, new charge transport layers, and advanced encapsulation techniques, etc. Now, T80 lifetimes (i.e., the length of time in operation until measured output power is 80% of original output power) of over 1,000 hours have been demonstrated. However, it is still far below the industry required 20 years lifetime, indicating the ineffectiveness of current approaches. To make this advance, non-incremental and fundamentally new strategies are required to improve the intrinsic stability of perovskite active materials.
In this project, we propose a new paradigm to develop intrinsically robust perovskite active layers through the incorporation of multi-functional semiconducting conjugated ligands. In preliminary work, we have demonstrated that semiconducting ligands can spontaneously organize within the active layer to passivate defects and restrict halide diffusion, resulting in dramatic improvements in moisture and oxygen tolerance, reduced phase segregation, and increased thermal stability. Combining a team with expertise spanning the gamut of materials synthesis, computational materials design, and device engineering, we will develop a suite of multi-functional semiconducting ligands capable of improving the intrinsic stability perovskite materials while preserving and even enhancing their electronic properties. Through this strategy, we aim to achieve over 25% cell efficiency with operational stability over 20 years for future commercial use.
More information: https://letiandougroup.com/
In the past few years, perovskite solar cell technology has made significant progress, improving in efficiency to ~25%, while maintaining attractive economics due to the use of inexpensive soluble materials coupled with ultra low-cost deposition technologies. However, the real applications of these devices requires new breakthroughs in device performance, large-scale manufacturing, and improved stability. Among these, stability and degradation are among the most significant challenges for perovskite technologies. Perovskite absorber layer and organic charge transport materials can be sensitive to water, oxygen, high temperatures, ultraviolet light, and even electric field, all of which will be encountered during operation. To address these issues, significant efforts have been made, including mixed dimensionality and surface passivation; alternative absorber materials and formulations, new charge transport layers, and advanced encapsulation techniques, etc. Now, T80 lifetimes (i.e., the length of time in operation until measured output power is 80% of original output power) of over 1,000 hours have been demonstrated. However, it is still far below the industry required 20 years lifetime, indicating the ineffectiveness of current approaches. To make this advance, non-incremental and fundamentally new strategies are required to improve the intrinsic stability of perovskite active materials.
In this project, we propose a new paradigm to develop intrinsically robust perovskite active layers through the incorporation of multi-functional semiconducting conjugated ligands. In preliminary work, we have demonstrated that semiconducting ligands can spontaneously organize within the active layer to passivate defects and restrict halide diffusion, resulting in dramatic improvements in moisture and oxygen tolerance, reduced phase segregation, and increased thermal stability. Combining a team with expertise spanning the gamut of materials synthesis, computational materials design, and device engineering, we will develop a suite of multi-functional semiconducting ligands capable of improving the intrinsic stability perovskite materials while preserving and even enhancing their electronic properties. Through this strategy, we aim to achieve over 25% cell efficiency with operational stability over 20 years for future commercial use.
More information: https://letiandougroup.com/
Research categories:
Energy and Environment, Material Processing and Characterization, Nanotechnology
School/Dept.:
Chemical Engineering
Professor:
Letian
Dou
More information: https://letiandougroup.com/
Human Factors: Enhancing Performance of Nurses and Surgeons
Description:
High physical and cognitive workload among surgeons and nurses are becoming more common. The purpose of this project is to examine the contributors to these and develop technology to understand and enhance their performance.
The SURF student will participate in data collection in the operating room at Indiana University School of Medicine, data analysis and interpretation, and write his/her results for a journal publication. The student will regularly communicate his/her progress and results with faculty, graduate mentors, and surgeon collaborators.
More information: https://engineering.purdue.edu/YuGroup
The SURF student will participate in data collection in the operating room at Indiana University School of Medicine, data analysis and interpretation, and write his/her results for a journal publication. The student will regularly communicate his/her progress and results with faculty, graduate mentors, and surgeon collaborators.
More information: https://engineering.purdue.edu/YuGroup
Research categories:
Human Factors
Preferred major(s):
- Industrial Engineering
- Biomedical Engineering
- Computer Science
Desired experience:
Desired experience:
Human Factors, Machine Learning, Sensors, Programming
School/Dept.:
industrial engineering
Professor:
Denny
Yu
More information: https://engineering.purdue.edu/YuGroup
Human-AI interactions for designing
Description:
Student will co-develop human and AI interactions for creativity understanding and development of designs using Chat GPT as a framework. The student will collect the data with the Ph.D. student and do analysis of the data. Additionally student will explore the use of chat gpt to create virtual reality based designs.
Research categories:
Other
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Karthik
Ramani
Image-Based Models of Intracranial Aneurysms
Description:
Intracranial aneurysms often remain stable over years; however, a ruptured aneurysm may cause subarachnoid hemorrhage and stroke. Clinical management of incidentally discovered unruptured intracranial aneurysms (UIA) is challenging because risk of rupture must be weighed against the risk of intervention. In current clinical practice, guidelines for assessing risk of an UIA rupture are based on its morphology and patient’s clinical history. While numerous studies indicate local blood flow forces can affect aneurysm growth and rupture, the translational value of these studies remains controversial. The Rayz Lab is funded by an NIH R01 award to develop a comprehensive methodology for UIA risk stratification, with a multidisciplinary team that includes Purdue biomedical and mechanical engineers, neurosurgeons from Barrow Neurological Center, and radiologists and MR imaging scientists from U.C. San Francisco and Northwestern. They work on developing a novel approach to enhance in vivo MRI flow measurements to obtain reliable patient-specific hemodynamic metrics related to vessel remodeling and aneurysm growth (Fig. 3). The methodology combines morphological, clinical, and flow-related risk factors; thus, improving accuracy of predicted aneurysm stability or growth. This project will result in quantitative analyses and data that can improve clinical management of cerebral aneurysms; thereby, bridging the gap between engineering and clinical communities. MR imaging data acquired by collaborators at medical institutions will be used to generate patient-specific computational and experimental models of intra-aneurysmal flow. Angiographic images are used to construct patient-specific vascular geometries to be used for computational fluid dynamics (CFD) modeling or in vitro measurements in 3D-printed flow phantoms. Measured or computed flow metrics are correlated to observed aneurysm progression. Fellows will help with processing MR images and constructing patient-specific flow models. They will be involved in image segmentation, 3D modeling and printing of the geometries, and basic numerical simulations of the flow with the open-source modeling platform SimVascular. Fellows will gain research skills related to 1) basic principles of MR imaging, image-processing and computational modeling, and 3) fundamentals of flow physics and biomedical transport.
Research categories:
Cardiovascular Disease Research
Preferred major(s):
- Biomedical Engineering
School/Dept.:
Biomedical Engineering
Professor:
Vitaliy
Rayz
More information: https://engineering.purdue.edu/CFML
Immunoengineering for cancer immunotherapy: Reprogramming the function of natural killer cells in glioblastoma
Description:
The goal of this Summer undergraduate research program is to develop cell-based immunotherapies for glioblastoma (GBM) with engineered natural killer (NK) cells by targeting mechanisms of immunosuppression in the tumor microenvironment. Specifically, the project is focused on engineering the immune functions of NK cells to generate CAR-NK and CRISPR KO variants of NK cells to multispecifically, via synthetic genetic circuits, interact with the tumor microenvironment and rescue NK cell activity from dysfunction. In this context, the project will characterize and optimize a multi-specific CAR-NK cell product for the treatment of glioblastoma, designed to co-target multiple elements of NK cell dysfunction in the tumor microenvironment. This project builds on the lab’s recent publication describing the very first triple-engineered NK cell platform for GBM addressing antigen escape and immunometabolic reprogramming via CD73, and will incorporate elements that reprogram the cells’ metabolic function via, among other oncometabolite and glutamine targeting. These engineered NK cells will be developed from induced pluripotent stem cells as well as peripheral blood.
The student’s role in the project will be to isolate and differentiate immune cells, characterize and learn how to effectively engineer these cells to express various multispecific constructs, learn how to manipulate NK cell activity in the context of metabolic modulation via adenosine and glutamine, and perform functional assays including cytotoxicity, degranulation and immunophenotyping.
The student will also be involved in learning some computational analysis to analyze RNAseq and CRISPR screen data. The student will learn skills incell-based immunotherapy and immunoengineering, cancer biology, cell therapy product development and formulation, synthetic biology and genetic engineering.
In terms of lab participation, the student will be involved in weekly lab meetings with the rest of the lab where they will present their findings, and in regular individual meetings with the PI. The student will be trained and mentored by a graduate student.
The student’s role in the project will be to isolate and differentiate immune cells, characterize and learn how to effectively engineer these cells to express various multispecific constructs, learn how to manipulate NK cell activity in the context of metabolic modulation via adenosine and glutamine, and perform functional assays including cytotoxicity, degranulation and immunophenotyping.
The student will also be involved in learning some computational analysis to analyze RNAseq and CRISPR screen data. The student will learn skills incell-based immunotherapy and immunoengineering, cancer biology, cell therapy product development and formulation, synthetic biology and genetic engineering.
In terms of lab participation, the student will be involved in weekly lab meetings with the rest of the lab where they will present their findings, and in regular individual meetings with the PI. The student will be trained and mentored by a graduate student.
Research categories:
Cellular Biology, Medical Science and Technology
Preferred major(s):
- No Major Restriction
School/Dept.:
Industrial and Physical Pharmacy
Professor:
Sandro
Matosevic
More information: http://www.matoseviclab.com
Investigation of Depressurization of High Temperature Gas Cooled Reactor and Containment Building
Description:
High temperature gas-cooled reactors (HTGR) designs are likely candidates for the Next Generation Nuclear Plant (NGNP) due to their varied potential applications including process heat for chemical reactions and direct (Brayton) cycle power conversion. An HTGR fault that needs study is a break in the primary coolant boundary that leads to depressurization of the reactor vessel and loss of forced cooling of the core. In this accident, although most air in the reactor cavity and surrounding building is initially swept out by the helium, any remaining air in this space and the air re-entering from the surrounding building cavities can enter the primary coolant circuit through the break, and can cause severe damage to the graphite structures via oxidation. The amount of air entering the pressure vessel is a complex function of the primary helium inventory, and the discharged helium that displaces, and mixes, with the air in the cavity. A test facility is now built to simulate these phenomena and currently tests are conducted. The SURF students will help in conducting tests using test procedures, and acquiring data for various test condition such as helium flow rate, pressure and temperature and preform data analysis. Adequate training and background will be provided to perform the tests. It is team project with faculty, graduate students and undergraduate students.
Research categories:
Energy and Environment, Fluid Modelling and Simulation, Thermal Technology
Preferred major(s):
- Mechanical Engineering, Nuclear Engineering, Chemical Engineering, Indutrial Engineering, Electrical Engineering
- Nuclear Engineering
- Physics
Desired experience:
Course work in fluid mechanics, heat transfer desirable, aptitude to work on experiments, interest in developing laboratory skill, willing to work in team and learn
School/Dept.:
School of Nuclear Engineeing
Professor:
Shripad
Revankar
Investigation of Microstructure-Property-Processing Relationships in Concentrated Surfactant Solutions
Description:
Aqueous surfactant solutions are widely used to formulate detergent-based products for cleaning, laundry, and other personal care activities (e.g., shampoo, body wash). The goal of this SURF project is to determine how the microstructure and properties of surfactant-based solutions are affected by the removal of water and the addition of processing aids. The project's hypothesis is that certain chemical additives, including salt and perfumes, will change how the surfactants self-assemble in water which will in turn lead to changes in the surfactant solution's viscosity and flow behavior (its "rheology"). Through this project, the SURF student will: (1) learn about commercial surfactants like sodium laureth sulfate; (2) conduct advanced optical microscopy and image analysis on solutions with different amounts of water and additives; and (3) observe how the application of shear forces will change the self-assembled surfactant microstructures. The main outcome of this project will be a better understanding of the surfactant solution’s microstructure-property-processing relationships which will enable companies to more efficiently manufacture concentrated solutions to achieve desired properties and performance while also meeting sustainability goals such as reducing water from commercial products.
Research categories:
Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Enthusiasm for chemistry and an interest in materials research. Basic understanding of optics and digital photography and image processing would be awesome.
School/Dept.:
Materials Engineering
Professor:
Kendra
Erk
More information: https://soft-material-mechanics.squarespace.com/home/
Leg heat therapy to improve functional performance in peripheral artery disease (PAD)
Description:
Lower-extremity peripheral artery disease (PAD) is a manifestation of systemic atherosclerosis that affects more than 236 million individuals worldwide. Patients with PAD have a worse quality of life (QOL) than their healthy counterparts, due in part to the marked decline in physical functioning. Few non-invasive therapies currently exist to improve functional performance and restore QOL in people with PAD. Dr. Roseguini and his team are currently examining the benefits of home-based leg heat therapy (HT) on lower-extremity functioning and QOL in patients with PAD (Fig. 2). This novel approach consists of custom engineered trousers and a portable water pump. Hot water is circulated through the trousers, evenly heating the buttocks, thighs, and calf. This system is safe and convenient for application in a home setting without supervision. In a randomized, double-blind, sham-controlled clinical trial, PAD patients will be randomly assigned to one of two groups that either receive leg HT or a sham intervention. Participants randomized to the leg HT group will be asked to apply the treatment daily for 90 min using water-circulating trousers perfused with water heated to 42ºC. In the sham group, water at 33ºC will be circulated through the trousers. The primary study outcome is the change in 6-minute walk distance between baseline and the 12-week follow up. Performance on the 6-min walk test correlates closely with physical activity levels in the community and is linked to cardiovascular and all-cause mortality risk and the rate of mobility loss in patients with PAD. Secondary outcomes include performance on the short physical performance battery; handgrip strength; QOL (SF-36 and WIQ); and the morphology, fat content, maximal strength, and bioenergetics of the calf muscles as assessed using magnetic resonance imaging and phosphorus-31 magnetic resonance spectroscopy. Students will work in a multidisciplinary team composed of research coordinators, nurses, vascular surgeons, physicists, and biostatisticians. Fellows will be exposed to state-of-the-art techniques for assessment of walking performance, skeletal muscle function, and metabolism in PAD patients.
Research categories:
Cardiovascular Disease Research
Preferred major(s):
- Biomedical Engineering
- Health Science PreProfessional
- Kinesiology
School/Dept.:
Health and Kinesiology
Professor:
Bruno
Roseguini
More information: https://hhs.purdue.edu/directory/bruno-roseguini/
Localized Deep Learning for Decentralized and Dynamic Environments
Description:
Despite being widely used, global end-to-end learning has several key limitations. It requires centralized computation, making it feasible only on a single device or a carefully synchronized cluster. This restricts its use on unreliable or resource-constrained devices, such as commodity hardware clusters or edge computing networks. Localized deep learning has the potential to develop highly decentralized, parallel, asynchronous, and fault-tolerant algorithms that can learn on heterogeneous hardware devices under dynamic conditions while maintaining comparable model performance. The long-term vision would be an "Internet of AI" where devices can continuously learn in any conditions.
REU participants will be part of a collaborative team focused on developing novel localized deep learning approaches. One particular target project is a novel localized deep learning approach that we have named a Minimal Learning Unit (MLU). The goal is to create a learning algorithm with local objectives that learns rich unsupervised representations in a highly decentralized and fault-tolerant way. As one specific context, suppose a sensor network should be trained to detect a complex or global event such as anomalous activity over a large area of the wilderness. Each sensor has a very incomplete picture of the situation and can communicate with nearby sensors but cannot communicate with a global centralized server. The goal is to implement both width-parallel and depth-parallel learning on an unreliable set of sensor devices that have limited compute power. This project will focus on the fundamental aspects of novel local learning mechanisms in this highly decentralized environment.
REU participants will be part of a collaborative team focused on developing novel localized deep learning approaches. One particular target project is a novel localized deep learning approach that we have named a Minimal Learning Unit (MLU). The goal is to create a learning algorithm with local objectives that learns rich unsupervised representations in a highly decentralized and fault-tolerant way. As one specific context, suppose a sensor network should be trained to detect a complex or global event such as anomalous activity over a large area of the wilderness. Each sensor has a very incomplete picture of the situation and can communicate with nearby sensors but cannot communicate with a global centralized server. The goal is to implement both width-parallel and depth-parallel learning on an unreliable set of sensor devices that have limited compute power. This project will focus on the fundamental aspects of novel local learning mechanisms in this highly decentralized environment.
Research categories:
Big Data/Machine Learning, Deep Learning
Preferred major(s):
- No Major Restriction
School/Dept.:
ECE
Professor:
David
Inouye
Low-cost user-friendly biosensors for animal health
Description:
Infectious diseases are a leading cause of economic burden on food production from animals. For example, African Swine Fever is the deadliest animal pandemic and led to loss of half the swine herds in China in 2019. Detection of such diseases can be challenging because the clinical signs can be similar to other diseases.
Our research project focuses on developing a low-cost user-friendly biosensor based on paper that can detect which pathogen is causing the disease quickly and provide recommendations on appropriate next steps. Such a biosensor would provide a rapid readout to the farmer or the veterinary physician and guide surveillance efforts.
Lab members working in the team have three objectives: i) design, test, and optimize primers for detecting pathogens and genes associated with African Swine Fever, ii) build and field-test a paper-based device for conducting loop-mediated isothermal amplification, and iii) build and field-test a heating/imaging device for conducting the paper-based assay in the field.
The SURF student will work on one of the objectives depending on their background and experience.
Our research project focuses on developing a low-cost user-friendly biosensor based on paper that can detect which pathogen is causing the disease quickly and provide recommendations on appropriate next steps. Such a biosensor would provide a rapid readout to the farmer or the veterinary physician and guide surveillance efforts.
Lab members working in the team have three objectives: i) design, test, and optimize primers for detecting pathogens and genes associated with African Swine Fever, ii) build and field-test a paper-based device for conducting loop-mediated isothermal amplification, and iii) build and field-test a heating/imaging device for conducting the paper-based assay in the field.
The SURF student will work on one of the objectives depending on their background and experience.
Research categories:
Biological Simulation and Technology, IoT for Precision Agriculture
Preferred major(s):
- Biological Engineering - multiple concentrations
- Biochemistry
- Agricultural Engineering
- Biomedical Engineering
- Mechanical Engineering
- Electrical Engineering
Desired experience:
Relevant skills for the project:
• Wet lab skills and experience with molecular biology
• Autodesk Fusion 360 for 3D Modeling/Printing and Laser Cutting
• Python Programming Language for image processing and graphical user-interface using Raspberry Pi (or any other single board computer)
To be successful at this position, you should have a GPA>3.5, prior experience working in a lab, and the ability to work in a team.
School/Dept.:
Agricultural and Biological Engineering
Professor:
Mohit
Verma
More information: www.vermalab.com
Lyophilization Research at LyoHUB
Description:
Freeze-drying, also called lyophilization, is widely used in manufacturing of injectable pharmaceuticals, vaccines, biotech products, chemical reagents, food and probiotic cultures. The SURF undergraduate researchers will have an opportunity to be involved in one of the ongoing projects in LyoHUB technology demonstration facility in Discovery Park in collaboration with one or more of 20+ LyoHUB industry members.
