Research Projects

Projects are posted below; new projects will continue to be posted through February. To learn more about the type of research conducted by undergraduates, view the 2018 Research Symposium Abstracts.

2019 projects will continue to be posted through January!

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:


3D Printed Mobile Microrobots

Research categories:  Mechanical Engineering, Nanotechnology
School/Dept.: Mechanical Engineering
Professor: David Cappelleri
Preferred major(s): ME, ECE, ChE
Desired experience:   Junior standing or higher, 3D printing experience, electrical circuits experience, CAD design fluency, programming experience.

In this project, the student will be tasked with using a state-of-the-art two photon polymerization (TPP) 3D printer to fabricate mobile microrobots for biomedical and manufacturing applications. A new technique will be developed for printing with photoresist with embedded magnetic particles and aligning the particles during the 3D printing process. The student will explore various micro-3D printer settings and evaluate them for different microrobot designs. The mobile microrobots produced will be tested with an external magnetic field generating system.

More information:


A New Ignition Technology for Lean-burn Combustion Engines

Research categories:  Aerospace Engineering, Mechanical Engineering
School/Dept.: Aeronautics & Astronautics
Professor: Li Qiao
Preferred major(s): Mechanical or Aerospace Engineering
Desired experience:   Thermodynamics, fluid mechanics, experimental skills, design experience

The gas turbine and internal combustion engine industries are pushing towards lean-burn combustion. Lean combustion means a small amount of fuel (much lower than the stoichiometric condition) is supplied and burned in the combustion chamber. The biggest advantage of lean-burn combustion is that it can lower emissions. However, lean-burn technologies have several challenges. One of the challenges is ignition, which becomes difficult for lean fuel/air mixtures. A potential solution is to use hot turbulent jets to ignition a lean mixture, rather than a spark plug. The hot jets are generated by burning a near-stoichiometric mixture in a small volume called pre-chamber.

This research will investigate the ignition behavior of a lean mixture by a hot turbulent jet. The undergraduate researcher will work closely with a graduate student on experiments. High-speed imaging techniques will be applied to visualize the jet penetration and ignition processes in a combustor at Zucrow Laboratories.


Active Learning: Choosing the Right Data for Machine Learning

Research categories:  Computer Engineering and Computer Science
School/Dept.: Electrical and Computer Engineering
Professor: Yung-Hsiang Lu
Preferred major(s): Computer Engineering, Computer Science, Electrical Engineering, Computer and Information Technology
Desired experience:   Computer Programming

Machine learning can be classified into different categories. One is called supervised learning: each piece of data is associated with a correct answer (also called label). Since machine learning is not perfect, labeling usually needs human efforts and can be very expensive. Another type of learning is unsupervised learning: there is no correct answer and this is frequently used in clustering data into groups. Active learning is somewhat in between. Unsupervised learning is used to cluster data and identify the data that is distinct and should be labeled.

This project will use public datasets of images (or videos) as the foundation for training machine models (supervised learning). Then, new data is clustered to discover which should be labeled. This is part of the CAM2 (Continuous Analysis of Many CAMeras). CAM2 discovers, retrieves, and analyzes vast amounts of real-time data from worldwide network cameras.

More information:


Additive Manufacturing (3D Printing) of Solid Propellants

Research categories:  Aerospace Engineering, Chemical, Material Science and Engineering, Mechanical Engineering
School/Dept.: ME
Professor: Steven Son
Preferred major(s): ME, AAE, ChE or MSE
Desired experience:   Junior or senior level students are preferred. Aptitude and interest in graduate school also desirable. Good laboratory or hands on work experience desirable.

Significant advancements have been made in the fabrication of energetic materials with additive manufacturing (AM) processes. The geometric flexibility of AM has been touted, but little has been done to combine complex geometries with spatially-varying thermodynamically optimized materials in solid propellants. Investigation of the intersection of these areas is needed to fulfill the potential of tailorability of AM processes for propellant optimization. The propellant grains result in complex geometries. Recent development of an ultrasonic-vibration assisted direct write printing system at Purdue has opened a range of new materials for printing. Steps are being taken to combine AM techniques in a single, multi-nozzle printer to allow continuous fabrication of a propellant with two or more major components. This project will focus on printing thermodynamically optimized solid propellants in with a range of internal geometries and investigating their effects with classical and more recent diagnostic techniques.


