3D printing of DNA origami laden hydrogels 

The goal of this research is to develop distributed manufacturing strategies for robust mRNA-containing biomaterials. Our approach is to use yeast such as S. cerevisiae to produce large volumes of mRNA and oligo DNA sequences with unprecedented accuracy and scalability. The DNA strands will form the custom-designed nanocage via self-assembly which will encapsulate mRNA. The DNA architectures will be programmed for on-demand mRNA release and 3D printed into a hydrogel formulation for stable storage and administration. This specific project will focus on the printing of DNA origami ladened hydrogels and study the impact of printing parameters on the resulting geometry and functionality of the overall material system.

AAMP UP- Adhesion of Printed Energetic Materials  

This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.

The project is run by Dr. Stephen Beaudoin and his team. Additively manufactured energetic materials do not adhere to themselves and casings with sufficient strength to survive gun launch. This project is focused on assessing the properties of the energetic composites that dictate how strongly the composites adhere to themselves and to their casings. The measurements will be made by cutting the composites and measuring the force required to initiate and propagate a crack, and also by using atomic force microscopy to measure directly the adhesion between energetic particles and binders and casings.

More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=11574

AAMP UP- Effect of the Microstructure on the Response of Energetic Materials 

This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.

The project is run by Dr. Marisol Koslowski and her team. In this project we will quantify the effect of microstructure on the detonation of HMX and RDX. The student will collect experimental data from literature and will work in collaboration with a PhD student to generate geometries that will be used in detonation simulations.

Students must be familiar with Python.

More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=29264

AAMP UP- In-situ Diagnosis of Additive Manufacturing with 3D Vision Sensor 

This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.

The project is run by Dr. Song Zhang and his team. The research 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.

More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=117610

AAMP UP- Sample Heating using Infrared Laser and Optics 

This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.

The project is run by Dr. Wayne Chen and his team. Mechanical properties are important metrics that provide insight for different engineering applications ranging from chemical bonding type on an atomic scale to macroscale design applications. However, research shows that mechanical properties can change as a function of strain rate (impact velocity) and temperature. Therefore, it is necessary to test materials and gather properties while replicating the environment they will endure in application to best inform researchers and engineers in the material design process. A Kolsky bar apparatus is used to perform mechanical testing on materials at high strain rates. This experimental technique has been used for the last ~50 years and has resulted in many materials characterization papers. Missing from the literature is temperature dependence of mechanical properties at high strain rates. We would like a student interested in lasers and optics to design and build an infrared laser device that will evenly heat a polymer composite sample to a specified temperature. The device must attach to the Kolsky bar apparatus and be both safe and efficient. This will allow for coupled temperature and strain rate mechanical experiments and extrapolation of the temperature effects of different materials.

An understanding of laser and optics would be beneficial but is not required.

More information: https://engineering.purdue.edu/AAE/people/ptProfile?resource_id=1261

AAMP-UP: Additive Manufacturing 

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.

More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=34218

Advanced Vehicle Automation and Human-Subject Experimentation  

Vehicle automation is developing at a rapid rate worldwide. While fully autonomous vehicles will be pervasive on the roadway for the next several years, many research initiatives are currently underway to understand and design approaches that will make this technology a future reality. This work ranges from the development of sensors and controls algorithms, to schemes for networks and connectivity, to the creation of in-vehicle driver interfaces. Here, one component that is key to the effective design of next-generation autonomous driving systems is the human driver and, thus studying human-vehicle interactions and defining driver’s roles/tasks will be important.

The goal of this project is to describe and measure the ways in which a person interacts with advanced vehicle automation. Students will assist with multiple activities and will learn a combination of the following: how to a) develop/code advanced driving simulation scenarios, b) collect driving performance data, c) analyze driver and performance data (using methods via software packages), and d) write technical reports and/or publications. Students may also gain experience collecting and analyzing complementary physiological measures, such as eye movement data, brain activity, skin conductance, and heart rate. The students will work closely with graduate student mentors to enhance learning.

More information: https://engineering.purdue.edu/NHanCE

Anomaly Detection in Extrusion Based Additive Manufacturing 

This project is a part of Prof. Monique McClain's team. Cheap commercial Fused Filament Fabrication 3D printers do not currently have the ability to sense when a print error is occurring (e.g. delimination, overextrusion, etc.). If an operator is not monitoring the print or has limited experience, then this will lead to the creation of defective parts and wasted material, time, and effort. In order to move towards automatic on-the-fly correction during a print, it is important to be able to measure and classify critical 3D printing defects. By using an existing 3D printer with integrated sensors, the research assistant will have to design and conduct printing experiments that can allow us to distinguish normal (good) operation from abnormal (bad) operation. Then, machine learning/statistical process control algorithms can be applied to the carefully collected data in order to test the accuracy of the algorithms in detecting such errors. Ultimately, the research assistant will gain experience in additive manufacturing, design of experiments, and data analysis from this project.