Research categories:
Chemical, Computational/Mathematical, Computer Engineering and Computer Science, Industrial Engineering, Life Science, Mechanical Engineering
Desired experience:
Varies.
School/Dept.:
AAE/CHE
Professor:
Alina
Alexeenko
Magnetometry and noise thermometry
Description:
Quantum computing in the present day requires large macroscopic circuits and optical lattices and controls which are very expensive. Solid-state techniques for quantum computing will allow the miniaturization and the components and cost-effective scale-up of quantum technologies. A problem of solid-state quantum technologies is noise, especially thermal noise, which scrambles the quantum information. On this theme, one lofty goal of quantum engineering is achieving topologically protected quantum states which are protected from thermal decoherence. These materials have tale-tale signatures of stable quantization, which start to appear in thermal transport and magnetometry measurements. In this project, we attempt to set up such measurements at Purdue University at low temperatures on candidate topological materials.
Common thermometry measurements cannot be performed on thin-film samples required for this project. However, thermometry based on Johnson-Nyquist noise on Pt electrodes allows measuring the same when placed proximate to a quantum material in the thin-film form. Overall the method allows the local temperature measurement, and hence quantum decoherence, on a solid-state sample. The temperature difference, if quantized, is an excellent measure for quantization. The understanding for the thermal signatures of the sample will be complemented by magnetometry, which is will be achieved by the installation of a SQUID magnetometer by the sample (made by Quantum Design). Overall, the project requires that the student become an expert in thermometry using noise as a guiding principle. The project requires the candidate to become proficient in LabView and Python coding to transduce the noise signatures from e-beam platinum deposits on silicon in milli-Kelvin temperatures, both in the absence and the presence of a solid-state sample.
Common thermometry measurements cannot be performed on thin-film samples required for this project. However, thermometry based on Johnson-Nyquist noise on Pt electrodes allows measuring the same when placed proximate to a quantum material in the thin-film form. Overall the method allows the local temperature measurement, and hence quantum decoherence, on a solid-state sample. The temperature difference, if quantized, is an excellent measure for quantization. The understanding for the thermal signatures of the sample will be complemented by magnetometry, which is will be achieved by the installation of a SQUID magnetometer by the sample (made by Quantum Design). Overall, the project requires that the student become an expert in thermometry using noise as a guiding principle. The project requires the candidate to become proficient in LabView and Python coding to transduce the noise signatures from e-beam platinum deposits on silicon in milli-Kelvin temperatures, both in the absence and the presence of a solid-state sample.
Research categories:
Material Processing and Characterization, Nanotechnology, Thermal Technology
Preferred major(s):
- No Major Restriction
- Electrical Engineering
- Physics
- Materials Engineering
Desired experience:
Knowledge of Fourier transformation is useful.
School/Dept.:
Physics and Astronomy
Professor:
Arnab
Banerjee
More information: https://www.physics.purdue.edu/people/faculty/arnabb.php
Making Decisions About Household Items Exposed to Chemically Contaminated Drinking Water
Description:
Drinking water contamination is a global problem, and a challenge across North America. In the past decade, numerous chemical spills, wildfires, backflow incidents, and other activities have contaminated drinking water. As a result, many households have encountered water at their faucets that contained high levels of volatile organic compounds (VOC) and semi-volatile organic compounds (SVOC). Often, households are warned not to use the water due to ingestion, inhalation, and sometimes dermal exposure concerns. In some cases though, this water has contacted personal items and home water filters. Personal items have included baby pacifiers, bottles, toys, teething rings, utensils, and other items. If not cleaned thoroughly, these products may release chemicals that reach the user. Separately, home water filters may also be exposed to this highly VOC and SVOC contaminated drinking water but the degree these devices can reduce excessive contamination has gone unstudied. While home water filters are industry tested against low levels of contaminants, no such testing is available for post-disaster scenarios that involve excessive contamination levels. Despite this lacking information, officials have sometimes recommended households rinse personal items with clean tap water or purchase and use home water filter devices. The lack of prior testing inhibits households from knowing if the recommended actions are effective at protecting their health.
This study will develop a better understanding of the degree personal items and home water filters are contaminated by VOCs and SVOCs when exposed to contaminated water. Specific objectives are to: (1) Review the myriad products and types of materials that contact with water, (2) Review VOC/SVOC uptake phenomena associated with the specific plastics identified, (3) Down-select products and conduct VOC/SVOC contamination testing to estimate uptake, (4) Evaluate different practices for removing the contamination from the product. Results will help health officials and households understand whether contaminated products can be used after cleaning or should be discarded.
The student will work with a graduate student to contaminate and then evaluate different cleaning practices on various household items. The project will involve repeating recommended practices issued by public health officials and also evaluating other newer practices. The student will be taught on how to prepare solutions, collect samples, analyze data, and report results. Results are expected to be shared widely with public health officials after the project is completed.
This study will develop a better understanding of the degree personal items and home water filters are contaminated by VOCs and SVOCs when exposed to contaminated water. Specific objectives are to: (1) Review the myriad products and types of materials that contact with water, (2) Review VOC/SVOC uptake phenomena associated with the specific plastics identified, (3) Down-select products and conduct VOC/SVOC contamination testing to estimate uptake, (4) Evaluate different practices for removing the contamination from the product. Results will help health officials and households understand whether contaminated products can be used after cleaning or should be discarded.
The student will work with a graduate student to contaminate and then evaluate different cleaning practices on various household items. The project will involve repeating recommended practices issued by public health officials and also evaluating other newer practices. The student will be taught on how to prepare solutions, collect samples, analyze data, and report results. Results are expected to be shared widely with public health officials after the project is completed.
Research categories:
Chemical Unit Operations, Engineering the Built Environment, Environmental Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Strong motivation to learn and apply knowledge.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Andrew
Whelton
More information: www.PlumbingSafety.org
Mass spectrometry of biomolecules and nanoclusters
Description:
We are using mass spectrometry to study the localization of lipids, drugs, and proteins in biological tissues and to prepare novel functional interfaces using well-defined polyatomic ions. The student will work with a graduate student mentor to either perform nanocluster synthesis and characterization using mass spectrometry and electrochemical measurements or to develop new analytical approaches for quantitative analysis of biomolecules in biological samples. We are also developing computational approaches for connecting mass spectrometry imaging data with biochemical pathways. In both projects, the student will be trained to operate state-of-the-art mass spectrometers and perform independent data acquisition and analysis. The student will also work with scientific literature to obtain a broader understanding of the field.
Research categories:
Biological Characterization and Imaging, Medical Science and Technology, Nanotechnology
Preferred major(s):
- No Major Restriction
Desired experience:
general chemistry, calculus, analytical or physical chemistry
School/Dept.:
Chemistry
Professor:
Julia
Laskin
More information: https://www.chem.purdue.edu/jlaskin/
Microstructural control of energetic materials
Description:
The goal of this project is to understand how various material parameters affect the microstructure, and thus performance, of energetic materials (i.e. propellants, explosives, pyrotechnics). This project requires U.S. citizenship. The researcher will learn material science and manufacturing principles.
Research categories:
Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Exposure to material science
School/Dept.:
Mechanical Engineering
Professor:
Monique
McClain
More information: https://mcclain.team/
Mobility Evolution in the US: Evidence from Bike-sharing and Electric Vehicle Adoption
Description:
The project goal is to investigate the trends in next generation mobility in the US as evidenced by bike-sharing ridership and electric vehicle (EV) ownership. Objectives include: i) exploring the effect of the urban built environment and demographical fabric on the usage of bike-sharing; ii) forecasting EV ownership rates in the future considering the influence of incentives, new technologies, and barriers. The student candidate will collect historical data available from public sources such as US Census, US Department of Energy, FHWA and other sources and compile with bike-sharing ridership data from an open- source website, EV registration data and other survey data collected by the mentor/faculty advisor. Using these data, a baseline model (which can be a time series model, or any machine learning model) will be developed that will incorporate the effects of influencing factors affecting bike-sharing ridership and/or EV ownership. The student will get an opportunity to work with scholars in the STSRG group as well as to collaborate with the ASPIRE Engineering Research Center.
Research categories:
Big Data/Machine Learning, Energy and Environment, Engineering the Built Environment, Other
Preferred major(s):
- No Major Restriction
Desired experience:
- Knowledge of MS Excel and programming (R, Python, C++, Java)
- Basic knowledge or course work in statistics (regression, time series)
- Data analysis skills including downloading, cleaning, and merging different datasets
School/Dept.:
Civil Engineering
Professor:
Nadia
Gkritza
More information: https://engineering.purdue.edu/STSRG; https://engineering.purdue.edu/ASPIRE
Model and control strategy development to modernize the pharmaceutical tablet manufacturing process
Description:
The pandemic, such as COVID-19 crisis, has highlighted the requirement for smart manufacturing in pharmaceuticals. Continuous manufacturing is a highly promising solution given its lower capital cost, smaller footprint, and higher efficiency compared to batch manufacturing. Instead of relying on frequent off-line quality tests of samples from each batch, designing an effective and efficient process with knowledge and optimal control strategies becomes increasingly important. Ultimately, an automated smart system can be built to produce high-quality drug products with minimized errors from human intervention.
In a dry granulation tableting line, the powders are transformed into granules before being compressed into tablets. The granulation step can increase the powder flowability by enlarging particle size and improving the powder blend's content uniformity by minimizing segregation. The goals of this project include (1) investigating the impact of granulation on final tablet qualities and building high-fidelity models using first principles and machine learning, and (2) developing soft sensors to predict critical quality attributes such as tensile strength in real time. (3) Implementing model-based process control strategy to control end-to-end pharmaceutical manufacturing processes. All the research works will be conducted in Purdue's newly installed tablet manufacturing pilot plant at the FLEX Lab in Discovery Park.
In a dry granulation tableting line, the powders are transformed into granules before being compressed into tablets. The granulation step can increase the powder flowability by enlarging particle size and improving the powder blend's content uniformity by minimizing segregation. The goals of this project include (1) investigating the impact of granulation on final tablet qualities and building high-fidelity models using first principles and machine learning, and (2) developing soft sensors to predict critical quality attributes such as tensile strength in real time. (3) Implementing model-based process control strategy to control end-to-end pharmaceutical manufacturing processes. All the research works will be conducted in Purdue's newly installed tablet manufacturing pilot plant at the FLEX Lab in Discovery Park.
Research categories:
Big Data/Machine Learning, Chemical Unit Operations, Other
Preferred major(s):
- No Major Restriction
Desired experience:
Basic skills in programming (Python or MATLAB) and powder characterization experience would be a plus, but they are not necessary. All students are welcome if they are interested in hands-on experiments and pharmaceutical processes.
School/Dept.:
Chemical Engineering
Professor:
Gintaras
Reklaitis
Modernization of Pharmaceutical Drug Product Manufacturing
Description:
The continuous mode of manufacturing for pharmaceutical products represents the future of the pharmaceutical industry; it ultimately leads to cheaper and safer drugs, as well as a more reliable drug supply chain. To realize these advantages, however, effective fault detection and diagnostic systems need to be in place, so intervention strategies can be implemented in case the system goes malfunctions.
In this project, we will investigate the ribbon splitting phenomenon in a roller compactor, which is a phenomenon can adversely affect that quality of the product granules coming out of the roller compactor. Little is known about its impact on the product quality as well as the predictability of the phenomenon. The ability to predict this phenomenon can be a boon to effective implementation of condition-based maintenance strategies that have been accepted to be a critical requirement for the successful shift to continuous pharmaceutical manufacturing. This study requires particle technology expertise, which will be provided by Prof. Marcial Gonzalez in Mechanical Engineering, as well as process systems engineering expertise provided by Prof. Rex Reklaitis and Prof. Zoltan Nagy in Chemical Engineering.
In this project, we will investigate the ribbon splitting phenomenon in a roller compactor, which is a phenomenon can adversely affect that quality of the product granules coming out of the roller compactor. Little is known about its impact on the product quality as well as the predictability of the phenomenon. The ability to predict this phenomenon can be a boon to effective implementation of condition-based maintenance strategies that have been accepted to be a critical requirement for the successful shift to continuous pharmaceutical manufacturing. This study requires particle technology expertise, which will be provided by Prof. Marcial Gonzalez in Mechanical Engineering, as well as process systems engineering expertise provided by Prof. Rex Reklaitis and Prof. Zoltan Nagy in Chemical Engineering.
Research categories:
Big Data/Machine Learning, Chemical Unit Operations, Material Processing and Characterization, Other
Preferred major(s):
- No Major Restriction
Desired experience:
Python programming/coding experience is a PLUS, but not required, although enthusiasm to learn is a must. Students interested in a career in powder processing and/or pharmaceutical manufacturing, are encouraged to apply.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Gintaras
Reklaitis
Molecular microscopy to inform the design of medications
Description:
As illustrated with the COVID vaccines, storage and stability of medications can limit widespread availability. We are developing innovative chemical imaging tools with ultrafast pulsed lasers capable of mapping transformations within medical formulations to model and inform stability and bioavailability. Depending on the interests of the students, project scope can range from: i) bench-science in sample preparation and characterization, ii) instrument design and optical path alignment, iii) data acquisition and image analysis algorithm development, iv) partnership with collaborators in the pharmaceutical industry. We have a vibrant and diverse cohort of current researchers dedicated to fostering a supportive and collaborative research environment for all.
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Garth
Simpson
More information: http://www.chem.purdue.edu/simpson/
Multi-rotor Trajectory Planning
Description:
This project will focus on development of a multi-rotor 3D trajectory planner using the Casadi library and deployment on an open source autopilot. Path-planning in cluttered environments is a challenging task, and this project intends to make the algorithms more accessible to students and researchers in general.
One of the most significant hurdles in training students in the field of robotics, is the difficulty of embedded development. The Casadi library builds an equation graph that is quite similar to tensor flow used in machine learning. This equation graph can be used to compute gradients and jacobians using automatic differentiation across the graph which makes it an excellent candidate for use in optimal control and trajectory generation.
Another benefit of the Casadi library is that the equation graph can be used to generate efficient C-code that implements the algorithms for deployment to hardware systems. The Casadi library is built using C++ but the interfaces to Python and code generation to C make it quite flexible. Equations can be concisely represented in the Casadi language using the python language, that students can readily understand and modify.
The project will be conducted at the Purdue UAS Research and Test (PURT) facility. PURT is the largest indoor motion capture facility in the world. It is housed in an aircraft hangar at the Purdue airport and consists of 60 Qualisys Oqus7+ motion capture cameras. These cameras can track rigid bodies moving in the facility with 1 mm accuracy. In developing trajectory tracking algorithms, the students will be leveraging state of the art solutions that allow multi-rotors to approach speeds of 20 m/s while maneuvering autonomously through hoops. The motion capture system makes this easier by providing the exact pose of the vehicle in the facility, however we intend to study degradation of the tracking solution when using emulated GPS data in the facility. The camera system latency is 5 ms, significantly less than the latency of GPS receivers (typically 100 ms). Therefore the noise and latency patterns of GPS can be emulated and used to study the vehicles response in this scenario.
One of the most significant hurdles in training students in the field of robotics, is the difficulty of embedded development. The Casadi library builds an equation graph that is quite similar to tensor flow used in machine learning. This equation graph can be used to compute gradients and jacobians using automatic differentiation across the graph which makes it an excellent candidate for use in optimal control and trajectory generation.
Another benefit of the Casadi library is that the equation graph can be used to generate efficient C-code that implements the algorithms for deployment to hardware systems. The Casadi library is built using C++ but the interfaces to Python and code generation to C make it quite flexible. Equations can be concisely represented in the Casadi language using the python language, that students can readily understand and modify.
The project will be conducted at the Purdue UAS Research and Test (PURT) facility. PURT is the largest indoor motion capture facility in the world. It is housed in an aircraft hangar at the Purdue airport and consists of 60 Qualisys Oqus7+ motion capture cameras. These cameras can track rigid bodies moving in the facility with 1 mm accuracy. In developing trajectory tracking algorithms, the students will be leveraging state of the art solutions that allow multi-rotors to approach speeds of 20 m/s while maneuvering autonomously through hoops. The motion capture system makes this easier by providing the exact pose of the vehicle in the facility, however we intend to study degradation of the tracking solution when using emulated GPS data in the facility. The camera system latency is 5 ms, significantly less than the latency of GPS receivers (typically 100 ms). Therefore the noise and latency patterns of GPS can be emulated and used to study the vehicles response in this scenario.
Research categories:
Computer Architecture
Preferred major(s):
- Aeronautical and Astronautical Engineering
- Electrical Engineering
- Mechanical Engineering
School/Dept.:
AAE
Professor:
James
Goppert
More information: https://engineering.purdue.edu/PURT
Nanophotonic quantum optics with neutral atoms
Description:
Quantum technologies based on neutral atoms have played important roles in quantum simulation, quantum computation and quantum networks. An outstanding challenge for the neutral atom-based quantum information technologies is the realization of an efficient optical interface that would enable long-range transmission of quantum information via photonic qubits. Interfacing neutral atoms with nanoscale photonic waveguide and resonators promises strong atom-light interactions and new applications in quantum technologies. This project explores design of nanophotonics and experimental methods to couple cold (laser-cooled) or hot neutral atoms to electromagnetic modes in a nanophotonic microring resonator. Specifically, a participating undergraduate student will assist graduate students and senior group members in modeling and characterizing nanophotonic microring circuits, designing and constructing opto-electronics for stabilization and control of the optical circuits. A student may also assist in theoretical modeling and experiments on quantum nonlinear optics based on neutral atoms coupled to a nanophotonic resonator.
Research categories:
Nanotechnology
Preferred major(s):
- Physics
- Electrical Engineering
Desired experience:
Basic understanding of electronic circuits, electricity and magnetism, some knowledge of quantum mechanics would be preferred.
School/Dept.:
Physics and Astronomy
Professor:
Chen-Lung
Hung
More information: https://ultracold.physics.purdue.edu/
Nanoscale 3D printing
Description:
The ability to create 3D structures in the micro and nanoscale is important for many applications including electronics, microfluidics, and tissue engineering. This project deals with development and testing of a setup for building 3D structures using a femtosecond pulsed laser. A method known as two photon polymerization is used to fabricate such structures in which a polymer is exposed to laser and at the point of the exposure the polymer changes its structure. Moving the laser in a predefined path results in the desired shape and the structures. The setup incorporates the steps from designing a CAD model file to slicing the model in layers to generating the motion path of the laser needed for fabricating the structure. Possible improvements to the process by the undergraduate researcher include control algorithms, better CAD models, and better manufacturing strategies.
Research categories:
Deep Learning, Fabrication and Robotics, Material Processing and Characterization, Nanotechnology
Preferred major(s):
- Mechanical Engineering
Desired experience:
junior or senior standing, CAD, Matlab or Python, minimum GPA > 3.5
School/Dept.:
Mechanical Engineering
Professor:
Xianfan
Xu
More information: https://engineering.purdue.edu/NanoLab/
Nanoscale Heat Transfer
Description:
This project deals with study of heat transfer in very thin film materials using Raman Spectroscopy and Ultrafast laser systems. Heat transfer in nanoscale materials including 2D materials (very thin layered materials bonded by van der Waal’s force) shows superior characteristics for applications in numerous advanced devices. Their thermal transport behaviors are also different compared with bulk materials, and an understanding of the transport process is important for applications of these materials. We use non-contact, optical method (i.e., lasers etc.) to investigate heat flow in these materials. The undergraduate student will work with graduate students to learn to use state-of-the-art experimental facilities, carry out experiments, and analyze experimental results.