Adhesives at the Beach

Research categories:  Bioscience/Biomedical, Chemical, Environmental Science, Life Science, Material Science and Engineering, Physical Science
School/Dept.: Department of Chemistry
Professor: Jonathan Wilker
Preferred major(s): Biology, Biomedical Engineering, Chemical Engineering, Chemistry, Materials Engineering
Desired experience:   This project will involve aspects of marine biology (e.g., working with live mussels), materials engineering (e.g., measuring mechanical properties of adhesives), and chemistry (e.g., making surfaces with varied functionalities). Few people at any level will come in with knowledge about all aspects here. Consequently we are looking for adventurous students who are wanting to roll up their sleeves, get wet (literally), and learn several new things.

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. Characterization efforts include experiments with live animals, extracted proteins, and peptide models. 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. In order to design higher performing synthetic materials we must, first, learn all of the tricks used by nature when making adhesives. Future efforts for this coming summer will revolve around work with live mussels. Plans for experiments include changing the water, surfaces, and other environmental conditions around the animals. Mechanical performance of the resulting adhesives will be quantified and compared. Microscopy and other methods will be used to further understand the factors that dictate how these fascinating biological materials can function under such demanding conditions.


After the Fire: Rapid Decontamination of Plastic Potable Water Infrastructure Materials

Research categories:  Chemical, Civil and Construction, Environmental Science, Material Science and Engineering
School/Dept.: Materials Engineering
Professor: Kendra Erk
Desired experience:   Clear enthusiasm for chemistry and materials; evidence of strong internal motivation and initiative.

The 2018 Camp Fire is the most destructive and deadliest wildfire in California’s history, and more than 27,000 Californians faced tremendous hardship and loss. Many people are asking when they will be able to have safe drinking water again, when can they rebuild, and how to determine if their homes are safe. These critical questions require scrutiny because of the extensive damage to the drinking water distribution systems and even building plumbing. Volatile organic chemicals (VOC) have been found at 100s-1000s of ppb levels exceeding safe drinking water limits. The SURF student will: (1) conduct experiments that simulate plastic potable water infrastructure chemical contamination and (2) determine the effectiveness of water rinsing and warm air flushing at removing organic contaminants that have diffused into the plastics. The student will work with faculty and a Lillian Gilbreth postdoctoral research associate who were called into the disaster zone for their expertise at responding to and recovering from the large-scale drinking water distribution contamination incident.


Applications of Deep Reinforcement Learning

Research categories:  Computer Engineering and Computer Science
School/Dept.: IE
Professor: Vaneet Aggarwal
Preferred major(s): CS, EE, Math, Stats

Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps; for example, maximize the points won in a game over many moves. Our group is involved in coming up with fundamental algorithms for many aspects in reinforcement learning, and applying them to problems in social networks and transportation.


Building Computer Systems Software for AI and IoT

Research categories:  Computer Engineering and Computer Science
School/Dept.: ECE
Professor: Felix Lin
Preferred major(s): ECE or CS
Desired experience:   Enthusiasm in programming, exploring, and building.

While the mobile computers are still flourishing, we are quickly embracing a variety of new computing paradigms -- wearables, IoT, and VR headsets, just to name a few. They challenge the way we design and build computer software today. In this project, you will be involved in redefining systems software to make computers smarter, faster and cooler. This project will give you a lot of fun in hacking Linux, frameworks, and various modern hardware.

Underrepresented minority students are particularly encouraged to apply.

More information:


Building Software for Environmental Modeling

Research categories:  Agricultural, Computer Engineering and Computer Science, Environmental Science, Other
School/Dept.: Agricultural and Biological Engineering
Professor: Dharmendra Saraswat
Preferred major(s): Agricultural Engineering , Civil Engineering, Computer Science or related disciplines
Desired experience:   Programming skills in any language with some experience in frontend and backend web development is desired.

Agricultural and Biological Engineering Department has contributed several tools for environmental modeling community. It is a challenge to review and understand old codes with minimum documentation. This project involves modernizing an environmental modeling software written primarily in Perl. In this project, the SURF student will first assess the current application, create a plan for the new iteration in collaboration with the project supervisor, get a head start on developing the new application and document the process. The SURF student will work with a staff programmer.