Bone Fracture and Microscale Deformation Processes 

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.

More information: https://engineering.purdue.edu/MYMECH

Bone Fracture and Toughness Modification 

This SURF research project seems to engage a student in the study of fracture of bone. In particular we seek to change the strength and toughness of bone through a pharmacological treatment. A project participant would use pig or cow bone, modifiy such bone with the pharmacological treatment and conduct mechanical property measurements on said bone.

More information: https://engineering.purdue.edu/MYMECH

Data Driven Modeling of Electric Vehicle Impacts on Traffic Safety 

As the Biden administration recently announced a national target for electric vehicle (EV) sales, more and more EVs will be on road in the future. Meanwhile, there will be an increasing possibility of traffic crashes between EVs and Internal Combustion Engine (ICE) vehicles or between EVs and pedestrians/cyclists. However, we have a limited understanding of how EVs will influence traffic safety, especially at road intersections. This study will leverage affluent historical traffic crash data (including driver demographic information, driver behavior, and traffic conditions) in Indiana and conduct data-driven modeling to uncover what factors are associated with crashes involving EVs. In specific, this study will focus on crashes on all interstate and state highways in Indiana. The expected outcome will lead to policy recommendations on developing EV safety regulations, improving vehicle safety features and highway design in the future.

More information: https://engineering.purdue.edu/ASPIRE; https://engineering.purdue.edu/STSRG

Decisions for handling contaminated personal effects and plumbing after drinking water contamination 

Chemical spills and backflow incidents are common threats to drinking water distribution and plumbing systems. Sometimes free product and drinking water with dissolved contaminants can travel through this infrastructure and reach building faucets. When this occurs health officials, system owners, and infrastructure owners rapidly seek information about whether individual constituents became sequestered in certain parts of the systems and how best to remove them. Plastics are an important concern because many are easily permeated by organic compounds which prompts them to leach chemicals into clean water making it unsafe.

In response to drinking water contamination incidents over the past 20 years and requests from health departments and households affected, this project will examine the fate of fuel chemicals in contact with plumbing materials (i.e., pipes, gaskets) and plastic personal effect materials (i.e., baby bottles, plates, cups, etc.). Diesel, gasoline and crude oil are being considered. The student will conduct the contamination experiments, collect water samples and analyze them using state-of-the-art instrumentation. The student will analyze, interpret, and report the information with advisement of one graduate research assistant and two faculty who respond to these types of water contamination incidents.

Other questions that may be explored include the chlorination of the fuel components and formation of disinfectant byproducts, mechanical integrity impacts on the plastic materials, chemical transformations of the leached products. This work directly supports emergency response and recovery activities of the Center for Plumbing Safety.

More information: www.PlumbingSafety.org

Design and Control of Hybrid Thermal Management Systems 

Thermal management systems are used in a wide range of systems primarily for electronics cooling, and are becoming increasingly critical for aircraft as air vehicles become increasingly electrified or even hybridized. However, designing these systems is becoming increasingly challenging because the heat loads that they need to manage vary frequently in duration and magnitude. A "hybrid" thermal management system (TMS) is one that also includes a thermal battery (thermal energy storage device) to improve the system's ability to respond quickly to unexpected heat loads. These systems are similar in nature to hybrid electric vehicles that balance the use of the engine and a battery to achieve a common objective.

Designing a thermal energy storage (TES) device that has a large enough capacity, can absorb heat quickly, and is lightweight is challenging because it needs to perform well under many different load conditions, including when the heat loads are random. Performance metrics need to be simple enough that they can be evaluated by iterative optimization algorithms while capturing the complexity of the design requirements. In this project, the student(s) will design a TES device using optimization algorithms to find the best dimensions and test it in simulation against previously-designed TES devices. They will also support experimental work related to ongoing research in the area of design and control of these complex thermal systems.

More information: https://engineering.purdue.edu/JainResearchLab/

EMBRIO Projects 

Multiple projects hosted under EMBRIO

Energetic Particle Adhesion via enhanced centrifuge method 

This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.

The project is run by Dr. Stephen Beaudoin and his team. The research will use tools from data science and machine learning to develop predictive models for the performance of energetic materials. Students will learn about neural networks, deep learning, and the chemistry and physics of energetic materials.