Research categories:
Energy and Environment, Nanotechnology, Thermal Technology
Preferred major(s):
- Mechanical Engineering
- Physics
Desired experience:
Thermoscience courses, interests in hands-on experiments, GPA>3.5
School/Dept.:
Mechanical Engineering
Professor:
Xianfan
Xu
More information: https://engineering.purdue.edu/NanoLab/
Operation and characterization of SPT-100 Hall thruster
Description:
Hall thrusters are widely utilized for spacecraft propulsion. Mars exploration missions currently planned by NASA utilize Deep Space Transport which is going to be propelled by Hall thruster technology. In Hall thruster neutral gas propellant is ionized and accelerated in ExB-field configuration to reach high propellant exhaust velocities in the range 10 - 50 km/s.
In this project student will work with Hall thruster SPT-100. The project will include operating the thruster and hollow cathode neutralizer, and measurements of electrical parameters of the thruster, exhaust plasma jet properties, and thrust. The student will use Langmuir probes (and other diagnostics) for measurements of plasma parameters and hanging pendulum thrust stand for the thrust measurements. In addition, the student is going to prepare and update related documentation for AAE 521 Plasma Lab.
In this project student will work with Hall thruster SPT-100. The project will include operating the thruster and hollow cathode neutralizer, and measurements of electrical parameters of the thruster, exhaust plasma jet properties, and thrust. The student will use Langmuir probes (and other diagnostics) for measurements of plasma parameters and hanging pendulum thrust stand for the thrust measurements. In addition, the student is going to prepare and update related documentation for AAE 521 Plasma Lab.
Research categories:
Other
Preferred major(s):
- No Major Restriction
School/Dept.:
AAE
Professor:
Alexey
Shashurin
More information: https://engineering.purdue.edu/EPPL
Optimization of magnetically responsive membranes for tissue testing. Collaborative project: Adrian Buganza Tepole (PI), Andres Arrieta (PI), Craig Goergen (PI)
Description:
There is a need for testing tissues in vivo to enable the development of better diagnostic tools and treatments. Actuating on tissues under homeostatic conditions (i.e., under biologically functional conditions) is challenging due to the complex boundary conditions introduced by any device interacting with the tissue. Therefore, biological tissue testing is mostly conducted ex-vivo, implying the loss of homeostasis and capturing of less relevant material properties. An alternative approach is to develop membranes responsive to remote stimulus such as magnetic fields.
This project aims to determine the microstructure design of polymer membranes with magnetically responsive particles to actuate on biological tissues under biologically relevant conditions. Specifically, this implies optimizing the material microstructure by orienting magnetically-responsive particles across the cross-section of the membrane.
Specific tasks & deliverables
1. To familiarize with the fabrication process of polymeric membranes embedding magnetically responsive particles.
2. To fabricate and conduct mechanical tests of magnetically responsive membranes.
3. To test the adhesion properties of the developed membranes to animal skin.
4. To conduct actuation tests of membrane+skin (bilayer) patches under magnetic fields as a function of particle orientation.
5. Documentation of the fabrication process, adhesion tests, and magnetic actuation results. Production of a final report, compatible with further presentation as a poster or student paper.
Special project outcomes
1. Familiarization with fabrication of magnetically-responsive materials.
2. Replicate material testing protocols for the adhesion and in-plane stretching response of polymeric membranes.
3. Familiarization with magnetic actuation of bilayer membranes.
4. Familiarization with testing of biological tissues.
This project aims to determine the microstructure design of polymer membranes with magnetically responsive particles to actuate on biological tissues under biologically relevant conditions. Specifically, this implies optimizing the material microstructure by orienting magnetically-responsive particles across the cross-section of the membrane.
Specific tasks & deliverables
1. To familiarize with the fabrication process of polymeric membranes embedding magnetically responsive particles.
2. To fabricate and conduct mechanical tests of magnetically responsive membranes.
3. To test the adhesion properties of the developed membranes to animal skin.
4. To conduct actuation tests of membrane+skin (bilayer) patches under magnetic fields as a function of particle orientation.
5. Documentation of the fabrication process, adhesion tests, and magnetic actuation results. Production of a final report, compatible with further presentation as a poster or student paper.
Special project outcomes
1. Familiarization with fabrication of magnetically-responsive materials.
2. Replicate material testing protocols for the adhesion and in-plane stretching response of polymeric membranes.
3. Familiarization with magnetic actuation of bilayer membranes.
4. Familiarization with testing of biological tissues.
Research categories:
Biological Characterization and Imaging, Fabrication and Robotics, Material Processing and Characterization, Medical Science and Technology
Preferred major(s):
- Biomedical Engineering
- Mechanical Engineering
- Materials Engineering
Desired experience:
Desirable experience:
Material characterization, prior experimental work on polymers
School/Dept.:
Mechanical Engineering
Professor:
Adrian
Buganza Tepole
More information: https://engineering.purdue.edu/ProgrammableStructures/
Optimize flux-bias-line design for superconducting quantum circuits
Description:
Superconducting (SC) circuit is a promising platform for realizing small quantum computers and other applications in quantum information sciences. The goal of the project is to improve the design of so-called "flux-bias-lines" on SC quantum devices that is critical for the accurate control of superconducting qubits. The student will be involved in the design, modeling, and numerical simulation of the flux-bias-lines. They will learn how to perform simulation and optimization in commercial physics simulation software (Comsol and Ansys). The improved design will make a direct impact on future devices in the lab.
This project focuses primarily on analytical design and numerical modeling. However, the student will also have a chance to participate in other experiments, working with other graduate and undergraduate researchers in the lab. These could include building microwave and radio-frequency electronics and custom hardware for the control and measurement of SC quantum devices.
This project focuses primarily on analytical design and numerical modeling. However, the student will also have a chance to participate in other experiments, working with other graduate and undergraduate researchers in the lab. These could include building microwave and radio-frequency electronics and custom hardware for the control and measurement of SC quantum devices.
Research categories:
Material Modeling and Simulation, Nanotechnology
Preferred major(s):
- Physics
- Electrical Engineering
- Computer Engineering
- Materials Engineering
Desired experience:
Freshman-level electricity and magnetism, and a basic understanding of electrical circuits are strongly recommended. Coding experience, preferably in python, will be very useful.
If you love building things (whether it's hardware, software, or a mix), and love solving puzzles, there is a very high chance that you will enjoy and excel in this project.
School/Dept.:
Physics and Astronomy
Professor:
Ruichao
Ma
More information: www.ma-quantumlab.com
Paper-based Microfluidics for Rapid Infectious Disease Diagnostics
Description:
The goal of the project is to design low-cost and user-friendly paper-based point-of-care (POC) diagnostics tests for the detection of a panel of infectious diseases.
These student will be involved directly in the research related to the fabrication and testing of these point-of-care technologies, designed to allow for sensitive, rapid, and repeatable multiplexed detection of a variety of food and waterborne pathogens with high precision and accuracy and minimal sample handling. Target pathogens include parasites such as P. falciparum, (malaria), and Cyclospora Cayetanensis (found in agricultural water that severely lacks detection technologies), along with bacteria-induced foodborne and waterborne infectious diseases such as E. Coli O157:H7, S. Typhimurium, Listeria spp. and Campylobacter Jejuni. These will be aptamer-enabled biosensors, which will be further amenable for the rapid and low cost detection of other diseases, such as inflammation marker panels for Troponin, CRP, IL-6, and TNF-α. Aptamers are DNA molecules with high stability, high affinity for both small molecules and whole-cell pathogens, and are robust when exposed to harsh environments.
The main biorecognition element for the detection of these whole-cell pathogens, responsible for infectious diseases of interest, will be aptamers, which will allow for whole-cell pathogen detection, without amplification or cell lysis. Blood serum samples will be loaded in the sample well, and will diffuse to the four testing areas, each labeled for one individual pathogen. The initially negative testing areas will display a pink color. A positive test for one of the pathogens will be recognized by a change of color from pink to purple. A 3D printed portable imaging box, equipped with an image capture system and embedded color recognition and analysis software will allow for images of the test strips to be taken at constant illumination, on site, at primary care clinics or anywhere at the patient’s home, regardless of time of the day and natural illumination conditions. The portable imaging device will be able to display the test results on the screen. Thus, the detection limit of the diagnostic devices will be pushed down to levels beyond the ones possible with the naked eye, considering the limitation of human vision performance, especially at low illumination levels. A negative test for one pathogen will display an unchanged pink color of the corresponding testing area. We will optimize the device that has already been demonstrated in preliminary work in Stanciu’s group for food samples for E. Coli O157:H7, Listeria monocytogenesis and Salmonella typhimurium, to serum samples for the four pathogens of interests. Ultimately, the project's objective is to establish device performance (detection limit, linear range) .
These student will be involved directly in the research related to the fabrication and testing of these point-of-care technologies, designed to allow for sensitive, rapid, and repeatable multiplexed detection of a variety of food and waterborne pathogens with high precision and accuracy and minimal sample handling. Target pathogens include parasites such as P. falciparum, (malaria), and Cyclospora Cayetanensis (found in agricultural water that severely lacks detection technologies), along with bacteria-induced foodborne and waterborne infectious diseases such as E. Coli O157:H7, S. Typhimurium, Listeria spp. and Campylobacter Jejuni. These will be aptamer-enabled biosensors, which will be further amenable for the rapid and low cost detection of other diseases, such as inflammation marker panels for Troponin, CRP, IL-6, and TNF-α. Aptamers are DNA molecules with high stability, high affinity for both small molecules and whole-cell pathogens, and are robust when exposed to harsh environments.
The main biorecognition element for the detection of these whole-cell pathogens, responsible for infectious diseases of interest, will be aptamers, which will allow for whole-cell pathogen detection, without amplification or cell lysis. Blood serum samples will be loaded in the sample well, and will diffuse to the four testing areas, each labeled for one individual pathogen. The initially negative testing areas will display a pink color. A positive test for one of the pathogens will be recognized by a change of color from pink to purple. A 3D printed portable imaging box, equipped with an image capture system and embedded color recognition and analysis software will allow for images of the test strips to be taken at constant illumination, on site, at primary care clinics or anywhere at the patient’s home, regardless of time of the day and natural illumination conditions. The portable imaging device will be able to display the test results on the screen. Thus, the detection limit of the diagnostic devices will be pushed down to levels beyond the ones possible with the naked eye, considering the limitation of human vision performance, especially at low illumination levels. A negative test for one pathogen will display an unchanged pink color of the corresponding testing area. We will optimize the device that has already been demonstrated in preliminary work in Stanciu’s group for food samples for E. Coli O157:H7, Listeria monocytogenesis and Salmonella typhimurium, to serum samples for the four pathogens of interests. Ultimately, the project's objective is to establish device performance (detection limit, linear range) .
Research categories:
Chemical Catalysis and Synthesis, Internet of Things (IoT), Medical Science and Technology, Nanotechnology, System-on-a-Chip
Preferred major(s):
- No Major Restriction
Desired experience:
General chemistry or biochemistry laboratory training.
School/Dept.:
Materials Engineering
Professor:
Lia
Stanciu
More information: https://lia-stanciu.squarespace.com/
Physics and Analytics of Lithium Batteries
Description:
Lithium ion (Li-ion) batteries are ubiquitous. Thermal, electrochemical, and degradation characteristics of these systems are critical toward safer and high-performance batteries for electric vehicles. As part of this research, physics-based and data-driven analytics of experimental and simulated performance under normal and anomalous operating conditions of lithium-ion and lithium metal batteries will be performed.
The final deliverable will be one research report (based on weekly progress presentations and updates) and one final presentation.
The final deliverable will be one research report (based on weekly progress presentations and updates) and one final presentation.
Research categories:
Energy and Environment, Material Modeling and Simulation, Material Processing and Characterization, Thermal Technology
Desired experience:
Strong analytical skill and desire to learn new experimental and modeling & analysis tools.
School/Dept.:
Mechanical Engineering
Professor:
Partha
Mukherjee
More information: https://engineering.purdue.edu/ETSL/
Physics-Informed Machine Learning to Improve the Predictability of Extreme Weather Events
Description:
Atmospheric blocking events and 'Bomb Cyclones' are an important contributor to high impact extreme weather events. Both these weather extremes lead to heat waves, cold spells, droughts, and heavy precipitation episodes, which have dire consequences for the public health, economy, and ecosystem. For example, the blocking-induced heat waves of 2003 in Europe led to tens of thousands of human casualties and tens of billions of dollars of financial damage.
Traditionally, prediction of extreme weather events is based on direct numerical simulation of regional or global atmospheric models, which are expensive to conduct and involve a large number of tunable parameters. However, with the rapid rise of data science and machine learning in recent years, this proposed work will apply convolutional neural network to an idealized atmospheric model to conduct predictability analysis of extreme weather events within this model. With this proposed machine-learning algorithm, our project will provide a robust forecast of heat waves and atmospheric blocking with a lead-time of a few weeks. With more frequent record-breaking heat waves in the future, such a prediction will offer a crucial period of time (a few weeks) for our society to take proper preparedness steps to protect our vulnerable citizens.
This project is based on developing and verifying the machine learning algorithm for detecting extreme weather events in an idealized model. We will use Purdue’s supercomputer Bell to conduct the simulations. The undergraduate student will play an active and important role in running the idealized model, and participate in developing the algorithms. As an important component of climate preparedness, the proposed work aims to develop a physics-informed machine learning framework to improve predictability of extreme weather events.
Closely advised by Prof. Wang, the student will conduct numerical simulations of an idealized and very simple climate model, and use python-based machine learning tools to predict extreme weather events within the model. Prof. Wang will provide weekly tutorial sessions to teach key techniques along with interactive hands-on sessions. The students will get access to the big datasets on Purdue’s Data Depot, analyze and visualize data of an idealized atmospheric model. The student will use convolutional neural networks (CNNs) to train and assess a Machine-Learning model. The student will further use feature tracking algorithm to backward identify the physical structure in the atmosphere that is responsible for the onset of extreme weather events.
Traditionally, prediction of extreme weather events is based on direct numerical simulation of regional or global atmospheric models, which are expensive to conduct and involve a large number of tunable parameters. However, with the rapid rise of data science and machine learning in recent years, this proposed work will apply convolutional neural network to an idealized atmospheric model to conduct predictability analysis of extreme weather events within this model. With this proposed machine-learning algorithm, our project will provide a robust forecast of heat waves and atmospheric blocking with a lead-time of a few weeks. With more frequent record-breaking heat waves in the future, such a prediction will offer a crucial period of time (a few weeks) for our society to take proper preparedness steps to protect our vulnerable citizens.
This project is based on developing and verifying the machine learning algorithm for detecting extreme weather events in an idealized model. We will use Purdue’s supercomputer Bell to conduct the simulations. The undergraduate student will play an active and important role in running the idealized model, and participate in developing the algorithms. As an important component of climate preparedness, the proposed work aims to develop a physics-informed machine learning framework to improve predictability of extreme weather events.
Closely advised by Prof. Wang, the student will conduct numerical simulations of an idealized and very simple climate model, and use python-based machine learning tools to predict extreme weather events within the model. Prof. Wang will provide weekly tutorial sessions to teach key techniques along with interactive hands-on sessions. The students will get access to the big datasets on Purdue’s Data Depot, analyze and visualize data of an idealized atmospheric model. The student will use convolutional neural networks (CNNs) to train and assess a Machine-Learning model. The student will further use feature tracking algorithm to backward identify the physical structure in the atmosphere that is responsible for the onset of extreme weather events.
Research categories:
Big Data/Machine Learning, Deep Learning, Fluid Modelling and Simulation
Preferred major(s):
- No Major Restriction
School/Dept.:
Earth, Atmospheric, and Planetary Sciences
Professor:
Lei
Wang
Plastics in infrastructure and the environment
Description:
Submitted via email
Research categories:
Other
Preferred major(s):
- No Major Restriction
School/Dept.:
Lyles School of Civil Engineering
Professor:
Andrew
Whelton
Plastics, Water, and Air: Chemical Emissions and Leaching
Description:
Water infrastructure is critical to the safety and economic health of communities. The restoration and maintenance of water supply and wastewater infrastructure are ongoing challenges for the Nation. Cured-in-place pipe (CIPP) composites technology is a popular method for repairing buried sewer pipes. CIPP technology is also now growing in popularity for repairing drinking water pipes. This is due in large part to economic considerations, as it can be 60-80% less costly than other repair alternatives. Unfortunately, the process of curing (polymerizing) the new plastic inside the damaged pipe can release hazardous materials into the air. For drinking water applications, the CIPPs can allow chemicals to leach into drinking water. Chemical air releases have resulted in illness to members of the general public and workers, and contributed to one worker fatality. The overall goal of this research is to reduce chemical volatilization from CIPP by understanding mechanisms of chemical release. This research directly addresses multiple National Academy of Science, Engineering, and Medicine grand challenges focused on restoring infrastructure, sustainably supplying water, building healthy cities, and reducing pollution.
The student will work with a graduate student and help evaluate chemical emissions during plastic manufacture using heat and steam. Sewer and drinking water resins will be explored. The student will help conduct the laboratory experiments, sample analysis, data analysis, interpretation, and reporting. Results will be shared with health officials, municipalities, and regulators after study completion. Prior studies where undergraduates have contributed on this topic can be found on the website listed below.
The student will work with a graduate student and help evaluate chemical emissions during plastic manufacture using heat and steam. Sewer and drinking water resins will be explored. The student will help conduct the laboratory experiments, sample analysis, data analysis, interpretation, and reporting. Results will be shared with health officials, municipalities, and regulators after study completion. Prior studies where undergraduates have contributed on this topic can be found on the website listed below.
Research categories:
Composite Materials and Alloys, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Strong work ethic and commitment to learn and apply knowledge.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Andrew
Whelton
More information: www.CIPPSafety.org
Promoting secure software supply chains with Sigstore
Description:
Many software products rely on components developed by other teams or companies. These components are known as the software supply chain. It is important that these components be trustworthy. The Sigstore project aims to help engineers guarantee that the components on which they rely were really produced by organizations that they trust. Sigstore is a fast-growing open-source project but we need help innovating its feature set and promoting its use.
Research categories:
Cybersecurity
Preferred major(s):
- No Major Restriction
Desired experience:
Strong programming skills, familiarity with Linux programming environment (e.g. you are comfortable on the terminal), some knowledge of cybersecurity exploits (e.g. buffer overflows). Knowledge of web systems a plus.
Successful applicants are likely EE, CompEng, or CS majors.