Characterization of Decomposition and Detonation of Cocrystal Explosives

Research categories:  Aerospace Engineering, Chemical, Material Science and Engineering, Mechanical Engineering
School/Dept.: ME
Professor: Steven Son
Preferred major(s): ME, AAE, ChE or MSE
Desired experience:   Junior or Senior UG students preferred. Good lab skills are highly desired.

Cocrystal explosives offer the possibility improved safety and performance over conventional materials. The SURF student would assist graduate students in the study of novel cocrystal explosives. Both slow heating and detonation experiments and detonation experiments will be designed and performed.


Computational Modeling of Photon Transport in Nanocomposites

Research categories:  Computational/Mathematical, Material Science and Engineering, Mechanical Engineering, Nanotechnology
School/Dept.: Mechanical Engineering
Professor: Xiulin Ruan
Preferred major(s): Mechanical Engineering, Materials Sciences, Physics, Electrical Engineering, Computational Engineering
Desired experience:   The student should have an intermediate level of scientific computing experience (i.e. MATLAB or Python knowledge), strong analytical and numerical skills, and an interest in parallel computing. Completed coursework in Physics (Electricity & Magnetism) and Heat Transfer will be helpful, but not required.

This project will aid in an ongoing effort to achieve ultra-efficient nanocomposites for radiative cooling applications. Achieving radiative cooling requires engineering optical properties of nanocomposites to reflect and emit in certain regions. This work will focus on how to optimize the nanocomposites through computational modeling to achieve the optimal optical properties.


Computational modeling of mechanosensitive behaviors of cells

Research categories:  Bioscience/Biomedical, Computational/Mathematical, Life Science
School/Dept.: Weldon School of Biomedical Engineering
Professor: Taeyoon Kim
Preferred major(s): Mechanical Engineering
Desired experience:   C language, MATLAB, and other coding skills

Cells are able to sense surrounding mechanical environments. For example, a number of experiments have demonstrated that nano- and micro-patterns can guide migration of cells. This cell behavior is called the contact guidance and plays an important role in various physiological processes. In this research project, we aim to develop a rigorous computational model to study mechanisms of the contact guidance.


Cure-in-Place-Shelters for Disaster Preparedness

Research categories:  Chemical, Civil and Construction, Environmental Science, Material Science and Engineering, Mechanical Engineering
School/Dept.: Materials Engineering
Professor: Kendra Erk
Preferred major(s): Science or engineering students are welcome, including but not limited to chemistry, physics, geology, and the following engineering disciplines: chemical, civil, environmental, materials, mechanical.
Desired experience:   Enthusiasm for chemistry and an interest in materials research. Prior experiences with composites would be a benefit to the project but are not required.

Quick-cure polymer-based composites can be used for creating temporary shelters and other structures immediately after a disaster (i.e. earthquake, hurricane, etc.) Currently, it can take days to months to provide traditional types of temporary housing. The few temporary shelter options on the market are designed around concepts such as DRASH tents, modular construction, and trailers. Our research team has recently conducted studies on cured-in-place composites for infrastructure repair. This model polymer composite system could be developed into rapidly-deployable shelters that require few tools, could be towed, air-dropped, or stored, would be lightweight but strong and rigid. The SURF student will (1) investigate whether uncured composite can withstand the pressures necessary for inflation into shape, (2) assist in developing non-toxic UV-curable resin formulations and (3) characterize and understand how the mechanical, thermal, shelf-life and other material properties are influenced by the chemical formulation to determine structure/property/performance maps. Through this project, students will develop knowledge and important skills in material design and mechanical testing of composites.


Data Visualization and Analysis for IoT Based Smart Irrigation System

Research categories:  Agricultural, Civil and Construction, Computer Engineering and Computer Science, Environmental Science, Other
School/Dept.: Agricultural and Biological Engineering
Professor: Dharmendra Saraswat
Preferred major(s): Agricultural Engineering, Civil Engineering, Environmental Engineering, Computer Science or related disciplines
Desired experience:   Programming skills in any language with some experience in statistics is desired.