More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=11574

Field Engineering of Quantum Memories 

The goal of this project is to develop a quantum memory using a crystal that can store quantum optical information. Such quantum memory will be essential for developing the future quantum networks where storage of optical entanglement is key to long-distance secure communication. The quantum memory operates below 4K temperature and it requires field engineering to control optical information. Students will be designing and implementing electronic circuit and electrodes around the crystal to achieve high frequency , high voltage control of the field around the crystal used as quantum memory. This is an experimental project in Prof Hosseini Lab in the Birck Nanotechnology Center at Purdue Discovery Park.

Forecasting the 2022 U.S. Elections using Mathematical Modeling 

Election forecasting involves polling likely voters, weighting polling data, combining it with other information (e.g., how the economy is doing), accounting for uncertainty, and communicating forecasts to the public. In this project, we will use mathematical modeling to produce forecasts of the upcoming 2022 U.S. midterm elections, and we will build a website to post our election forecasts.

More information: https://modelingelectiondynamics.gitlab.io/2020-forecasts/index.html

Friction and Wear Study of Carbon Carbon Disk Brakes 

The objective of this investigation is to measure friction and wear of carbon carbon disk brakes under ambient and high temperature applications. The objectives will be achieved by learning to use a disk brake apparatus to measure friction and wear. The Carbon Carbon Disk Brake (CCDB) is turn key and easily operated using computer controls.
The candidate involved will learn how carbon disks are prepared and learn to use this state of the art rig to collect a set of data for publication.

Functional Near-InfraRed Spectroscopy (fNIRS) Testbed Development for Studying Human Interaction with Autonomous Systems 

Bio or physiological sensors have become ubiquitous, and this additional sensing capability is being integrated into clothing and other devices (e.g. smart watches) that we wear to constantly provide us with feedback about our health. Physiological sensors can also be used to infer mental processes occurring in the brain; this is called "psychophysiological" sensing. In our lab we are studying the use of various types of psychophysiological measurements to infer or predict human decision-making and other cognitive factors (such as their trust) during interactions with machines, robots, and other autonomous systems. This type of research is important for improving the safety and performance of autonomous systems designed to interact with or assist humans, such as autonomous vehicles, nurse robots, or surgical robots used by physicians. We are looking for a student to set up a new sensor, called functional Near-InfraRed Spectroscopy (fNIRS), for use in our lab and to test it against some prior work we've done with similar sensors to study human trust in automation.

More information: https://engineering.purdue.edu/JainResearchLab/

Heterogeneous Integration/Advanced packaging 

The rapid increase in chip performance associated with Moore’s law has also raised interest and expectations around creating packaging devices with improved size, weight, and power. To keep sizes manageable while improving functionality, complex packaged electronics like iPhones require similar components to be compressed together horizontally and vertically, and combined with dissimilar components providing complementary functions. Significant challenges in heterogeneous integration to be addressed in research include maintaining the reliability of connections such as solder bumps, managing thermal cycling, and limiting damage from mechanical stress that can cause failures.

Identifying and reducing health and environmental impacts of plastic used to repair buried pipes 

Drinking water and sewer pipes are decaying across the nation, and inexpensive methods for repairing these assets are being increasingly embraced. One method called cured-in-place-pipe (CIPP) involves workers chemically manufacturing a new plastic pipe inside an existing damaged pipe. This is the least expensive pipe repair method and, as such, is preferred by utilities and municipalities. The practice is often conducted outdoors and industry ‘best’ practice involves discharging the plastic manufacturing waste into the environment and nearby pipelines. Under some conditions, this waste finds its way into public areas and buildings prompted illnesses and environmental damage. Another consequence can be direct leaching of unreacted chemicals into water or volatilization of chemicals from the new plastic into air.

This project will involve the student working with a graduate student as well as leading experts on plastics manufacturing, chemistry, public health, civil/environmental engineering, and communications. The student will learn plastic manufacturing methods, environmental sampling and analysis methods, and participate in the process of reducing human health and environmental risks of the practice. To complete this work, the student will learn and apply infrastructure, environmental, and public health principles.

More information: More information about the project: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2129166&HistoricalAwards=false; More information about the topic: www.CIPPSafety.org

Imaging and designs of the bio-inspired tissue-engineered matrix. 

This project will be a nexus of the design, imaging, and manufacturing science and engineering of tissue matrix by cellular engineering. The project will engage students in high-resolution imaging, building a 2D and 3D digital design construct of the cellular matrix, and application of AR/VR for the human-matrix interface. Students will learn convergence of imaging, design, tissue engineering, and visualization. This project will be conducted in the College of Engineering and the College of Agriculture.