School/Dept.:
Electrical & Computer Engineering
Professor:
James
Davis
More information: https://www.sigstore.dev/
Quantum Characterization Setup Software Development
Description:
Our research group is in the midst of constructing new quantum optics characterization setups. These setups are used to characterize the photoluminescence properties of different quantum emitters down to the single photon level! This single emitter level property characterization is critical in the development of quantum optical computing, sensing, and communications! We are looking for an undergraduate student that can help with the development of software to control the various parts of the setup and to write the drivers to interface the hardware to the upper-level analysis software required to control the setup.
Research categories:
Big Data/Machine Learning, Deep Learning, Fabrication and Robotics, Material Processing and Characterization, Nanotechnology, Other
Preferred major(s):
- No Major Restriction
Desired experience:
Experience with embedded systems programming, analog to digital conversion experience, driver experience, python software experience.
School/Dept.:
Electrical and Computer Engineering (ECE)
Professor:
Vladimir
Shalaev
Quantum Characterization Setup Software Development
Description:
This research project focuses on the development of software algorithms for automated analysis of single photon emitters in silicon nitride nanopillars. The students role will be to develop these algorithms to help produce large datasets to be used in machine learning studies and in basic process development studies of emitters generated though the annealling of SiN/SiO nanopillars.
Research categories:
Big Data/Machine Learning, Material Processing and Characterization, Nanotechnology, System-on-a-Chip
Preferred major(s):
- No Major Restriction
Desired experience:
Strong python programming skills and algorithm development skills. Additionally, image processing skills are a plus!
School/Dept.:
Electrical and Computer Engineering (ECE)
Professor:
Alexander
Kildishev
Quantum Sensing for Next-Generation Microelectronics
Description:
Discovering (a) novel scalable quantum platforms and (b) low-dissipation, compact, and high-frequency microelectronics systems are widely believed to be one of the central grand challenges faced by our society to fulfill the need for creating information processing, memory, and communication technologies of the future (next-generation micro-electronics). Crucially, along with the advancement in theory and synthesis, the success of these efforts relies on the development of sensitive probes that can couple efficiently with this broad class of systems to gain a fundamental understanding of the phenomena of interest. However, this endeavor is proving challenging for present-day probes. This is because, in the regime of interest, the underlying signals that need detection are typically weak and/or vary at small (nanoscale) spatial and high frequency (GHz) temporal scales. Recently, a new class of probes, dubbed quantum probes, has emerged to solve this challenge. Such quantum probes take advantage of the Achilles heel of quantum systems – their strong sensitivity to the environment. Several well-established quantum sensing protocols have been developed which can sense signals with sensitivities and/or resolution beyond those offered by classical sensors. This has opened doors to use these novel quantum metrology tools for probing next-generation microelectronics systems in regimes that were previously not possible, and thus aid in their advancement.
This project focuses on the development and application of one such quantum metrology tool—optically active spin qubit (e.g. Nitrogen-Vacancy center in diamond). In particular, it aims to realize the basic working principle and design of a spin-qubit-based quantum system to enable multiple lengths (from chip-scale widefield geometry to nanoscale) and ultrafast time-scale (10’s GHz) probing of new-era microelectronic systems by measuring the stray magnetic fields and/or temperature profile they generate. As one specific example of an application area, we will apply the developed capability to probe for the first-time low frequency noise of spintronic materials. The scope of this project includes the design and augmentation of existing setup (e.g. to widefield geometry), benchmarking of the developed capability via testing performed on known signals, and design of sensing protocols along with the demonstration of beyond state-of-the-art metrology of spintronic materials (via multi-physics modeling in conjunction with experiments).
This project focuses on the development and application of one such quantum metrology tool—optically active spin qubit (e.g. Nitrogen-Vacancy center in diamond). In particular, it aims to realize the basic working principle and design of a spin-qubit-based quantum system to enable multiple lengths (from chip-scale widefield geometry to nanoscale) and ultrafast time-scale (10’s GHz) probing of new-era microelectronic systems by measuring the stray magnetic fields and/or temperature profile they generate. As one specific example of an application area, we will apply the developed capability to probe for the first-time low frequency noise of spintronic materials. The scope of this project includes the design and augmentation of existing setup (e.g. to widefield geometry), benchmarking of the developed capability via testing performed on known signals, and design of sensing protocols along with the demonstration of beyond state-of-the-art metrology of spintronic materials (via multi-physics modeling in conjunction with experiments).
Research categories:
Microelectronics
Preferred major(s):
- No Major Restriction
- Physics
- Electrical Engineering
School/Dept.:
ECE
Professor:
Pramey
Upadhyaya
RCAC Anvil REU Internship (x6)
Description:
Internship opportunities:
1. Data analytics: Instrument and perform analysis of scientific application workloads on the Anvil system
2. High Performance Computing (HPC): Extend the Anvil system to burst scientific workflows into the Microsoft Azure cloud
3. Kubernetes: To support science gateways applications, extend Anvil’s Kubernetes-based composable subsystem to use cloud-based Kubernetes clusters
4. Containers to Support Education: Enable the use of large-scale notebook deployments to provide interactive access to Anvil in support of education
Applicants must be U.S. citizens. Open to undergrad students from all backgrounds.
1. Data analytics: Instrument and perform analysis of scientific application workloads on the Anvil system
2. High Performance Computing (HPC): Extend the Anvil system to burst scientific workflows into the Microsoft Azure cloud
3. Kubernetes: To support science gateways applications, extend Anvil’s Kubernetes-based composable subsystem to use cloud-based Kubernetes clusters
4. Containers to Support Education: Enable the use of large-scale notebook deployments to provide interactive access to Anvil in support of education
Applicants must be U.S. citizens. Open to undergrad students from all backgrounds.
Research categories:
Big Data/Machine Learning, Computer Architecture, Internet of Things (IoT), Other
Preferred major(s):
- No Major Restriction
Desired experience:
Linux command line experience preferred. However, programming experience is not a requirement for our REU. We seek students with a range of computational backgrounds and will provide research opportunities appropriate for beginner to advanced levels in computing. Our REU is designed to help you develop these computational skills.
School/Dept.:
RCAC
Professor:
Amanda
Hassenplug
More information: https://www.rcac.purdue.edu/anvil/reu
Random walks with applications in polymer physics and protein crystallization
Description:
Continuous random walks (CRW) – i.e. processes with diffusion and drift – are ubiquitous
in chemistry, appearing in a wide range of fields such as heat and mass transfer, Brownian dynamics (BD) simulations, polymer physics, nucleation theory, and chemical
reaction pathways. Oftentimes in the aforementioned applications, one is concerned with simulating specific types of rare events such as random paths that stay within a particular region of phase space, those which end in a particular region, or those which reach one region before another. Illustrative examples include (a) generating polymer conformations with a specific topology (e.g., rings); (b) examining random pathways in a reaction coordinate space that produce one product compared to others (e.g., polymorphs in crystallization); (c) examining diffusion trajectories of proteins that stay in a region for a sufficiently long time before reaction occurs.
In this project, we are thinking of ways to generate such rare pathways efficiently. The SURF student will work with a graduate student to develop efficient approximations for random walks with a constraint, by examining the partial differential equations that describe different random walks. The student will also look at some example problems in polymer physics where this application could be used.
in chemistry, appearing in a wide range of fields such as heat and mass transfer, Brownian dynamics (BD) simulations, polymer physics, nucleation theory, and chemical
reaction pathways. Oftentimes in the aforementioned applications, one is concerned with simulating specific types of rare events such as random paths that stay within a particular region of phase space, those which end in a particular region, or those which reach one region before another. Illustrative examples include (a) generating polymer conformations with a specific topology (e.g., rings); (b) examining random pathways in a reaction coordinate space that produce one product compared to others (e.g., polymorphs in crystallization); (c) examining diffusion trajectories of proteins that stay in a region for a sufficiently long time before reaction occurs.
In this project, we are thinking of ways to generate such rare pathways efficiently. The SURF student will work with a graduate student to develop efficient approximations for random walks with a constraint, by examining the partial differential equations that describe different random walks. The student will also look at some example problems in polymer physics where this application could be used.
Research categories:
Biological Simulation and Technology, Fluid Modelling and Simulation
Preferred major(s):
- Chemical Engineering
- Physics
- Mathematics
- Mechanical Engineering
- Materials Engineering
- Mechanical Engineering
- Chemistry
Desired experience:
The student should have a background in differential equations, probability, and a basic knowledge of coding. Knowledge in partial differential equations is desired (if possible).
School/Dept.:
Chemical Engineering
Professor:
Vivek
Narsimhan
More information: https://viveknarsimhan.wixsite.com/website
Rapid characterization of high temperature alloys
Description:
Refractory alloys are used in extreme environments (high temperatures, corrosive environments, high stresses, high irradiation fluxes) and enable transportation, energy generation, and defense technologies to be realized. However, refractory alloys developed to date lack a balance of high temperature properties, namely oxidation resistance and strength, which, in some situations, prevents them from replacing other state of the art materials such as Ni-based superalloys. Over the past year, our group has developed machine learning and other predictive models that enable high-throughput discovery of novel refractory alloys exhibiting such balance of properties.
This SURF project aims to characterize the strength and oxidation resistance of tens to hundreds of refractory alloys using high-throughput characterization methods. Such methods for this project could include: Raman microscopy, surface profilometry, X-ray diffraction, automated scanning electron microscopy, and indentation. As part of this project, you will learn at least two of these methods and apply them to compositionally graded specimens comprising up to 85 unique alloys - potentially encompassing thousands of unique alloy compositions.
Significant data will be collected during this project, and the data must be collected and stored according to the FAIR principles (Findability, Accessibility, Interoperability, Reuse). Thus, some background in Python programming and Excel is desired for this project. It is expected that at the end of this project, you will publish a publicly accessible NanoHub.org tool that enables users from across the world to access and analyze the data.
This SURF project aims to characterize the strength and oxidation resistance of tens to hundreds of refractory alloys using high-throughput characterization methods. Such methods for this project could include: Raman microscopy, surface profilometry, X-ray diffraction, automated scanning electron microscopy, and indentation. As part of this project, you will learn at least two of these methods and apply them to compositionally graded specimens comprising up to 85 unique alloys - potentially encompassing thousands of unique alloy compositions.
Significant data will be collected during this project, and the data must be collected and stored according to the FAIR principles (Findability, Accessibility, Interoperability, Reuse). Thus, some background in Python programming and Excel is desired for this project. It is expected that at the end of this project, you will publish a publicly accessible NanoHub.org tool that enables users from across the world to access and analyze the data.
Research categories:
Big Data/Machine Learning, Material Processing and Characterization
Preferred major(s):
- Materials Engineering
- Chemical Engineering
- Mechanical Engineering
- Physics
Desired experience:
- Materials Characterization
- Computer Science / Programming (python preferred)
School/Dept.:
Materials Engineering
Professor:
Michael
Titus
Real-Time Measurements of Volatile Chemicals in Buildings with Proton Transfer Reaction Mass Spectrometry
Description:
The objective of this project is to utilize state-of-the-art proton transfer reaction mass spectrometry (PTR-MS) to evaluate emissions and exposures of volatile chemicals in buildings. My group is investigating volatile chemical emissions from consumer and personal care products, disinfectants and cleaning agents, and building and construction materials. You will assist graduate students with full-scale experiments with our PTR-MS in our new Purdue zEDGE Tiny House and process and analyze indoor air data in MATLAB.
Research categories:
Big Data/Machine Learning, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Preferred skills: experience with MATLAB, Python, or R. Coursework: environmental science and chemistry, physics, thermodynamics, heat/mass transfer, and fluid mechanics.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Nusrat
Jung
More information: https://www.purdue.edu/newsroom/stories/2020/Stories%20at%20Purdue/new-purdue-lab-provides-tiny-home-for-sustainability-education.html
Regenerative Heart Valves and Vascular Grafts to Address Pediatric Limitations
Description:
The Harbin Lab has identified a liquid, fibril-forming type I collagen polymer (oligomer) that under physiologic conditions forms collagen-fibril scaffolds with features of collagen scaffolds found naturally in tissues, namely D-banded ultrastructure, high mechanical stability, and slow turnover in vivo (high resistance to proteolytic degradation). This low-cost biologic can be formulated as an injectable, in situ scaffold-forming product with applications as a regenerative soft tissue filler [3, 11]. As purified collagen liquid that self-assembles, it is also amenable to scalable biomanufacturing where geometry, fibril architecture, and mechanical properties can be controlled and therapeutic cells and drugs readily encapsulated [12-16]. Driven by collagen-cell mechanochemical signaling, these materials show site-appropriate tissue regeneration by a paradigm shifting mechanism of action in which they persist, integrate, and support vascularization and site-appropriate tissue formation without evoking an inflammatory-mediated foreign body response. Collectively, these characteristics uniquely position oligomer as an enabling biopolymer for engineering advanced regenerative tissue replacements, including heart valves and vascular grafts that adapt and grow with patients.
The Harbin Lab aims to apply an integrative multi-disciplinary approach to the design, fabrication, and evaluation of functional and regenerative blood vessel and heart valve replacements. Students will apply and scale innovative collagen polymer device biofabrication methods informed by computational modeling, non-clinical performance testing (e.g., hierarchical structural analysis, multi-scale biomechanical and biotransport properties), and preclinical animal testing. Notably, previous undergraduate mentees have been integral to development and validation of this biomaterial platform. Alexis Yrineo, working under the co-mentorship of Drs. Goergen and Harbin, performed early preclinical studies demonstrating the potential of in situ scaffold-forming collagen to limit abdominal aortic aneurysm expansion, which led to a first-author publication, and an undergraduate design team won Best Senior Design Project for development of an early-stage prototype regenerative heart valve replacement for children with tetralogy of Fallot. Fellows will gain research skills related to 1) innovative fabrication of replacement tissues, including prioritization of multi-scale design specifications based on cardiovascular surgeon and patient needs; 2) biomechanical and in-flow performance testing using standard methods for material characterization; and 3) use of animal models and non-invasive imaging modalities for preclinical evaluation and testing.
The Harbin Lab aims to apply an integrative multi-disciplinary approach to the design, fabrication, and evaluation of functional and regenerative blood vessel and heart valve replacements. Students will apply and scale innovative collagen polymer device biofabrication methods informed by computational modeling, non-clinical performance testing (e.g., hierarchical structural analysis, multi-scale biomechanical and biotransport properties), and preclinical animal testing. Notably, previous undergraduate mentees have been integral to development and validation of this biomaterial platform. Alexis Yrineo, working under the co-mentorship of Drs. Goergen and Harbin, performed early preclinical studies demonstrating the potential of in situ scaffold-forming collagen to limit abdominal aortic aneurysm expansion, which led to a first-author publication, and an undergraduate design team won Best Senior Design Project for development of an early-stage prototype regenerative heart valve replacement for children with tetralogy of Fallot. Fellows will gain research skills related to 1) innovative fabrication of replacement tissues, including prioritization of multi-scale design specifications based on cardiovascular surgeon and patient needs; 2) biomechanical and in-flow performance testing using standard methods for material characterization; and 3) use of animal models and non-invasive imaging modalities for preclinical evaluation and testing.
Research categories:
Cardiovascular Disease Research
Preferred major(s):
- Biomedical Engineering
School/Dept.:
Biomedical Engineering
Professor:
Sherry
Voytik-Harbin
More information: https://engineering.purdue.edu/BME/Academics/Graduate/CurrentResearchOpportunities/voytik_harbin
Retrospectively Gated Multispectral 4D Photoacoustic Cardiovascular Imaging
Description:
Currently, clinical care for cardiac patients employs traditional tests that lack sensitivity, require hours to complete, or are invasive. While cardiac ultrasound can be used to visualize anatomical structures and heart dynamics [1, 2], it does not provide compositional or oxygenation information of tissue. Multispectral vibrational photoacoustic tomography (VPAT) imaging has the potential to quantify a variety of biological components, including lipids and both oxygenated and deoxygenated blood due to their unique wavelength-dependent excitations [3]. However, its utility for cardiac imaging has yet to be explored, largely due to temporal resolution being limited by the laser pulse repetition frequency. Fellows will assist in a multidisciplinary project to develop a retrospective gating imaging technique to overcome limitations in sampling rate by reordering VPAT and ultrasound images based on the appropriate cardiac time point (Fig. 1). The objective is to create a technique capable of acquiring 4D cardiac datasets to assess changes in tissue oxygenation and composition. The central hypothesis is that changes in cardiac oxygenation and lipid accumulation due to disease initiation and progression can be identified with a combined multispectral 4D VPAT/ultrasound technique well before any changes are observed with traditional ultrasound. Development of this multimodal imaging approach will provide a holistic view of the complex interplay between changes in cardiovascular morphology, hemodynamics, kinematics, and composition. Students will 1) work with graduate mentors to optimize the imaging system by quantifying signal-to-noise ratio, 2) analyze results via advanced image processing techniques, and 3) assess use of this approach using murine cardiac disease models. Fellows will gain research skills in multiple areas related to 1) medical imaging theory, 2) animal surgery, and 3) clinical treatments for myocardial infarction.
Research categories:
Cardiovascular Disease Research
Preferred major(s):
- Biomedical Engineering
Desired experience:
Small animal handling, image processing, coding
School/Dept.:
Biomedical Engineering
Professor:
Craig
Goergen
More information: https://engineering.purdue.edu/cvirl
Roles of Cytoskeletal Structures in Neurite Outgrowth
Description:
Neurite outgrowth is a physiological process where neurons generate farther projections, which is essential for wiring nervous systems during development and regeneration after trauma or disease. The neurite outgrowth is known to be driven mainly by interactions between cytoskeletal components, such as microtubules, cross-linkers, and dynein motors. Previous studies suggested that dynein motors interact with and walk on a pair of neighboring microtubules which are transiently linked by cross-linkers. However, it still remains elusive how these molecular interactions result in neurite elongation at larger scale. To investigate the mechanisms of neurite elongation, we developed an agent-based model that consists of essential cytoskeletal elements with consideration of their mechanical properties and physical interactions. In this project, using the agent-based model, a student will explore extensive parametric spaces to find intrinsic mechanisms of the neurite outgrowth.
Research categories:
Biological Simulation and Technology, Cellular Biology
Preferred major(s):
- Bioengineering
- Mechanical engineering
- Biochemistry
Desired experience:
MATLAB, C language
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Taeyoon
Kim
More information: https://engineering.purdue.edu/mct
SCALE Heterogeneous Integration/ Advanced Packaging: 3D Cryogenic Packaging for Superconducting Computing
Description:
This project is one of several SCALE SURF research projects. SCALE projects are restricted to students who are U.S. Citizens. By applying to this project, you can be considered for any of the SCALE projects with one application. See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023.
In 2017, a large-scale, 3D integrated quantum processor was demonstrated by MIT Lincoln Laboratory using heterogeneous 3D integration to create an architecture that enables the use of the third dimension without sacrificing qubit performance [D. Rosenberg, et al., Nature 2017]. In these quantum applications, conventional Sn-based solder bumps are not reliable while Indium and bismuth-based solders are promising for 3D integration at low temperatures. In this topic, new cryogenic compatible packaging materials and cryogenic superconducting multi-chip bonding techniques are needed to further explore and investigate the microelectronics devices and packages at low/cryogenic temperatures.