It is reported that currently almost 33 percent of the global population is affected by water scarcity and by 2030, this figure is expected to climb up to almost 50 percent. Around 60 percent of the water used for irrigation is wasted, either due to evapotranspiration, land runoff, or simply inefficient, primitive irrigation application methods. This realization has brought attention to smart irrigation – powered by the internet of things (IoTs) – that can be a better way of managing water stress on a global basis. In this project, the SURF student will customize commercially available software to analyze and visualize data, perform calculations/combine new data, run time-based calculations, plot functions for visual understanding and perform sophisticated analysis by combining data from several field nodes. The SURF student will work with Project Supervisor and a staff programmer.


Design and Analysis of Novel Approaches for Packaging of Li-Ion Batteries for Automotive Applications

Research categories:  Computational/Mathematical, Mechanical Engineering, Mechanical Systems, Other
School/Dept.: School of Mechanical Engineeing
Professor: Thomas Siegmund
Preferred major(s): Mechanical Engineering

E-mobility is a key driver of future transportation systems. E-vehicles rely on energy storage in batteries, and such batteries packages need to be integrated into the overall vehicle structure under consideration of structural and thermal design considerations. This research project will advance novel solutions to do so. The SURF student will work on CAD model design, simulations and experiments on simulated Li-ion battery packages for mechanical and thermal safety.


Developing new techniques for NO3- isotope analysis

Research categories:  Chemical, Environmental Science
School/Dept.: EAPS
Professor: Greg Michalski
Preferred major(s): Chemistry
Desired experience:   wet chemistry, analytical chemistry, research

Nitrate is a important compound in the atmosphere and biosphere. Its stable isotope composition of nitrate are informative about assessing sources of nitrate and processes that form it. However, current analysis techniques and slow and cost ineffective. Therefore developing new nitrate isotope analysis techniques that are fast, accurate, precise, and inexpensive is desirable. We are developing a new technique using Ti3+ to reduce NO3- into N2O for analysis by isotope ratio mass spectrometry


Development of New Approaches for Biological Imaging and Materials Design using Mass Spectrometry

Research categories:  Chemical, Innovative Technology/Design, Material Science and Engineering
School/Dept.: Chemistry
Professor: Julia Laskin
Preferred major(s): Chemistry, biochemistry, chemical engineering, computer science, electrical engineering, materials engineering
Desired experience:   We are looking a different skill set for different aspects of the project. If you are excited about science and dedicated to research, you will find an excellent environment in our lab.

We have two projects in the lab. In one project, we develop new analytical approaches for imaging of numerous biomolecules in biological systems. We need help in running experiments, analyzing data, and development of new computational approaches, which will streamline data analysis and facilitate biological discoveries. In another project, we develop unique instruments for designing layered coatings using beams of complex ions. In this project, we need help with the synthesis of relevant precursor molecules, their characterization using mass spectrometry and other analytical techniques, ion deposition on surfaces, and surface characterization.


Elastically-driven flow focusing in micro-channels

Research categories:  Chemical, Life Science, Material Science and Engineering, Mechanical Engineering
School/Dept.: Chemical Engineering
Professor: Vivek Narsimhan
Preferred major(s): Chemical Engineering, Biological Engineering, Physics, Chemistry, Applied Mathematics
Desired experience:   Basic understanding of MATLAB

Separation of biological suspensions (e.g., cells, bacteria, macro-particles in solution) find wide use in the detection, diagnosis, and treatment of disease. Traditional techniques such as centrifugation and filtration (size-exclusion) are common, but for many point-of-care applications, it is desired to use strategies that are more gentle, cheap, portable, and low-volume. Here, microfluidics has emerged as an attractive method to address these concerns. Using channels with minimal power sources or moving parts (i.e., only syringes), several laboratory studies have demonstrated that one can purify and isolate cancer cells, leukocytes, or bacteria samples from diluted whole blood without the use of specific biomarkers. The scientific premise behind these studies is that various components in blood have different shapes, sizes, and deformability, and this variability in physical properties allows one to isolate/purify these components using flow forces.