More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=232598

Infield study of long term virtual and augmented reality-based training for vocational skilling 

Welding is a skill that requires manual dexterity, adept psychomotor skills, and attention to numerous details of the process. Virtual Reality (VR)-based simulators for welding have gained popularity in recent decades and have been integrated with in-person training methods to provide hands-on practice to learners for improving the necessary psychomotor skills. Previous research studies have shown that respondents agree with using VR-based welding simulators as a tool to develop basic welding skills in new trainees. Using a VR welding simulator, the trainee’s understanding was much clearer when doing the welding process, and welding skills also developed. In addition, the simulator helps the trainee redo the exercises without considering the wastage of workpiece material and access to other equipment. Considering these advantages offered by VR-based training methods, our research would focus on the systematic study to explore and evaluate the usage of the technology to facilitate user experience and the development of psychomotor skills required for welding. The student researcher would be needed to help conduct experiments with field subjects. He/she would collect data during the investigations and later help perform statistical analysis of the data. This work will eventually lead to submitting a paper to a top-tier ACM conference.

Interpreting paleoclimate data from Antarctica using numerical models 

Scientists study the Earth's past climate in order to understand how the climate will respond to ongoing global change in the future. One of the best analogs for future climate might the period that occurred approximately 3 million years ago, during an interval known as the mid-Pliocene Warm Period. During this period, the concentration of carbon dioxide in the atmosphere was similar to today's and sea level was 15 or more meters higher, due primarily to warming and consequent ice sheet melting in polar regions. However, the temperatures in polar regions during the mid-Pliocene Warm Period are not well determined, in part because we do not have records like ice cores that extend this far back in time. We are studying surface temperatures in Antarctica during the mid-Pliocene Warm Period using a new type of climate proxy, known as cosmogenic noble gas (CNG) paleothermometry. In this project, the SURF student will be involved with improving numerical models that we use to interpret CNG data.

More information: https://www.purdue.edu/science/geochronology/thermochron/

On a Microgrid for Renewable Energy Systems and Water Security 

NA

Operation and characterization of SPT-100 Hall thruster  

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 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.

More information: https://engineering.purdue.edu/EPPL

Reachability Analysis for Complex Dynamic Systems 

Numerical simulations of complex dynamical systems are difficult to evaluate in realistic scenarios due to uncertainty in the environment. To understand properties of these systems, such as performance and safety, the dynamic models are simulated for sets of possible interactions – a method known as Reachability Analysis. We are looking for a student interested in numerical methods and learning about these tools so that they can apply them to existing examples and prepare for a friendly competition of related tools that will occur later in 2022. Students with an interest in dynamics or control systems are encouraged to apply.

More information: https://engineering.purdue.edu/JainResearchLab/

SURF 2022 Colombia Purdue Partnership Project 2 

NA

Sister2Sister 

Multiple projects hosted under Sister2Sister

Software for deep learning and deep learning for software 

Possible industry involvement: Some of these projects are funded by Facebook research awards and J.P.Morgan AI research awards. 

We have three openings for different tasks including those listed below.
NOTE: We especially encourage applications from women, Aboriginal peoples, and other groups underrepresented in computing.

*** Subproject 1. 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: 
EAGLE: Creating Equivalent Graphs to Test Deep Learning Libraries (our ICSE 2022 paper, forthcoming, check my homepage)
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

*** Subproject 2. 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. 

Early work and background can be found here: 
https://www.cs.purdue.edu/homes/lintan/projects.html

*** Subproject 3. Leveraging Deep Learning to Detect and Fix Software Bugs and Vulnerabilities

In this project, we will develop cool machine learning approaches to automatically learn bug and vulnerability patterns and fix patterns from historical data to detect and fix software bugs and security vulnerabilities. 

Early work and background can be found here: 
https://www.cs.purdue.edu/homes/lintan/publications/cure-icse21.pdf
https://www.cs.purdue.edu/homes/lintan/publications/deeplearn-tse18.pdf

More information: https://www.cs.purdue.edu/homes/lintan/research.html

Super-Resolution Optical Imaging with Single Photon Counting and Optomechanics with Nanostructured Membranes 

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 optical forces on structured membranes induced by a laser. The modeling of 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.

Uncovering Patterns in Innovator Behavior and Decision Making 

The objective of the proposed work is to develop an in-depth understanding of the interrelationships between innovator challenges and the resources they require to succeed. This will be done by analyzing innovator stories from publicly available podcasts like ‘How I built this’, and ‘Y-combinator’ which present first-hand accounts of well-known innovator paths to success. Stories from the podcasts will be analyzed to highlight specific problems faced by innovators and the ways they addressed them. The project will involve working with a relational database and a graphical user interface to document and call out variables that are key to each studied story. These data will be analyzed to generate insights to help future innovators. The project offers the opportunity to work closely with a graduate mentor who will assist in conducting the analysis and synthesizing results to draw out insights.

More information: N/A