Reference: Rosenberg, D., et al. "3D integrated superconducting qubits." npj quantum information 3.1 (2017): 1-5.)
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
In 2017, a large-scale, 3D integrated quantum processor was demonstrated by MIT Lincoln Laboratory using heterogeneous 3D integration to create an architecture that enables the use of the third dimension without sacrificing qubit performance [D. Rosenberg, et al., Nature 2017]. In these quantum applications, conventional Sn-based solder bumps are not reliable while Indium and bismuth-based solders are promising for 3D integration at low temperatures. In this topic, new cryogenic compatible packaging materials and cryogenic superconducting multi-chip bonding techniques are needed to further explore and investigate the microelectronics devices and packages at low/cryogenic temperatures.
Reference: Rosenberg, D., et al. "3D integrated superconducting qubits." npj quantum information 3.1 (2017): 1-5.)
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Processing and Characterization, Microelectronics, Nanotechnology, System-on-a-Chip
Preferred major(s):
- Electrical Engineering
- Materials Engineering
- Mechanical Engineering
Desired experience:
1. Microelectronics, micro/nanotechnology courses
2. Clean room fabrication experience
3. Enthusiasm for material fabrication and characterizations
4. Familiar with SEM, TEM analysis
School/Dept.:
ME
Professor:
Tiwei
Wei
More information: https://alphalab-purdue.org/
SCALE Heterogeneous Integration/ Advanced Packaging: Glass Interposer Development for 3D Heterogenous Integration
Description:
This project is one of several SCALE SURF research projects. SCALE projects are restricted to students who are U.S. Citizens. By applying to this project, you can be considered for any of the SCALE projects with one application. See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023.
Interposer is one of the most potential solutions for future 3D integration with ultrafine pitch. Silicon interposer has been developed in both industry and academia. However, silicon interposer has limitations, such as low productivity due to limited wafer size, extra expensive semiconductor fabrication processes, and poor electrical properties like insert loss and signal crosstalk. On the contrary, glass can be one kind of promising material as an interposer because of its excellent properties, such as good electrical resistivity, relatively low CTE compared to organic material, and possible high productivity with big panel sizes provided by glass suppliers.
Recent research studies have mainly focused on three challenges in glass interposer technology: (1) formation of the fine pitch via, which is more difficult than through silicon via (TSV) due to the unfavorable etching process ; (2) via metallization and via filling process, which become much more complicated because of the rough morphology of TGV surface, and difficulty to fill the tapered via through Damascus electroplating; (3) reliability concern, which is caused by brittleness and poor mechanical strength of glass.
Through glass via fabrications
Reference: Wei, T. W., Cai J.*, et al. Performance and reliability study of TGV interposer in 3D integration[C]//2014 IEEE 16th Electronics Packaging Technology Conference (EPTC). IEEE, 2014: pp. 601-605.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Interposer is one of the most potential solutions for future 3D integration with ultrafine pitch. Silicon interposer has been developed in both industry and academia. However, silicon interposer has limitations, such as low productivity due to limited wafer size, extra expensive semiconductor fabrication processes, and poor electrical properties like insert loss and signal crosstalk. On the contrary, glass can be one kind of promising material as an interposer because of its excellent properties, such as good electrical resistivity, relatively low CTE compared to organic material, and possible high productivity with big panel sizes provided by glass suppliers.
Recent research studies have mainly focused on three challenges in glass interposer technology: (1) formation of the fine pitch via, which is more difficult than through silicon via (TSV) due to the unfavorable etching process ; (2) via metallization and via filling process, which become much more complicated because of the rough morphology of TGV surface, and difficulty to fill the tapered via through Damascus electroplating; (3) reliability concern, which is caused by brittleness and poor mechanical strength of glass.
Through glass via fabrications
Reference: Wei, T. W., Cai J.*, et al. Performance and reliability study of TGV interposer in 3D integration[C]//2014 IEEE 16th Electronics Packaging Technology Conference (EPTC). IEEE, 2014: pp. 601-605.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Modeling and Simulation, Material Processing and Characterization, Microelectronics, Nanotechnology, System-on-a-Chip
Preferred major(s):
- Electrical Engineering
- Mechanical Engineering
- Materials Engineering
Desired experience:
1. Microelectronics, micro/nanotechnology courses
2. Clean room fabrication experience
3. Enthusiasm for material fabrication and characterizations
4. Familiar with SEM, and TEM analysis.
School/Dept.:
ME
Professor:
Tiwei
Wei
More information: https://alphalab-purdue.org/
SCALE Heterogeneous Integration/ Advanced Packaging: High-Temperature Solders for Aerospace and Defense
Description:
This project is one of several SCALE SURF research projects, and is restricted to US citizens. If you are interested in more than one SCALE SURF project, you can apply to all of them with one application. ** Be sure to address each project by name in your application. ** See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023.
Low-melting point metals based on tin are used to connect semiconductor packages to circuit boards. The specific solder composition that is chosen for a product depends on the product's use conditions, for example, consider the differences in use conditions for a cell phone, an implanted pace maker, strapped onto a car engine, and in a satellite.. This project explores the performance and manufacturing differences between solders for different use cases as a function of composition and application. We are collaborating with researchers from Auburn University, the University of Maryland, Raytheon, BAE Systems, the Department of Defense to develop a guide for solder selection for aerospace and defense applications. These researchers have backgrounds in materials engineering, mechanical engineering, industrial engineering, and electrical engineering, so many different skill sets are needed and you will see different perspectives. This project will require extensive review of the literature and performing materials characterization, processing, manufacturing, and reliability experiments. Student researchers will learn a wide range of materials and mechanical property, processing, and characterization techniques and will work closely with faculty and graduate students from Materials Engineering and Mechanical Engineering.
To apply to a SCALE SURF project, go to the SURF website: https://engineering.purdue.edu/Engr/Research/EURO/SURF/Research/Y2023
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Preferred majors:
• Materials Engineering
• Mechanical Engineering
Required Experience and Skills:
Desired experience:
• Experience with programming in Python, C/C++, and/or MATLAB.
• Enthusiasm for scientific research.
• Understanding of introductory materials science and engineering concepts.
Academic Years Eligible:
Rising juniors and seniors with the desired experience will be preferred, but rising sophomores are also eligible to apply.
Low-melting point metals based on tin are used to connect semiconductor packages to circuit boards. The specific solder composition that is chosen for a product depends on the product's use conditions, for example, consider the differences in use conditions for a cell phone, an implanted pace maker, strapped onto a car engine, and in a satellite.. This project explores the performance and manufacturing differences between solders for different use cases as a function of composition and application. We are collaborating with researchers from Auburn University, the University of Maryland, Raytheon, BAE Systems, the Department of Defense to develop a guide for solder selection for aerospace and defense applications. These researchers have backgrounds in materials engineering, mechanical engineering, industrial engineering, and electrical engineering, so many different skill sets are needed and you will see different perspectives. This project will require extensive review of the literature and performing materials characterization, processing, manufacturing, and reliability experiments. Student researchers will learn a wide range of materials and mechanical property, processing, and characterization techniques and will work closely with faculty and graduate students from Materials Engineering and Mechanical Engineering.
To apply to a SCALE SURF project, go to the SURF website: https://engineering.purdue.edu/Engr/Research/EURO/SURF/Research/Y2023
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Preferred majors:
• Materials Engineering
• Mechanical Engineering
Required Experience and Skills:
Desired experience:
• Experience with programming in Python, C/C++, and/or MATLAB.
• Enthusiasm for scientific research.
• Understanding of introductory materials science and engineering concepts.
Academic Years Eligible:
Rising juniors and seniors with the desired experience will be preferred, but rising sophomores are also eligible to apply.
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Processing and Characterization, Microelectronics
Preferred major(s):
- Materials Engineering
Desired experience:
• Experience with programming in Python, C/C++, and/or MATLAB.
• Enthusiasm for scientific research.
• Understanding of introductory materials science and engineering concepts.
School/Dept.:
Materials Engineering
Professor:
Carol
Handwerker
SCALE Heterogeneous Integration/ Advanced Packaging: Multi-Photon 3D-printed Nano Vertical Compliant Interconnects for sub-Micron Pitch
Description:
This project is one of several SCALE SURF research projects, and is restricted to US citizens. If you are interested in more than one SCALE SURF project, you can apply to all of them with one application. ** Be sure to address each project by name in your application. ** See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023.
Heterogeneous integration of different dielets (processor, memory, RF, etc.) has made rapid strides in the last decade driven by the development of three-dimensional (3D) integration, fan-out wafer-level packaging, and interposers. A key requirement of package scaling is the reduction of the I/O pitch, which requires elimination of solder and micro-solder bumps. Scaling of solder bumps below 40 µm pitch is challenging due to multiple issues, such as solder extrusion, bridging and intermetallic compound (IMC) formation. Therefore, micro and nano-Cu interconnects using Cu to Cu thermal compression bonding and hybrid bonding have been demonstrated for next generation heterogeneous integration. However, nano-Cu interconnects suffer from electromigration related failures at sub-micron pitch sizes. Here we propose Cu, Ag or cobalt composite with graphene or reduced graphene oxide for compliant and high conductivity interconnects. Graphene is a 2D array of sp2-bonded carbon atoms and is known to have extraordinary electrical and mechanical properties. The carrier mobility of graphene is 2.5 x 104 cm2V-1s-1 and the maximum current carrying capacity is up to 108 Acm-2, therefore, graphene-based materials show great potential for future interconnect technologies such as Cu-graphene or Co-graphene or Ag-graphene composites. SURF student will prepare Cu-graphene, Co-graphene, Ag-graphene composites and measure thermal conductivity using a TLM test structure. Multi-photon 3D printing will also be explored to define nanometer feature size. Future work will include effect of these composites on mechanical, thermal and electromigration properties.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Heterogeneous integration of different dielets (processor, memory, RF, etc.) has made rapid strides in the last decade driven by the development of three-dimensional (3D) integration, fan-out wafer-level packaging, and interposers. A key requirement of package scaling is the reduction of the I/O pitch, which requires elimination of solder and micro-solder bumps. Scaling of solder bumps below 40 µm pitch is challenging due to multiple issues, such as solder extrusion, bridging and intermetallic compound (IMC) formation. Therefore, micro and nano-Cu interconnects using Cu to Cu thermal compression bonding and hybrid bonding have been demonstrated for next generation heterogeneous integration. However, nano-Cu interconnects suffer from electromigration related failures at sub-micron pitch sizes. Here we propose Cu, Ag or cobalt composite with graphene or reduced graphene oxide for compliant and high conductivity interconnects. Graphene is a 2D array of sp2-bonded carbon atoms and is known to have extraordinary electrical and mechanical properties. The carrier mobility of graphene is 2.5 x 104 cm2V-1s-1 and the maximum current carrying capacity is up to 108 Acm-2, therefore, graphene-based materials show great potential for future interconnect technologies such as Cu-graphene or Co-graphene or Ag-graphene composites. SURF student will prepare Cu-graphene, Co-graphene, Ag-graphene composites and measure thermal conductivity using a TLM test structure. Multi-photon 3D printing will also be explored to define nanometer feature size. Future work will include effect of these composites on mechanical, thermal and electromigration properties.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Processing and Characterization, Microelectronics, Nanotechnology
Preferred major(s):
- Mechanical Engineering,
- Materials Engineering
- Chemical Engineering
Desired experience:
MSE230 or introductory materials course. Training will be provided on SEM and other tools needed.
School/Dept.:
School of Mechanical Engineering
Professor:
Shubhra
Bansal
SCALE Heterogeneous Integration/ Advanced Packaging: Next Generation Low Temperature Solders for Consumer and High Reliability Applications
Description:
This project is one of several SCALE SURF research projects, and is restricted to US citizens. If you are interested in more than one SCALE SURF project, you can apply to all of them with one application. ** Be sure to address each project by name in your application. ** See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023.
Low-melting point metals based on tin are used to connect semiconductor packages to circuit boards. The specific solder composition that is chosen for a product depends on the product's use conditions, for example, consider the differences in use conditions for a cell phone, an implanted pace maker, strapped onto a car engine, and in a satellite. While most solder alloys have melting points between 217 °C (high temperature Pb-free alloys) and 183 °C (Sn-Pb eutectic), a new generation of Sn-Bi solder alloys are being developed that have melting points around 139 °C to lower soldering processes in order to minimize warpage-induced asse mbly defects. This project explores the alloy design space for Sn-Bi alloys in terms of performance and manufacturing as a function of composition and application. We are collaborating in this research with a range of microelectronics companies, including Intel, Texas Instruments, Nvidia, AMD, and Macdermid Alpha. Student researchers will learn a wide range of materials and mechanical property, processing, and characterization techniques and will work closely with faculty and graduate students from Materials Engineering and Mechanical Engineering.
To apply to a SCALE SURF project, go to the SURF website: https://engineering.purdue.edu/Engr/Research/EURO/SURF/Research/Y2023
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Low-melting point metals based on tin are used to connect semiconductor packages to circuit boards. The specific solder composition that is chosen for a product depends on the product's use conditions, for example, consider the differences in use conditions for a cell phone, an implanted pace maker, strapped onto a car engine, and in a satellite. While most solder alloys have melting points between 217 °C (high temperature Pb-free alloys) and 183 °C (Sn-Pb eutectic), a new generation of Sn-Bi solder alloys are being developed that have melting points around 139 °C to lower soldering processes in order to minimize warpage-induced asse mbly defects. This project explores the alloy design space for Sn-Bi alloys in terms of performance and manufacturing as a function of composition and application. We are collaborating in this research with a range of microelectronics companies, including Intel, Texas Instruments, Nvidia, AMD, and Macdermid Alpha. Student researchers will learn a wide range of materials and mechanical property, processing, and characterization techniques and will work closely with faculty and graduate students from Materials Engineering and Mechanical Engineering.
To apply to a SCALE SURF project, go to the SURF website: https://engineering.purdue.edu/Engr/Research/EURO/SURF/Research/Y2023
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Processing and Characterization
Preferred major(s):
- Materials Engineering
- Mechanical Engineering
School/Dept.:
Materials Engineering
Professor:
Carol
Handwerker
SCALE Heterogeneous Integration/ Advanced Packaging: Reimagining Solder Joints Technology for Semiconductors by Using Dimensionality to Tailor Properties
Description:
This project is one of several SCALE SURF research projects, and is restricted to US citizens. If you are interested in more than one SCALE SURF project, you can apply to all of them with one application. ** Be sure to address each project by name in your application. ** See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023.
Semiconductor Research Corporation (SRC) identifies the need for solders with peak reflow temperature less than 140 ℃ for Si heterogeneous integration and high temperature solders for SiC heterogeneous integration. Sn-based solders have shown promise for low-temperature regime and Bi-based solders are promising for high temperature applications. Both these classes of solder materials have their own challenges. For fine pitch interconnects, conventional Sn-based solder materials suffer from drawbacks including die stress due to high reflow temperatures, intermetallic formation, Sn-whisker growth and electromigration. Bi-based solders suffer from wettability issues. Here we to propose to develop a disruptive approach to tailoring properties of solder materials by changing their structural dimensionality. For example, Melting point depression of 26.6 ℃ has been observed for SAC nanoparticles with an average diameter of 18 nm for extremely fine pitch 2-8 µm applications. However, the difficulty lies in the reflow process due to formation of oxide and thereby impeding the coalescence of molten core particles. Reducing fluxes and acidic treatments have proven to be promising for oxide removal, however, the acidity of solution can alter the particle size, morphology and package integrity. Our intent is to explore the effect of the number of atomic layers on solder properties, which can be translated into a commercial process, if successful. Precursor based solution processing can be used to process quantum dots, 1D, 2D structures of these solders that should conceptually result in suppression of melting temperature and reduction in Sn-whisker growth. In the proposed project we will study the effect of dimensionality on Sn-Ag-Cu, Sn-Bi, Sn-In low temperature and Bi-based high temperature solders. SURF student will develop proof-of-concept with commercially available Sn-Ag-Cu and Sn-Bi solder and use ion-milling to exfoliate monolayers of the material. The monolayers will be passivated with organic ligands and subsequently melting temperature will be measured using differential scanning calorimetry (DSC). We will collaborate with GE Global Research and University of Binghamton for development of Bi-based high temperature solders. Future work will include development of processing methods for dimensionally modified solders, integration, reliability studies, etc.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Semiconductor Research Corporation (SRC) identifies the need for solders with peak reflow temperature less than 140 ℃ for Si heterogeneous integration and high temperature solders for SiC heterogeneous integration. Sn-based solders have shown promise for low-temperature regime and Bi-based solders are promising for high temperature applications. Both these classes of solder materials have their own challenges. For fine pitch interconnects, conventional Sn-based solder materials suffer from drawbacks including die stress due to high reflow temperatures, intermetallic formation, Sn-whisker growth and electromigration. Bi-based solders suffer from wettability issues. Here we to propose to develop a disruptive approach to tailoring properties of solder materials by changing their structural dimensionality. For example, Melting point depression of 26.6 ℃ has been observed for SAC nanoparticles with an average diameter of 18 nm for extremely fine pitch 2-8 µm applications. However, the difficulty lies in the reflow process due to formation of oxide and thereby impeding the coalescence of molten core particles. Reducing fluxes and acidic treatments have proven to be promising for oxide removal, however, the acidity of solution can alter the particle size, morphology and package integrity. Our intent is to explore the effect of the number of atomic layers on solder properties, which can be translated into a commercial process, if successful. Precursor based solution processing can be used to process quantum dots, 1D, 2D structures of these solders that should conceptually result in suppression of melting temperature and reduction in Sn-whisker growth. In the proposed project we will study the effect of dimensionality on Sn-Ag-Cu, Sn-Bi, Sn-In low temperature and Bi-based high temperature solders. SURF student will develop proof-of-concept with commercially available Sn-Ag-Cu and Sn-Bi solder and use ion-milling to exfoliate monolayers of the material. The monolayers will be passivated with organic ligands and subsequently melting temperature will be measured using differential scanning calorimetry (DSC). We will collaborate with GE Global Research and University of Binghamton for development of Bi-based high temperature solders. Future work will include development of processing methods for dimensionally modified solders, integration, reliability studies, etc.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Processing and Characterization, Microelectronics
Preferred major(s):
- Mechanical Engineering
School/Dept.:
Mechanical Engineering
Professor:
Shubhra
Bansal
SCALE Heterogeneous Integration/ Advanced Packaging: Self-alignment Technology for 3D System Integration
Description:
This project is one of several SCALE SURF research projects. SCALE projects are restricted to students who are U.S. Citizens. By applying to this project, you can be considered for any of the SCALE projects with one application. See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023.
For the typical 3D integration scheme, die-to-wafer bonding is a key technology that can enable the stacking of different chips, such as logic, memory, or power devices. Compared with wafer-to-wafer bonding, it is challenging for die-to-wafer bonding to achieve high throughput while maintaining a high alignment accuracy. Researchers have been investigating different self-alignment technologies to improve the high-precision chip alignment accuracy for die-to-wafer bonding, such as Surface tension-driven with hydrophilic chip surfaces. In this topic, we will explore innovative self-alignment methods for advanced die-to-wafer bonding, enabling high throughput heterogeneous integration.