In this project, we propose to improve focusing-based microfluidic techniques through the addition of long-chain, charge-neutral polymers (e.g., PEO or PVP) to the biological suspension. If added in dilute amounts (~1% wt. or below), these bio-compatible polymers impart additional flow forces to the particles in the fluid. These forces depend sensitively depend on the particle’s size, shape, and deformability, and hence can be used to fractionate particles by shape and size. The student will do the following: (a) fabricate non-spherical microparticles, and (b) visualize these particles flowing in a microfluidic device through microscopy or holography. The student will learn basic synthesis and image processing for this project.


Engineering of the Tumor Microenvironment of Pancreatic Cancer

Research categories:  Bioscience/Biomedical, Mechanical Engineering
School/Dept.: Mechanical Engineering
Professor: Bumsoo Han
Preferred major(s): Mechanical or Biomedical Engineering Majors
Desired experience:   Course work on solid and fluid mechanics are required - Basic programming skill on Matlab - Basic wet lab skills are preferred, but not required.

Pancreatic ductal adenocarcinoma (PDAC) poses a significant challenge with dismal 7% 5-year survival rates. Ineffective treatment of PDAC is linked primarily to poor drug delivery through a dense PDAC stroma and to elevated drug resistance of pancreatic cancer cells. These are largely correlated to the complex tumor microenvironment (TME) of PDAC. Due to its complexity, it is extremely difficult to identify promising molecular targets and to devise innovative strategies for efficient delivery of molecules at the PDAC TME. In order to address this technical challenge, this project aims to develop and validate engineered tumor models based on microfluidics and tissue engineering technologies. Students in this project are expected to learn about microfabrication, biomechanics, biotransport and fluorescence microscopy and analysis.


Enhance the Burn Rate of Solid Propellants

Research categories:  Aerospace Engineering, Mechanical Engineering
School/Dept.: Aeronautics & Astronautics
Professor: Li Qiao
Preferred major(s): Aerospace Engineering
Desired experience:   Thermodynamics, aerodynamics, propulsion

Composite solid propellants are a major source of chemical energy for most of the solid rockets in use today with applications to space, ballistic, tactical and assist propulsion, and are made up of three components: binder, energetic fuel and oxidizer. Enhancing the burn rates of solid propellants is crucial for improving performance of solid rocket motors in terms of higher thrust, simplified propellant grain geometry, and reduced overall size and weight of the propulsion system.

In this research, the undergraduate student will work closely with a graduate student to explore methods to enhance the burn rates of solid propellants. The nature of the research is experimental, involving materials synthesis and characterization, combustion measurement using high-speed infrared camera, and data collection and analysis.


Evaluate Epigenetic Effects on Transgene Expression

Research categories:  Bioscience/Biomedical, Chemical
School/Dept.: Davidson School of Chemical Engineering
Professor: Chongli Yuan
Preferred major(s): Chemical Engineering
Desired experience:   Previous research experience required

Transgene expression can be potentially regulated via epigenetic marks. We are making synthetic chromatin containing different histone modifications and assess their impact on transgene activity. Participating students will learn about molecular cloning, transcription assays and other molecular/cellular bio techniques.


Geodesic convolution with various applications in 3D data analysis

Research categories:  Computational/Mathematical, Computer Engineering and Computer Science, Mechanical Engineering
School/Dept.: Mechanical Engineering
Professor: Min Liu
Preferred major(s): ME, ECE, CS
Desired experience:   python, c++ code, experience with convolutional neural networks

The scope this project is to explore the mechanics of geodesic convolution (in contrast to the standard Euclidean space convolution) for deep neural networks. The objective is to research for a more efficient, robust and shape-aware filter to support various applications for 3D vision data analysis, E.g. Autonomous CAR, robot navigation, and Augmented realities.


High Performance Concrete from Recycled Hydrogel-Based Superabsorbent Materials

Research categories:  Chemical, Civil and Construction, Environmental Science, Material Science and Engineering
School/Dept.: School of Materials Engineering
Professor: Kendra Erk
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.