Reference: Fukushima, Takafumi, et al. "Self-assembly technologies with high-precision chip alignment and fine-pitch microbump bonding for advanced die-to-wafer 3D integration." 2011 IEEE 61st Electronic Components and Technology Conference (ECTC). IEEE, 2011.)
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
For the typical 3D integration scheme, die-to-wafer bonding is a key technology that can enable the stacking of different chips, such as logic, memory, or power devices. Compared with wafer-to-wafer bonding, it is challenging for die-to-wafer bonding to achieve high throughput while maintaining a high alignment accuracy. Researchers have been investigating different self-alignment technologies to improve the high-precision chip alignment accuracy for die-to-wafer bonding, such as Surface tension-driven with hydrophilic chip surfaces. In this topic, we will explore innovative self-alignment methods for advanced die-to-wafer bonding, enabling high throughput heterogeneous integration.
Reference: Fukushima, Takafumi, et al. "Self-assembly technologies with high-precision chip alignment and fine-pitch microbump bonding for advanced die-to-wafer 3D integration." 2011 IEEE 61st Electronic Components and Technology Conference (ECTC). IEEE, 2011.)
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Advanced Packaging, Composite Materials and Alloys, Fluid Modelling and Simulation, Heterogeneous Integration, Material Modeling and Simulation, Material Processing and Characterization, Microelectronics, Nanotechnology, Thermal Technology
Preferred major(s):
- Electrical Engineering
- Mechanical Engineering
- Materials Engineering
Desired experience:
1. Microelectronics, micro/nanotechnology courses
2. Clean room fabrication experience
3. Enthusiasm for material fabrication and characterizations
4. Familiar with SEM, TEM analysis
5. Fluid mechanics
Academic Years Eligible:
Rising juniors and seniors with the desired experience will be preferred, but rising sophomores are also eligible to apply.
School/Dept.:
ME
Professor:
Tiwei
Wei
More information: https://alphalab-purdue.org/
SCALE Radiation Hardening: Hybrid radiation shielding design and multi-objective optimization
Description:
This project is one of several SCALE SURF research projects. By applying to this project, you can be considered for any of the SCALE projects with one application. See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023. Please note that US citizenship is required to receive a SURF fellowship for this specific project.
Since there are multiple types of radiation in space environments, it is important to shield against these different sources. However, different materials have different levels of shielding against different radiation sources. In this project, we will devise a hybrid shielding material to protect against multiple sources of radiation (e.g., neutrons and protons.)
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Since there are multiple types of radiation in space environments, it is important to shield against these different sources. However, different materials have different levels of shielding against different radiation sources. In this project, we will devise a hybrid shielding material to protect against multiple sources of radiation (e.g., neutrons and protons.)
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Material Modeling and Simulation, Microelectronics, Radiation Hardening
Preferred major(s):
- Electrical Engineering
- Nuclear Engineering
- Computer Engineering
- Materials Engineering
Desired experience:
1. Experience with programming in Python, C/C++, and/or MATLAB.
2. Enthusiasm for scientific programming. Understanding of radiation transport and electromagnetism.
School/Dept.:
Electrical & Computer Engineering
Professor:
Peter
Bermel
More information: https://research.purdue.edu/scale
SCALE Radiation Hardening: Modeling radiation effects on semiconductor diodes
Description:
This project is one of several SCALE SURF research projects. By applying to this project, you can be considered for any of the SCALE projects with one application. See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023. Please note that US citizenship is required to receive a SURF fellowship for this specific project.
One of the important limits for device operation is the space-charge limit, which corresponds to the maximum allowed current before no more electrons cannot be emitted into a diode. This limit is given by the Mott-Gurney law in a trap-free solid or the Mark-Helfrich law for a solid with traps distributed exponentially in energy. Because ionizing radiation will create electrons and ions in a semiconductor device, this project will involve elucidating the effect of these charges on these limits. This may include using simulations to characterize behavior or adapting analytic theories to include ionizing radiation effects.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
One of the important limits for device operation is the space-charge limit, which corresponds to the maximum allowed current before no more electrons cannot be emitted into a diode. This limit is given by the Mott-Gurney law in a trap-free solid or the Mark-Helfrich law for a solid with traps distributed exponentially in energy. Because ionizing radiation will create electrons and ions in a semiconductor device, this project will involve elucidating the effect of these charges on these limits. This may include using simulations to characterize behavior or adapting analytic theories to include ionizing radiation effects.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Material Modeling and Simulation, Microelectronics, Nanotechnology, Radiation Hardening
Preferred major(s):
- Nuclear Engineering
- Electrical Engineering
- Materials Engineering
- Computer Engineering
Desired experience:
1. Experience with programming in Python, C/C++, and/or MATLAB.
2. Enthusiasm for scientific programming. Understanding of radiation transport and electromagnetism.
School/Dept.:
Nuclear Engineering
Professor:
Allen
Garner
More information: https://research.purdue.edu/scale
SCALE Radiation Hardening: Radiation Effects on Space Solar Cells for Planetary Missions
Description:
This project is one of several SCALE SURF research projects. By applying to this project, you can be considered for any of the SCALE projects with one application. See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023. Please note that US citizenship is required to receive a SURF fellowship for this specific project.
Solar cells are used as the primary power source for earth-orbiting satellites and as a primary/secondary source for various missions within the solar system. However, high energy particles from the sun, planetary magnetospheres, and the galaxy can impact solar cells in outer space. This can affect the performance and life expectancy of the space solar cell and associated power systems. Therefore, this study will analyze the performance of space solar cells, particularly the SolAero IMM-α, at various planetary orbits, such as Earth and Jupiter. This is done by using the Naval Research Lab Displacement Damage Dose (DDD) methodology by (1) obtaining particle fluence data and calculating the DDD of a specific orbit using SPENVIS; and (2) analyzing the solar cell’s performance/degradation with the given DDD.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Solar cells are used as the primary power source for earth-orbiting satellites and as a primary/secondary source for various missions within the solar system. However, high energy particles from the sun, planetary magnetospheres, and the galaxy can impact solar cells in outer space. This can affect the performance and life expectancy of the space solar cell and associated power systems. Therefore, this study will analyze the performance of space solar cells, particularly the SolAero IMM-α, at various planetary orbits, such as Earth and Jupiter. This is done by using the Naval Research Lab Displacement Damage Dose (DDD) methodology by (1) obtaining particle fluence data and calculating the DDD of a specific orbit using SPENVIS; and (2) analyzing the solar cell’s performance/degradation with the given DDD.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Material Modeling and Simulation, Microelectronics, Radiation Hardening
Preferred major(s):
- Electrical Engineering
- Nuclear Engineering
- Computer Engineering
- Materials Engineering
- Mechanical Engineering
Desired experience:
1. Experience with programming in Python, C/C++, and/or MATLAB.
2. Enthusiasm for scientific programming. Understanding of radiation transport and electromagnetism.
School/Dept.:
Electrical Engineering
Professor:
Peter
Bermel
More information: https://research.purdue.edu/scale
SCALE Radiation Hardening: Radiation-effects testing
Description:
This project is one of several SCALE SURF research projects. By applying to this project, you can be considered for any of the SCALE projects with one application. See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023. Please note that US citizenship is required to receive a SURF fellowship for this specific project.
Commercial off-the-shelf electronics are appealing for satellite applications because of their high capabilities (e.g., processing speed or memory). While they are generally tested for reliability for terrestrial applications, most manufacturers don’t have time to test or qualify them for space applications. In this project, we’ll select a novel commercial device to test, and develop a test procedure for testing. Last summer, our methodology was applied to a commercial magnetoresistive random access memory (MRAM) device, using a Gammacell chamber on campus. We will have the option to either extend that previous work or test a novel commercial device that has not been tested before.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Commercial off-the-shelf electronics are appealing for satellite applications because of their high capabilities (e.g., processing speed or memory). While they are generally tested for reliability for terrestrial applications, most manufacturers don’t have time to test or qualify them for space applications. In this project, we’ll select a novel commercial device to test, and develop a test procedure for testing. Last summer, our methodology was applied to a commercial magnetoresistive random access memory (MRAM) device, using a Gammacell chamber on campus. We will have the option to either extend that previous work or test a novel commercial device that has not been tested before.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Material Processing and Characterization, Microelectronics, Radiation Hardening
Preferred major(s):
- Electrical Engineering
- Computer Engineering
- Nuclear Engineering
- Materials Engineering
- Mechanical Engineering
Desired experience:
1. Experience with programming in Python, C/C++, and/or MATLAB
2. Enthusiasm for scientific programming. Understanding of radiation transport and electromagnetism.
School/Dept.:
Electrical & Computer Engineering
Professor:
Peter
Bermel
More information: https://research.purdue.edu/scale
SCALE Radiation Hardening: Space Radiation Environment Modeling
Description:
This project is one of several SCALE SURF research projects. By applying to this project, you can be considered for any of the SCALE projects with one application. See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023. Please note that US citizenship is required to receive a SURF fellowship for this specific project.
Outer space is known as a harsh environment, but not all regions in space are the same. For example, low earth satellite orbits benefit from charged particles being deflected by the Van Allen belts. In this project, we’ll focus on collecting data on the raw number of ionizing radiation particles associated with different space orbits, and then converting those values into aggregate measures such as total ionizing dose and single event effect rates, to help predict the lifetime of existing devices in these orbits.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Outer space is known as a harsh environment, but not all regions in space are the same. For example, low earth satellite orbits benefit from charged particles being deflected by the Van Allen belts. In this project, we’ll focus on collecting data on the raw number of ionizing radiation particles associated with different space orbits, and then converting those values into aggregate measures such as total ionizing dose and single event effect rates, to help predict the lifetime of existing devices in these orbits.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Material Modeling and Simulation
Preferred major(s):
- Electrical Engineering
- Computer Engineering
- Nuclear Engineering
- Materials Engineering
- Aeronautical and Astronautical Engineering
- Mechanical Engineering
Desired experience:
1. Experience with programming in Python, C/C++, and/or MATLAB.
2. Enthusiasm for scientific programming. Understanding of radiation transport and electromagnetism.
School/Dept.:
Electrical & Computer Engineering
Professor:
Peter
Bermel
More information: https://research.purdue.edu/scale
SCALE System-on-Chip: SoC design, verification, programming, and test
Description:
This project is one of several SCALE SURF research projects. By applying to this project, you can be considered for any of the SCALE projects with one application. See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023.
System on Chip Extension Technologies (SoCET) is a long running chip design team intended primarily for undergraduates to get experience in as many aspects of chip design, fabrication, and test as possible. The team is organized like a small chip design company with sub-teams for logic design, verification, chip-layout, analog design, printed circuit board (PCB) design, test, software, and special research projects in collaboration with research groups in ECE. Special projects include applications in hardware security and GPU design. Based on your interests and background, team leaders will work with you to assign you to an appropriate sub-team or special project. Because of the wide range of projects, the experience and skill requirements for SoCET are flexible. Almost any kind of background in circuit design, logic design, circuit simulation, computer architecture, and microcontroller programming will be useful in some part of the team. For more details on possible projects and sub-teams, see https://engineering.purdue.edu/SoC-Team.
In your application, please specify which of the SCALE technical areas you are most interested in. The
technical areas are:
• Radiation Hardening
• System-on-Chip (indicate this if you are interested in SoCET)
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects. For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
System on Chip Extension Technologies (SoCET) is a long running chip design team intended primarily for undergraduates to get experience in as many aspects of chip design, fabrication, and test as possible. The team is organized like a small chip design company with sub-teams for logic design, verification, chip-layout, analog design, printed circuit board (PCB) design, test, software, and special research projects in collaboration with research groups in ECE. Special projects include applications in hardware security and GPU design. Based on your interests and background, team leaders will work with you to assign you to an appropriate sub-team or special project. Because of the wide range of projects, the experience and skill requirements for SoCET are flexible. Almost any kind of background in circuit design, logic design, circuit simulation, computer architecture, and microcontroller programming will be useful in some part of the team. For more details on possible projects and sub-teams, see https://engineering.purdue.edu/SoC-Team.
In your application, please specify which of the SCALE technical areas you are most interested in. The
technical areas are:
• Radiation Hardening
• System-on-Chip (indicate this if you are interested in SoCET)
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects. For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Computer Architecture, Microelectronics, Nanotechnology, System-on-a-Chip, Other
Preferred major(s):
- Electrical Engineering
- Electrical Engineering Technology
- Computer Engineering
- Computer Engineering Technology
- Computer Science
Desired experience:
Almost any kind of background in circuit design, logic design, circuit simulation, computer architecture, and microcontroller programming will be useful on some part of this project.
School/Dept.:
Electrical and Computer Engineering
Professor:
Mark
Johnson
SCALE: Optimizing MXene properties
Description:
This project is one of several SCALE SURF research projects, and is restricted to US citizens. If you are interested in more than one SCALE SURF project, you can apply to all of them with one application. ** Be sure to address each project by name in your application. ** See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023.
Most of the materials we encounter in our daily lives are ‘bulk’ materials – they contain an enormous number of atoms in all three dimensions. However, if we instead consider materials with one dimension of only a few atoms in thickness, like graphene, we can achieve many unique physical and chemical properties unique from their bulk counterparts. For example, 2D magnetic materials have drawn significant attention because of their application in spintronics and quantum computing. One class of 2D materials with the potential to serve as the first room-temperature 2D magnets are MXenes, near atomically thin transition metal carbides or nitrides. For a magnetic material, the configuration can be ferromagnetic (FM) or antiferromagnetic (AFM) depending on the direction of spins of electrons. Using electronic structure calculations based on density functional theory (DFT), we can identify the magnetic configuration with lower energy. Further, the critical temperature, e.g. Curie temperature, is the temperature above which the material loses the spontaneous magnetization. For real-world applications, magnetic materials with a critical temperature that is higher than room temperature are desired. This project will combine DFT calculations to discover magnetic MXenes with high Curie temperatures.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Most of the materials we encounter in our daily lives are ‘bulk’ materials – they contain an enormous number of atoms in all three dimensions. However, if we instead consider materials with one dimension of only a few atoms in thickness, like graphene, we can achieve many unique physical and chemical properties unique from their bulk counterparts. For example, 2D magnetic materials have drawn significant attention because of their application in spintronics and quantum computing. One class of 2D materials with the potential to serve as the first room-temperature 2D magnets are MXenes, near atomically thin transition metal carbides or nitrides. For a magnetic material, the configuration can be ferromagnetic (FM) or antiferromagnetic (AFM) depending on the direction of spins of electrons. Using electronic structure calculations based on density functional theory (DFT), we can identify the magnetic configuration with lower energy. Further, the critical temperature, e.g. Curie temperature, is the temperature above which the material loses the spontaneous magnetization. For real-world applications, magnetic materials with a critical temperature that is higher than room temperature are desired. This project will combine DFT calculations to discover magnetic MXenes with high Curie temperatures.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Big Data/Machine Learning, Deep Learning, Material Modeling and Simulation, Microelectronics, Nanotechnology
Preferred major(s):
- No Major Restriction
Desired experience:
Introductory materials science or physics/chemistry of materials.
Introductory programming
School/Dept.:
MSE
Professor:
Alejandro
Strachan
More information: https://www.strachanlab.org
SCALE: Strain effect on properties of 2D MXene materials
Description:
This project is one of several SCALE SURF research projects, and is restricted to US citizens. If you are interested in more than one SCALE SURF project, you can apply to all of them with one application. ** Be sure to address each project by name in your application. ** See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023.
2D materials are a class of crystalline solids with a single layer only a few atoms thick. Because of their ultrathin body, 2D materials possess unique physical and chemical properties that are usually not seen in their bulk counterparts. Nowadays, 2D materials have been widely applied in solar cells, memory devices, chemical sensors. One emerging subset of the 2D materials class is MXenes, a new type of 2D material that has been successfully synthesized and studied in the last decade. MXenes are defined by a transition metal carbide or nitride with only atomically thin layers. The properties of a specific MXene are not always suitable for a given application, and one way to tune their properties is to apply strain. The mechanical strain has effects on the electronic and magnetic properties of materials because the strain changes the crystal structure of the materials. For example, the band gap of a material is an important property for electronic applications, and studies have shown that for some 2D materials, biaxial tensile strain decreases the band gap. Different strains, including biaxial, uniaxial, tensile, and compressive, also each have a different effect on the properties. In this project, the strain-tuned electronic and magnetic properties of novel MXenes will be studied. The physical mechanism behind the strain-induced properties will be characterized based on the change of crystal structures.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
2D materials are a class of crystalline solids with a single layer only a few atoms thick. Because of their ultrathin body, 2D materials possess unique physical and chemical properties that are usually not seen in their bulk counterparts. Nowadays, 2D materials have been widely applied in solar cells, memory devices, chemical sensors. One emerging subset of the 2D materials class is MXenes, a new type of 2D material that has been successfully synthesized and studied in the last decade. MXenes are defined by a transition metal carbide or nitride with only atomically thin layers. The properties of a specific MXene are not always suitable for a given application, and one way to tune their properties is to apply strain. The mechanical strain has effects on the electronic and magnetic properties of materials because the strain changes the crystal structure of the materials. For example, the band gap of a material is an important property for electronic applications, and studies have shown that for some 2D materials, biaxial tensile strain decreases the band gap. Different strains, including biaxial, uniaxial, tensile, and compressive, also each have a different effect on the properties. In this project, the strain-tuned electronic and magnetic properties of novel MXenes will be studied. The physical mechanism behind the strain-induced properties will be characterized based on the change of crystal structures.
In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.
For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.
Research categories:
Big Data/Machine Learning, Material Modeling and Simulation, Microelectronics, Nanotechnology
Preferred major(s):
Desired experience:
Introductory materials science or materials physics/chemistry
Introductory programing
School/Dept.:
MSE
Professor:
Alejandro
Strachan
More information: https://www.strachanlab.org
Scalable nanocarrier formulations to improve the bioavailability and efficacy of a potent prostate cancer drug
Description:
There is a critical need for therapies to reduce tumor burden and promote bone repair in patients suffering from bone-metastatic prostate cancer, which affects thousands of IN residents each year. This project focuses on the development and evaluation of a novel nanoparticle formulation of cabozantinib (Cabo), a potent kinase inhibitor chemotherapeutic drug. Cabo is a poorly water-soluble small molecule drug that cannot be dosed intravenously and exhibits low bioavailability when administered orally.
We hypothesize that formulating Cabo into a fast-dissolving organic nanoparticle will improve its dissolution kinetics and oral bioavailability. This in turn is expected to translate to higher efficacy against bone-metastatic prostate tumors in vivo. To test this, the student will design Cabo nanoparticle formulations using the Ristroph lab’s scalable Flash NanoPrecipitation technology and demonstrate improved dissolution kinetics in vitro compared to crystalline drug. This will be the focus of the SURF project. If successful, we will then evaluate the efficacy of the best-performing Cabo nanoformulation in vivo in Prof. Marxa Figueiredo's lab, which has expertise with Cabo and has developed a bone metastatic model of prostate cancer.
Ingrid will prepare nanoparticles containing Cabo using Flash NanoPrecipitation, following standard methods. She will assess nanoparticle formulations in vitro for diameter and polydispersity, surface charge, stability over time, and Cabo dissolution rate using dynamic light scattering and HPLC. Milestones and expected outcomes include (1) the development a nanoparticle formulation with >95% Cabo encapsulation efficiency, >50% drug loading, and stability for >1 week (ETM: 5 weeks); (2) the demonstration of >80% Cabo dissolution within 3h in simulated intestinal fluid (ETM: 5 weeks); and (3) the preparation of sufficient material to support the efficacy study in mice (out of scope for the SURF project; I plan to hire Ingrid as an undergraduate researcher in the fall to continue this project).
We hypothesize that formulating Cabo into a fast-dissolving organic nanoparticle will improve its dissolution kinetics and oral bioavailability. This in turn is expected to translate to higher efficacy against bone-metastatic prostate tumors in vivo. To test this, the student will design Cabo nanoparticle formulations using the Ristroph lab’s scalable Flash NanoPrecipitation technology and demonstrate improved dissolution kinetics in vitro compared to crystalline drug. This will be the focus of the SURF project. If successful, we will then evaluate the efficacy of the best-performing Cabo nanoformulation in vivo in Prof. Marxa Figueiredo's lab, which has expertise with Cabo and has developed a bone metastatic model of prostate cancer.
Ingrid will prepare nanoparticles containing Cabo using Flash NanoPrecipitation, following standard methods. She will assess nanoparticle formulations in vitro for diameter and polydispersity, surface charge, stability over time, and Cabo dissolution rate using dynamic light scattering and HPLC. Milestones and expected outcomes include (1) the development a nanoparticle formulation with >95% Cabo encapsulation efficiency, >50% drug loading, and stability for >1 week (ETM: 5 weeks); (2) the demonstration of >80% Cabo dissolution within 3h in simulated intestinal fluid (ETM: 5 weeks); and (3) the preparation of sufficient material to support the efficacy study in mice (out of scope for the SURF project; I plan to hire Ingrid as an undergraduate researcher in the fall to continue this project).
Research categories:
Medical Science and Technology, Nanotechnology
Preferred major(s):
- Chemical Engineering
- Biomedical Engineering
- Biological Engineering - multiple concentrations
- Biomedical Engineering
- Pharmacy
School/Dept.:
ABE
Professor:
Kurt
Ristroph
More information: https://www.ristrophlab.com/
Searching for bound top quark states in the CMS proton-proton collision data from the Large Hadron Collider
Description:
The research project is to hunt for new particles, i.e bound states of top quark, in the huge data set collected with the CMS detector at the Large Hadron Collider. Proton-Proton collision data is searched for any evidence or deviation from the standard model of particle physics. The candidate will use cutting-edge machine learning, artificial intelligence techniques to shed light on what holds the universe together. In particular with regard to the stability of the electroweak vacuum aka the fate of the universe. The research is able to shed light on what stabilizes the Higgs boson mass at 125 GeV and renders it not impacted by higher order loop effects involving top quarks. Without a solution the Higgs boson mass is driven to unphysically high mass values and hence, is in contradiction with the Higgs boson observation at low mass in 2012.
Candidates will be able to use a vast sample of top quark data, literally 100's of millions of top quark to search for any evidence of new particles. The Jung group even uses quantum computers to boost efficiency for reconstructing events and participants can have a choice in the direction and emphasis of the research project to the limits of what is possible. Students will contribute to the review process of analysis and publication and have a chance to be author for publication of technical/algorithm side or even for physics publications (provided contributions are above required threshold), provided sustained and multiple semester engagement.
Candidates will be able to use a vast sample of top quark data, literally 100's of millions of top quark to search for any evidence of new particles. The Jung group even uses quantum computers to boost efficiency for reconstructing events and participants can have a choice in the direction and emphasis of the research project to the limits of what is possible. Students will contribute to the review process of analysis and publication and have a chance to be author for publication of technical/algorithm side or even for physics publications (provided contributions are above required threshold), provided sustained and multiple semester engagement.
Research categories:
Big Data/Machine Learning
Desired experience:
python, c/c++ other programming languages are an advantage, course work on quantum mechanics and/or particle physics introductory level courses, modern physics are an advantage.
School/Dept.:
Physics and Astronomy
Professor:
Andy
Jung
More information: https://www.physics.purdue.edu/jung/
Solar Cells for Field Applications
Description:
Solar Cells for Field Applications
Research categories:
Other
Preferred major(s):
- No Major Restriction
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Peter
Bermel
Solution-phase chemistry to synthesize chalcogenide perovskites for photovoltaics applications
Description:
Chalcogenide Perovskites are an exciting class of semiconducting materials that may be useful in a variety of applications, including solar energy harvesting. These materials take an ABX3 composition where A is an alkaline earth metal (Ca, Sr, Ba), B is an early transition metal (Zr, Hf), and X is a chalcogen (S, Se). While preliminary work has shown that these materials have many interesting properties, the synthesis of these chalcogenide perovskites has proven to be very difficult, often requiring excessively high temperatures around 1000 C. Our group has recently made progress in developing lower-temperature methods (below 600 C) to make BaZrS3 and BaHfS3 using soluble molecules that contain bonds between the desired metal and chalcogen. However, this chemistry is relatively unexplored, and tuning the soluble molecules may enable other chalcogenide perovskites and related materials to be synthesized.
In this project, we will investigate the synthesis of new metal-chalcogen bonded molecules and investigate how changes in the structure of the molecules affect their solubility and decomposition. The student on this project will develop skills in chemical handling and synthesis, thin film fabrication, materials characterization, and laboratory safety. Specifically, they will get to work in gloveboxes and utilize techniques such as X-ray diffraction, Raman spectroscopy, and X-Ray fluorescence. Additionally, the student will learn how solution-based chemistry can be applied to the fabrication of solar cells and other semiconductor devices.
In this project, we will investigate the synthesis of new metal-chalcogen bonded molecules and investigate how changes in the structure of the molecules affect their solubility and decomposition. The student on this project will develop skills in chemical handling and synthesis, thin film fabrication, materials characterization, and laboratory safety. Specifically, they will get to work in gloveboxes and utilize techniques such as X-ray diffraction, Raman spectroscopy, and X-Ray fluorescence. Additionally, the student will learn how solution-based chemistry can be applied to the fabrication of solar cells and other semiconductor devices.
Research categories:
Energy and Environment, Material Processing and Characterization, Nanotechnology
Preferred major(s):
- Chemical Engineering
- Chemistry
- Materials Engineering
Desired experience:
General chemistry with lab
School/Dept.:
Chemical Engineering
Professor:
Rakesh
Agrawal
More information: https://engineering.purdue.edu/RARG/
Stem cell immunoengineering for targeted cancer therapy
Description:
Cancer is a major threat for humans worldwide, with over 18 million new cases and 9.6 million cancer-related deaths in 2019. Although most common cancer treatments include surgery, chemotherapy, and radiotherapy, unsatisfactory cure rates require new therapeutic approaches. Recently, adoptive cellular immunotherapies with chimeric antigen receptor (CAR) engineered T and natural killer (NK) cells have shown impressive clinical responses in patients with various blood and solid cancers. However, current clinical practices are limited by the need of large numbers of healthy immune cells, resistance to gene editing, lack of in vivo persistence, and a burdensome manufacturing strategy that requires donor cell extraction, modulation, expansion, and re-introduction per each patient. The ability to generate universally histocompatible and
genetically-enhanced immune cells from continuously renewable human pluripotent stem cell (hPSC) lines offers the potential to develop a true off-the-shelf cellular immunotherapy. While functional CAR-T and NK cells have been successfully derived from hPSCs, a significant gap remains in the scalability, time-consuming (5 or more weeks), purity and robustness of the differentiation methods due to the cumbersome use of serum, and/or feeder cells, which will incur potential risk for contamination and may cause batch-dependency in the treatment. This project thus aims to develop a novel, chemically-defined platform for robust production of CAR-T and CAR-NK cells from hPSCs. The students recruited will help to engineer stem cells with gene editing tools, differentiate stem cells into immune cells, and perform molecular and cellular assays to characterize the cells.
genetically-enhanced immune cells from continuously renewable human pluripotent stem cell (hPSC) lines offers the potential to develop a true off-the-shelf cellular immunotherapy. While functional CAR-T and NK cells have been successfully derived from hPSCs, a significant gap remains in the scalability, time-consuming (5 or more weeks), purity and robustness of the differentiation methods due to the cumbersome use of serum, and/or feeder cells, which will incur potential risk for contamination and may cause batch-dependency in the treatment. This project thus aims to develop a novel, chemically-defined platform for robust production of CAR-T and CAR-NK cells from hPSCs. The students recruited will help to engineer stem cells with gene editing tools, differentiate stem cells into immune cells, and perform molecular and cellular assays to characterize the cells.
Research categories:
Cellular Biology, Medical Science and Technology
Preferred major(s):
- No Major Restriction
- Chemical Engineering
- Biological Engineering - multiple concentrations
- Cell Molecular and Developmental Biology
- Biomedical Engineering
Desired experience:
Previous experience with cell culture and molecular biology is a bonus, but NOT required.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Xiaoping
Bao
More information: https://sites.google.com/view/xiaoping-bao/home
Structural Engineering for Blast Resistant Design
Description:
Today’s structures are highly engineered buildings and bridges capable of carrying everyday and extreme loads. In this project, students will get to work on understanding blast engineering design with a special focus on building materials like concrete and steel. Undergraduate researchers will work day-to-day alongside graduate students and permanent staff to create test plans, fabricate test specimens, and test large-scale structures to failure. Students will leave this summer with a greater understanding of engineering principles including structural dynamics, impact and blast loading, and composite behavior.
Research categories:
Composite Materials and Alloys, Energy and Environment, Engineering the Built Environment, Other
Preferred major(s):
- No Major Restriction
- Civil Engineering
- Mechanical Engineering
- Mechanical Engineering Technology
- Aeronautical and Astronautical Engineering
- Aeronautical Engineering Technology
- Construction Engineering
- Construction Management Technology
- Engineering (First Year)
- Materials Engineering
Desired experience:
Willing to work in a large-scale structural testing facility which may include some manual labor.
School/Dept.:
Civil Engineering
Professor:
Amit
Varma
More information: https://engineering.purdue.edu/~ahvarma/
Super-Resolution Optical Imaging with Single Photon Counting and Optomechanics with Nanostructured Membranes
Description:
Two projects are available. One involves the investigation of enhancing optical imaging resolution using single photon counting techniques. Conventional optical imaging has a hard limit on its spatial resolution, to about one half of the wavelength, and many situations can benefit from higher resolution. In addition, it is challenging to image through scattering media. By way of example, being able to sense with light deeper in the brain would be of enormous benefit in neuroscience. The statistics of photons emitted by or transmitted through an object contain valuable information about the object which could be used to enhance image resolution and possibly see through substantial background scatter. Experiments will be conducted using laser light and with a set of single photon avalanche detectors (SPADs) to measure photon correlations in time, over wavevector (direction), and between detectors in various imaging configurations. Results from these experiments will be used to assess the effectiveness of various techniques for enhancing spatial resolution in imaging applications. This work has a diverse set of potential applications including biological imaging, sensing defects in semiconductors, and imaging through fog. The other project relates to experimental work and the modeling of optical forces on structured membranes induced by a laser. The mechanical motion of a thin membrane deflected by laser light will be used to determine the membrane properties from experimental and simulated data. This will allow extraction of the mechanical material properties and more generally the validation of a theory for optomechanics that can then be used in design. The nascent field of optomechanics offers enormous impact scope, including remote actuation and propulsion, of importance in fields as diverse and molecular biology, communication, and transport. This project relates to attaining the underpinnings to move along such paths in engineering, as well as the basic physics of optical forces in material at small length scales.
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Composite Materials and Alloys, Deep Learning, Material Processing and Characterization, Medical Science and Technology, Nanotechnology
Preferred major(s):
- Electrical Engineering
- Mechanical Engineering
- Physics
- Biomedical Engineering
Desired experience:
Students with an interest in experimental or modeling work and some background in electromagnetics would be a good fit for this project. The undergraduate student will work with graduate students to perform experiments in an optics laboratory, modeling, data analysis using MATLAB or python, and review relevant literature.
School/Dept.:
Electrical Engineering
Professor:
Kevin
Webb
Sustainable Quench Oil Replacements for Austempering Salt Quenchants
Description:
Quench media are typically paraffinic oils, molten salts, or aqueous polymer-based fluids, depending on the quench speed and bath temperature needed. While beneficial, unfortunately, they have downsides that limit use. Paraffinic oils are petroleum derived which will become more problematic from a cost and regulatory perspective moving forward and generally do not have good high temperature stability. Salt baths work at high temperatures and are not petroleum-derived, but toxicity can be a concern. As always, corrosion can be problematic with any fluid. To solve these issues, others have researched natural oils as alternatives, such as vegetable oils from various sources. Unfortunately, oils are impure, heterogeneous, vary from source to source (and year to year), and most importantly, have residual double bonds and triglyceride esters that make them reactive. On the positive side, they are biobased, renewable, biodegradable, non-toxic and have relatively high flash points.
We propose to explore new quench oils specifically to replace salt baths in austempering applications. Salt baths pose sustainability and disposal issues, but austempering requires temperatures in excess of what most oils can bear. Ideally, we will identify quench oils with flash points up to 400 ºC, high heat capacities, high thermal conductivities, and that are non-corrosive. We will investigate natural oils and modern advanced oils (e.g. silicone, phosphate esters) as replacements using quench tests, thermal analysis and flash point measurement. This project is joint with Prof. Titus of MSE.
We propose to explore new quench oils specifically to replace salt baths in austempering applications. Salt baths pose sustainability and disposal issues, but austempering requires temperatures in excess of what most oils can bear. Ideally, we will identify quench oils with flash points up to 400 ºC, high heat capacities, high thermal conductivities, and that are non-corrosive. We will investigate natural oils and modern advanced oils (e.g. silicone, phosphate esters) as replacements using quench tests, thermal analysis and flash point measurement. This project is joint with Prof. Titus of MSE.
Research categories:
Ecology and Sustainability, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
none
School/Dept.:
MSE
Professor:
Jeffrey
Youngblood
Synthesis, processing, and characterization of next-generation sustainable polymers
Description:
Plastics are ubiquitous in many facets of our lives, and the plastics industry is the third-largest manufacturing sector in the United States. But as plastics production develops rapidly, the long-term environmental challenges are globally recognized. Chemically resistant plastic products have extremely long lifetimes before completely decomposing — a single-use coffee pod can last 500 years in a landfill. Plastic waste accumulation has led to pollution that affects land, waterways and oceans; organisms are being harmed by entanglement or ingestion.
Closed-loop circular utilization of plastics is of manifold significance, yet energy-intensive and poorly selective scission of the ubiquitous carbon-carbon (C-C) bonds in contemporary commercial polymers pose tremendous challenges to envisioned recycling and upcycling scenarios. Our group focuses on a unique topochemical approach for creating elongated C-C bonds with a bond length of 1.57~1.63 Å (in contrast to conventional bonds with a C-C bond length of ~1.54 Å) between repeating units in the solid state with decreased bond dissociation energies. These polymers with elongated and weakened C-C bonds exhibit rapid depolymerization within a desirable temperature range (e.g., 140~260 °C), while otherwise remaining remarkably stable under harsh conditions.
Students will get involved in the following research activities:
1. Synthesis of novel polymer single crystals via topochemical approach
2. Synthesis of polymers with elongated and weakened C-C bonds for circular utilization
3. Processing, characterization, and practical application of chemically recyclable (depolymerizable) polymer single crystals and polyolefin materials.
Closed-loop circular utilization of plastics is of manifold significance, yet energy-intensive and poorly selective scission of the ubiquitous carbon-carbon (C-C) bonds in contemporary commercial polymers pose tremendous challenges to envisioned recycling and upcycling scenarios. Our group focuses on a unique topochemical approach for creating elongated C-C bonds with a bond length of 1.57~1.63 Å (in contrast to conventional bonds with a C-C bond length of ~1.54 Å) between repeating units in the solid state with decreased bond dissociation energies. These polymers with elongated and weakened C-C bonds exhibit rapid depolymerization within a desirable temperature range (e.g., 140~260 °C), while otherwise remaining remarkably stable under harsh conditions.
Students will get involved in the following research activities:
1. Synthesis of novel polymer single crystals via topochemical approach
2. Synthesis of polymers with elongated and weakened C-C bonds for circular utilization
3. Processing, characterization, and practical application of chemically recyclable (depolymerizable) polymer single crystals and polyolefin materials.
Research categories:
Chemical Catalysis and Synthesis, Ecology and Sustainability, Energy and Environment, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Letian
Dou
More information: https://letiandougroup.com/
Synthetic data generation for flow phenomenon
Description:
Modern machine learning techniques, like deep learning, require a high amount of data for the training process. Acquiring a high amount of experimental data can be a resource-intensive task and can slow down deep learning workflows. For fluids mechanics problems, synthetic data can be used as a good substitute for experimental data as it can be generated in a way that follows the underlying physics of the problem by following the flow behavior. Additionally, the images can be generated to mimic the distribution of experimental data and hence minimize distribution shift. This project aims to develop a software module to generate synthetic particle image data that follows the physics of underlying flows and mimics experimental data for a low distribution shift.
Research categories:
Fluid Modelling and Simulation
Desired experience:
ME 308/ME 309: Fluid Mechanics
School/Dept.:
Mechanical Engineering
Professor:
Steven
Wereley
TEST 2023 Project: DO NOT APPLY
Description:
Words and stuff
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
all the things
School/Dept.:
Something here
Professor:
Kayla
Kobak
Tactile-based reactive control for robotic manipulation
Description:
The goal of this project is to develop perception, control, and planning algorithms for robotic manipulation for pick-and-place tasks. The robotic system includes a UR5, a robotiq gripper, a vision-based fingertip tactile sensor (i.e., GelSight sensor), as well as a depth camera (e.g., Kinect or RealSense). First, it is expected to develop computer vision algorithms to use the depth camera to identify interested objects in a given environment. Second, motion planning algorithms are expected to be developed for the UR5 to move the robotic arm from the home position to the location of the object that is estimated by the depth camera. Third, a gripping controller is expected to be developed for the Robotiq gripper, which will leverage the tactile feedback from the GelSight sensor for robust grasping of the deformable objects, and placing the objects in another location.
Research categories:
Fabrication and Robotics
Preferred major(s):
- No Major Restriction
Desired experience:
one or more training/experience from the following areas: robotics, robotic manipulation, motion planning, computer vision, tactile sensing, control
School/Dept.:
School of Industrial Engineering
Professor:
Yu
She
More information: www.purduemars.com
Thermally Responsive Smart Additives for PFAS-free Fire Fighting Foams.