Concrete that is internally cured by water-swollen superabsorbent polymer (SAP) particles has improved strength and durability. Widespread adoption of SAP-cured concrete is hindered by the lack of commercial SAP formulations that maintain their absorbency in cement’s high-pH environment. Most commercial SAP formulations are designed for disposable diapers and other absorbent hygiene products (AHPs), which account for ~12% (3.4M tons) of all non-durable goods in landfills. Over 70% of a diaper’s weight is composed of absorbent materials – mainly cellulose and polyacrylamide(PAM)-based SAP particles – the latter being chemically equivalent to the SAP particles that perform well in concrete research. Thus, a sustainable strategy to create effective concrete curing agents is to recycle the absorbent materials from AHPs and reprocess for use in concrete. AHP recycling efforts are already underway, including a plant in Italy with a 10,000-tonne annual capacity for AHP recycling. However, synthetic strategies must be developed to convert recycled AHPs into absorbent particles that perform well in concrete. Hypothesis and Objectives: We hypothesize that the PAM and cellulose components of AHPs can be separated and chemically crosslinked to form particles that display high absorption capacity in alkaline environments. The SURF student will: (1) obtain recycled absorbent materials and characterize the structures of the materials including composition, particle morphology, and swelling behavior; (2) design and synthesize absorbent particles by combining different ratios of recycled absorbent materials with a crosslinking agent and grinding/sieving to create particles with dry sizes of 10-100 micron; (3) identify the dosages of absorbent particles required to create internally cured concrete with good workability and mechanical strength; and (4) perform cost-benefit analysis of concrete cured by recycled particles and commercial SAP.


High-Volume Treatment of Metal-Polluted Water

Research categories:  Agricultural, Chemical, Civil and Construction, Environmental Science, Material Science and Engineering, Mechanical Engineering
School/Dept.: Materials Engineering
Professor: Kendra Erk
Preferred major(s): Science or engineering students are welcome, including but not limited to chemistry, physics, geology, and the following engineering disciplines: chemical, civil, environmental, materials, mechanical.
Desired experience:   Enthusiasm for chemistry and an interest in materials research. Prior experiences with composites would be a benefit to the project but are not required.

Mining of coal and metallurgical ores has significantly impacted the land and groundwater quality in many semi-arid regions and there are great challenges to mitigate the impact of this legacy pollution. The impacted areas have a portion of their scarce water resources chemically contaminated and are lacking a cost-effective and comprehensive strategy to rehabilitate the fouled groundwater. Laboratory testing of polluted water will be passively treated with geotextile-like materials that have been surface modified with polymers and clay minerals designed to selectively sequester trace chemical pollutants. The novel engineered material will be designed to have high surface area in a structure that will minimally impact water transport. As the water passes over the material, the pollutant will be irreversibly bound to the surface. The SURF student will investigate chemical surface modification of polymer mesh materials to induce chemical binding of the select pollutants. Testing will include measuring the reduction in pollutants as a function of exposure time and determining the total binding capacity of the modified material mesh exposed to a mixture of pollutants and other species typically present in groundwater (i.e. organic/inorganic particulates).


Human Body Communication

Research categories:  Bioscience/Biomedical, Computer Engineering and Computer Science, Electronics
School/Dept.: ECE
Professor: Shreyas Sen
Preferred major(s): ECE, BME

The student will work on theory and device design related to using the human body as a communication medium to improve Healthcare and HCI.


Human Factors Considerations: Older Adults and Autonomous Vehicle Systems

Research categories:  Computer Engineering and Computer Science, Industrial Engineering, Innovative Technology/Design
School/Dept.: Industrial Engineering
Professor: Brandon Pitts
Preferred major(s): Industrial Engineering
Desired experience:   Human Factors, Matlab, Transportation, some experience in statistics, some computer programming experience (in any language)

Automobiles are becoming increasingly autonomous. At the same time, the demographics of drivers using these advanced vehicles is changing. In particular, adults aged 65 years and older are the fastest growing age group worldwide and are expected to benefit from vehicle automation. However, age-related perceptual and cognitive difficulties may limit the extent to which these systems are useful for individuals in this age category. The goal of this project is, therefore, to quantify interactions between (older adult) drivers and autonomous driving systems in order to develop approaches that enhance roadway safety for various aging populations.

The SURF student will assist with collecting and analyzing data from human-subject experiments (using a laboratory driving simulator) and with writing any project publication. In addition, the student will meet regularly with faculty and graduate mentors to communicate his/her progress.