Description:
The aim of this project is to develop FS-free FFF formulations that meet specifications by combining new siloxane-based surfactants with controlled release of additives. The objective here is to develop the chemistry and methodology to encapsulate foam formulation additives with “smart” temperature release capabilities with the goal of minimizing foam degradation. The lifespan of firefighting foams is typically increased via the addition of viscosifiers such as polysaccharides to reduce foam drainage, but the increase in viscosity can impede foam spreading. In this objective, we aim to solve these issues by encapsulating the viscosifiers into temperature releasing polymer matrices. We hypothesize that (hypothesis 1) viscosifiers can be successfully encapsulated in temperature-sensitive microcapsules and (hypothesis 2) the viscosifiers can be released during fire-fighting operations increasing foam viscosity and reducing foam degradation without impacting foam generation and spreading. This project is joint with Prof. Carlos Martinez and he will co-advise.
Research categories:
Ecology and Sustainability, Material Processing and Characterization
Preferred major(s):
- Materials Engineering
- Chemical Engineering
- Chemistry
Desired experience:
no experience necessary. Just thinking about about doing research as a career as a PhD.
School/Dept.:
School of Materials Engineering
Professor:
Jeffrey
Youngblood
More information: https://scholar.google.com/citations?user=qkkQBDsAAAAJ&hl=en
Toward Calibration of Cognitive Factors (Trust, Self-Confidence, Risk) for Enhancing Human Interaction with Automation
Description:
Automation is being applied to increasingly complex tasks in manufacturing, medical, military applications and more. There is a need for better human automation interaction to prevent the misuse, disuse and abuse of automation. Our main objective is to develop algorithms for cognitive control so that automated and autonomous systems can respond better to, and guide, human behavior such that task performance is maximized. During this SURF 2023 project, the researcher will help with developing experimental platforms involving human-automation interaction utilizing an online quadrotor simulator module. This may include (but is not limited to) control algorithm and heuristic design of automation assistance, development of human automation interaction contexts and tasks, and incorporating psychophysiological sensors for data collection. Previous and current experiments utilizing this and similar platforms have involved modeling trust, modeling self-confidence, modeling risk perception, and improving learning rates.
Research categories:
Big Data/Machine Learning, Human Factors, Learning and Evaluation
Preferred major(s):
- No Major Restriction
Desired experience:
Strong coding skills, including experience in HTML, Javascript, MATLAB, and/or Python.
School/Dept.:
School of Mechanical Engineering
Professor:
Neera
Jain
Trustworthy Re-use of Pre-Trained Neural Networks
Description:
Deep neural networks (DNNs) are widely used, from image recognition in autonomous vehicles to detecting anomalies in system logs. Training these networks incurs a huge carbon footprint. Reusing pre-trained neural networks (PTNNs) reduces this cost and improves engineering efficiency. However, little attention has yet been paid to improving the software engineering infrastructure that supports the trustworthiness of PTNNs. At present, PTNNs are shared across industry via model hubs: collections of PTNNs organized by problem domain and machine learning framework. These zoos imitate traditional software registries, such as NPM and Maven, whereby engineers share software packages. PTNNs are still in their infancy, and there are many unknowns regarding their trustworthy exchange between engineering teams.
Undergraduate student(s) will work with graduate students on projects related to analyzing PTNNs, developing tools to standardize them (e.g. ONNX), and developing tools to measure them.
Undergraduate student(s) will work with graduate students on projects related to analyzing PTNNs, developing tools to standardize them (e.g. ONNX), and developing tools to measure them.
Research categories:
Cybersecurity, Deep Learning
Preferred major(s):
- No Major Restriction
Desired experience:
Successful applicants should have most of the following:
Introductory coursework or equivalent experience in machine learning and deep learning. Strong programming skills, familiarity with Linux programming environment (e.g. you are comfortable on the terminal). Vague knowledge of cybersecurity (e.g. buffer overflows). Knowledge of web systems (you know what React and Flask are, you've used one of them before). Data analysis skills (e.g. with Pandas).
Successful applicants are likely EE, CompEng, or CS majors.
School/Dept.:
Electrical & Computer Engineering
Professor:
James
Davis
More information: https://davisjam.github.io
Ultrasound Contrast Agent Optimization for Cardiovascular Applications
Description:
While angiography is typically performed using imaging techniques such as computed tomography or MRI, ultrasound provides an alternative means for cardiovascular imaging and is the fastest medical imaging modality. Ultrasound contrast agents (UCSAs) enhance the signal in a region by introducing pockets of significantly different impedance relative to the surrounding matrix. Most commercially available contrast agents create these impedance pockets by encapsulating air [17-20]. These agents create a large echogenic signal [17-20] because air has both reduced density and significantly different propagation properties than water. UCSAs are typically made by encapsulating a hydrophobic gas in a lipid monolayer [17, 21]. They can also be created via emulsification of a high density, hydrophobic liquid. One limitation of the emulsion-based agents is that they have lower contrast than gas filled agents and typically do not provide sufficient contrast for use in cardiovascular applications. Dr. Solorio’s lab has focused on developing liquid emulsion contrast agents composed of a protein shell that becomes highly echogenic after a thermocycling process in which the emulsion is heated and then cooled. After thermal cycling, they provide sufficient signal to allow detection in circulation and have shown enhanced echogenecity for at least 12 hours. Due to the stable nature of the nanoemulsion, the echogenic particles can be modified with targeting ligands to accumulate in inflamed tissues. These USCAs can be designed to deliver therapeutics in response to an external stimulus such as focused low frequency ultrasound. The research objective is to apply the novel contrast agents to study vascular pathology. USCAs will be used to 1) detect superficial plaque abnormalities such as ulcerations, 2) measure neovascularization of plaques, and 3) detect aneurysms. Fellows will gain research skills related to 1) understanding basic principles of ultrasound, 2) developing an understanding of the current limitations of contrast agents, 3) learning how to apply physical theory to clinically relevant systems, and 4) developing an understanding of contrast agent design.
Research categories:
Cardiovascular Disease Research
Preferred major(s):
- Biomedical Engineering
School/Dept.:
Biomedical Engineering
Professor:
Luis
Solorio
More information: https://soloriolab.wixsite.com/tmet?_ga=2.132562766.1956494512.1672867963-318297247.1665070848
Understanding Lyman Alpha Emitting Galaxies with Spectroscopy
Description:
This project is aimed at understanding the selection of Lyman-alpha-emitting galaxies in the distant universe, selected from the ODIN survey. Using spectroscopic surveys, including HETDEX and DESI, we will validate the selection, and quantify contamination from lower-redshift line emitters and from active galactic nuclei. Additionally, the use of spectroscopy will allow us to confirm young forming clusters of galaxies in the distant universe.
Research categories:
Other
Preferred major(s):
- Physics
Desired experience:
basic coding skills, in particular, with Python
sophomore-to-junior level knowledge in astronomy and astrophysics
School/Dept.:
Physics and Astronomy
Professor:
Kyoung-Soo
Lee
Understanding air pollution using stable isotopes
Description:
Air pollution, particularly aerosols, is the main environmental cause of death worldwide so understanding sources and sinks of aerosols in the environment is important. Student working on this project will use analytical techniques such as ion chromatography and isotope mass spectrometry to analyze aerosol extracted from filters. This data will be used assess sources and chemistry of aerosols in polluted environemnets.
Research categories:
Environmental Characterization
Preferred major(s):
- Chemistry
- Environmental Health Sciences
- Computer Science
Desired experience:
basic lab skills, self motivation, and eagerness
School/Dept.:
EAPS
Professor:
Greg
Michalski
More information: https://www.eaps.purdue.edu/research/michalski/
Understanding worker preferences for decarbonized manufacturing job attributes
Description:
The project goal is to determine how manufacturing workers in the Midwest value different attributes of their jobs that may be impacted by a transition to decarbonized manufacturing. The undergraduate researcher would work with the research team to coordinate and process structured interviews with workers in the steel industry, assist with recruiting for a choice-based-conjoint survey, and conduct preliminary data analysis based on interview and survey data. Some overnight travel (Indiana, Ohio) may be required, and expenses would be covered by project funds.
Research categories:
Energy and Environment, Human Factors
Preferred major(s):
- No Major Restriction
Desired experience:
Prior experience conducting interviews or surveys is a plus, as is experience with Python.
School/Dept.:
Mechanical Engineering
Professor:
Rebecca
Ciez
Using Machine Learning to Discover Perovskite Photocatalysts
Description:
Synopsis: The goal of this project is to apply quantum mechanics-based density functional theory simulations and machine learning to design novel halide perovskites with targeted photovoltaic, surface, and adsorption behavior for improved photocatalytic performance.
Targeted Need: Challenges of environmental pollution, global energy shortage, and overreliance on fossil fuels can be addressed using photocatalysis, where solar energy is harnessed for chemical processes such as hydrogen production, degradation of pollutants, and CO2 reduction [1]. Many semiconductors have been used as photocatalysts based on suitable band edge positions relative to redox potentials, strong optical absorption, and desirable adsorption and desorption of chemical species; examples include TiO2, Ga2O3, C3N4, CdS, and ZnS [2]. However, many limitations exist owing to wider than desired band gaps, ineffectiveness of charge carriers, and formation of harmful defects, motivating the search for novel and improved materials. Cheap and high-performing photocatalysts can also help avoid the use of transition or precious metals such as Pt and Pd as catalysts [3]. The chemical space of potential semiconductor photocatalysts is massive and not conducive to brute-force experimentation or even computation, which necessitates the use of data-driven strategies combining large computational datasets and state-of-the-art machine learning [4], prior to experimental validation and discovery.
Opportunity: Metal halide perovskites (HaPs) have risen in prominence for solar and related optoelectronic applications, and are suggested as promising photocatalysts. Recent publications report the use of MAPbI3, MAPbBr3 (MA=methylammonium), CsPbI3, Cs2BiAgBr6, and other single/double inorganic/hybrid perovskites, either in bulk crystalline form, 2D variants, nanoclusters, or as part of heterostructures, for water splitting, CO2 reduction, and organic synthesis [1,2]. However, this field remains very much in its infancy—HaPs are desirable photovoltaic (PV) materials with extremely tunable properties, but an exhaustive study of band edges, surface energies, and adsorption behavior across a wide chemical space is missing. Using high-throughput density functional theory (HT-DFT) computations, our research group has developed an initial dataset of the stability, band gap, and optical absorption characteristics of ABX3 HaPs with mixing at A, B, or X sites using common elemental or molecular species [5]. This provides the starting point for exploring photocatalytic activity of HaPs as a function of composition, phase, and surface orientation, by combining HT-DFT with machine learning (ML). Since DFT computations are expensive and cannot be performed endlessly, ML models trained on DFT data can help predict optical, electronic, surface, and adsorption properties of millions of new perovskite compositions, to accelerate by several orders of magnitude the screening of novel HaPs with a suitable combination of properties for catalyzing reactions.
Objectives: In this project, a HT-DFT+ML prediction, screening, and design approach will be applied to discover novel HaP compositions that display desired stability, optical absorption, surface stability, and activity towards species, for next-generation photocatalysis of technologically-important chemical processes, including CO2 reduction, H2 and O2 evolution (water splitting), and synthesis of various hydrocarbons. Specific objectives include: (i) using the existing DFT dataset of HaP crystal structures to build surface slabs, calculate surface energies, and adsorption energies of various molecules on stable surfaces, (ii) unique encoding of each material (descriptors) in terms of structure, composition, surface atoms, adsorbing species, etc. [4], and (iii) training of ML models based on regression techniques such as random forests and neural networks, ensuring rigorous optimization of hyperparameters, training data size, input dimensions, and applicability towards any new data point.
Role of Student Researcher: Using our available codes, software, and computing resources, students can quickly start running and analyzing simulations of photocatalytic properties. A variety of existing schemes can be applied and tested for numerical representation/description of materials and property prediction, such as using graph convolutional neural networks (GCNNs) for automatic crystal structure representation, which our group has good experience with. Student will carry out DFT and ML tasks under the guidance of a graduate student and the professor, and will be given the opportunity to lead one or two potentially high-impact journal publications. Given the prior work that has gone into this project, chances of success are very high, and future prospects will be plenty.
References
1. J. Yuan et al., Nanoscale, 13, 10281 (2021).
2. K. Ren et al., Journal of Materials Chemistry A, 10, 407 (2022).
3. Z. Luo et al., Nature Communications, 11, 4091 (2020).
4. J. Schmidt et al., npj Computational Materials, 5, 83 (2019).
5. A. Mannodi-Kanakkithodi et al., Energy and Environmental Science, 15, 1930-1949 (2022).
Targeted Need: Challenges of environmental pollution, global energy shortage, and overreliance on fossil fuels can be addressed using photocatalysis, where solar energy is harnessed for chemical processes such as hydrogen production, degradation of pollutants, and CO2 reduction [1]. Many semiconductors have been used as photocatalysts based on suitable band edge positions relative to redox potentials, strong optical absorption, and desirable adsorption and desorption of chemical species; examples include TiO2, Ga2O3, C3N4, CdS, and ZnS [2]. However, many limitations exist owing to wider than desired band gaps, ineffectiveness of charge carriers, and formation of harmful defects, motivating the search for novel and improved materials. Cheap and high-performing photocatalysts can also help avoid the use of transition or precious metals such as Pt and Pd as catalysts [3]. The chemical space of potential semiconductor photocatalysts is massive and not conducive to brute-force experimentation or even computation, which necessitates the use of data-driven strategies combining large computational datasets and state-of-the-art machine learning [4], prior to experimental validation and discovery.
Opportunity: Metal halide perovskites (HaPs) have risen in prominence for solar and related optoelectronic applications, and are suggested as promising photocatalysts. Recent publications report the use of MAPbI3, MAPbBr3 (MA=methylammonium), CsPbI3, Cs2BiAgBr6, and other single/double inorganic/hybrid perovskites, either in bulk crystalline form, 2D variants, nanoclusters, or as part of heterostructures, for water splitting, CO2 reduction, and organic synthesis [1,2]. However, this field remains very much in its infancy—HaPs are desirable photovoltaic (PV) materials with extremely tunable properties, but an exhaustive study of band edges, surface energies, and adsorption behavior across a wide chemical space is missing. Using high-throughput density functional theory (HT-DFT) computations, our research group has developed an initial dataset of the stability, band gap, and optical absorption characteristics of ABX3 HaPs with mixing at A, B, or X sites using common elemental or molecular species [5]. This provides the starting point for exploring photocatalytic activity of HaPs as a function of composition, phase, and surface orientation, by combining HT-DFT with machine learning (ML). Since DFT computations are expensive and cannot be performed endlessly, ML models trained on DFT data can help predict optical, electronic, surface, and adsorption properties of millions of new perovskite compositions, to accelerate by several orders of magnitude the screening of novel HaPs with a suitable combination of properties for catalyzing reactions.
Objectives: In this project, a HT-DFT+ML prediction, screening, and design approach will be applied to discover novel HaP compositions that display desired stability, optical absorption, surface stability, and activity towards species, for next-generation photocatalysis of technologically-important chemical processes, including CO2 reduction, H2 and O2 evolution (water splitting), and synthesis of various hydrocarbons. Specific objectives include: (i) using the existing DFT dataset of HaP crystal structures to build surface slabs, calculate surface energies, and adsorption energies of various molecules on stable surfaces, (ii) unique encoding of each material (descriptors) in terms of structure, composition, surface atoms, adsorbing species, etc. [4], and (iii) training of ML models based on regression techniques such as random forests and neural networks, ensuring rigorous optimization of hyperparameters, training data size, input dimensions, and applicability towards any new data point.
Role of Student Researcher: Using our available codes, software, and computing resources, students can quickly start running and analyzing simulations of photocatalytic properties. A variety of existing schemes can be applied and tested for numerical representation/description of materials and property prediction, such as using graph convolutional neural networks (GCNNs) for automatic crystal structure representation, which our group has good experience with. Student will carry out DFT and ML tasks under the guidance of a graduate student and the professor, and will be given the opportunity to lead one or two potentially high-impact journal publications. Given the prior work that has gone into this project, chances of success are very high, and future prospects will be plenty.
References
1. J. Yuan et al., Nanoscale, 13, 10281 (2021).
2. K. Ren et al., Journal of Materials Chemistry A, 10, 407 (2022).
3. Z. Luo et al., Nature Communications, 11, 4091 (2020).
4. J. Schmidt et al., npj Computational Materials, 5, 83 (2019).
5. A. Mannodi-Kanakkithodi et al., Energy and Environmental Science, 15, 1930-1949 (2022).
Research categories:
Big Data/Machine Learning, Chemical Catalysis and Synthesis, Energy and Environment, Material Modeling and Simulation
Preferred major(s):
- No Major Restriction
Desired experience:
Any experience with coding and/or data science will be useful, but not necessary. If student has taken courses on fundamentals of materials science, that will be helpful.
School/Dept.:
Materials Engineering
Professor:
Arun Kumar
Mannodi Kanakkithodi
More information: https://www.mannodigroup.com/
Using network science for precision learning intervention
Description:
The goal of this project is to develop precision learning intervention technology that leverages semantic network science to support early language learning and early intervention for developmental language disorder (DLD). DLD affects approximately 7% of the population, and results in lifelong risks for poor biomedical, educational, and professional outcomes, leading to tremendous costs to individuals and society. Our group seeks to combine recent theoretical and technical advances to develop methods for early identification and intervention of this common, yet understudied condition. Student will participate in coding / development of automated tools that tune early language learning targets according to the knowledge of the learner and will help pilot and assess efficacy of different intervention approaches. Student will work with senior members of the lab (postdocs and lab manager) to develop and acquire data to support the submission of a larger grant application in the Fall.
Research categories:
Big Data/Machine Learning, Medical Science and Technology, Other
Preferred major(s):
- No Major Restriction
Desired experience:
Preferred qualifications include: proficiency in R and/or Python, familiarity with Gitlab, exposure to or interest in learning about network science, and an interest in using remote technology to create engaging and effective early learning interventions in children under the age of 5.
School/Dept.:
SLHS
Professor:
Arielle
Borovsky
Vaginal Microbiome Regulation of Progesterone Signaling
Description:
The human Microbiome is a critical regulator of health and disease. Vaginal microbiome dysfunction has been implicated in several female reproductive tract conditions, but a precise understanding of the mechanisms by which the vaginal microbiome regulates human health are poorly understood. The objective of this project is to analyze human Microbiome data from 400 women to identify microbes, metabolites, and bacterial functions that regulate the expression of the progesterone receptor.
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging
Preferred major(s):
- No Major Restriction
Desired experience:
Statistics, Modeling topics, Cellular biology
School/Dept.:
Weldon School of BME
Professor:
Douglas
Brubaker
Vectorization for secure multiparty computation
Description:
This project will investigate the problem of vectorization in secure multiparty computation (MPC). MPC allows users to compute over encrypted data for security reasons. Unfortunately, this kind of computation is slow. As a result, we are investigating how to speed up MPC programs through compiler optimizations like vectorization. A student working on this project will help implement and benchmark new optimizations. They will gain skills in cryptography, programming languages, and compilers.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
Programming skills
Interest in learning about compilers and programming languages
School/Dept.:
Electrical and Computer Engineering
Professor:
Milind
Kulkarni