Illumination of Damage through Microtomography

Research categories:  Aerospace Engineering, Computer Engineering and Computer Science, Industrial Engineering, Material Science and Engineering, Mechanical Engineering
School/Dept.: Aeronautics and Astronautics
Professor: Michael Sangid
Preferred major(s): AAE, ME, MSE, EE, CSE, or IE
Desired experience:   Students are expected to work with Image Processing and Visualization tools, as well as Matlab.

Damage in structural materials is often difficult to quantify, instead we rely on large scale component level testing and curve fitting. With the advent of advanced microtomography, we have the ability to identify damage inside the bulk of the material, in which the samples are subjected to mechanical loading. Thus, in this project, microtomography scans will be reconstructed and the damage in the form of voids or cracks will be characterized and quantified in several material systems (including carbon fiber reinforced composites and Ti-6Al-4V produced via additive manufacturing). The interaction of damage with microstructural features will be assessed, in order to achieve a physics-based understanding of material failure.


Image analysis of vesicle membranes

Research categories:  Chemical
School/Dept.: Chemical Engineering
Professor: Vivek Narsimhan
Preferred major(s): Chemical Engineering, Biological Engineering, Physics, Chemistry, Applied Mathematics
Desired experience:   It is desirable for the student to have a background in MATLAB, Python, or equivalent.

Vesicles are elastic and highly deformable sacs of fluid enclosed by a lipid bilayer. These entities are critical for the intracellular compartmentation and molecular trafficking that underlie the signaling, defense and nutrition vital for an organism’s survival. Similar lipid architectures are also used in industrial applications ranging from drug and gene delivery to fabric softeners. Lastly, vesicles are model systems to understand fundamental processes that occur in all cellular membranes (e.g., budding, fusion, membrane-protein interactions). For these reasons, there is immense interest to characterize the physical properties and mechanical behavior of vesicular systems under various conditions.

In this project, the student will develop and modify image processing codes to analyze microscope images of vesicles. The goal of these codes is to extract elastic properties of the lipid bilayers through thermal fluctuations of the vesicle shape over time. Ambitious students will also have the opportunity to synthesize vesicles in lab, examine more complicated membrane architectures (multicomponent vesicles), and solve equations describing the shape dynamic of these entities under weak flow.


Indoor Air Pollution Research: From Nano to Bio

Research categories:  Agricultural, Bioscience/Biomedical, Chemical, Civil and Construction, Environmental Science, Life Science, Mechanical Systems, Nanotechnology, Physical Science
School/Dept.: Civil Engineering
Professor: Brandon Boor
Preferred major(s): Students from all majors are welcome to apply.
Desired experience:   Interest in studying contaminant transport in the environment, human health, air pollution, HVAC and building systems, microbiology, nanotechnology, and atmospheric science. Experience working in a laboratory setting with analytical equipment and coding with MATLAB, Python, and/or R. Passionate about applying engineering fundamentals to solve real-world problems.

Airborne particulate matter, or aerosols, represent a fascinating mixture of tiny, suspended liquid and solid particles that can span in size from a single nanometer to tens of micrometers. Human exposure to aerosols of indoor and outdoor origin is responsible for adverse health effects, including mortality and morbidity due to cardiovascular and respiratory diseases. The majority of our respiratory encounters with aerosols occurs indoors, where we spend 90% of our time. Through the SURF program, you will work on several ongoing research projects exploring the dynamics of nanoaerosols and bioaerosols in buildings and their HVAC systems.

Nanoaerosols are particles smaller than 100 nm in size. With each breath of indoor air, we inhale several million nanoaerosols. These nano-sized particles penetrate deep into our respiratory systems and can translocate to the brain via the olfactory bulb. These tiny particles are especially toxic to the human body and have been associated with various deleterious toxicological outcomes, such as oxidative stress and chronic inflammation in lung cells. Bioaerosols represent a diverse mixture of microbes (bacteria, fungi) and allergens (pollen, mite feces). Exposure to bioaerosols plays a significant role in both the development of, and protection against, asthma, hay fever, and allergies.

Your role will be to conduct measurements of nanoaerosols and bioaerosols in laboratory experiments at the Purdue Herrick Laboratories, as well as participate in a field campaign at Indiana University - Bloomington in collaboration with an atmospheric chemistry research group. You will learn how to use state-of-the-art air quality instrumentation and perform data processing and analysis in MATLAB.

More information:


Lake Michigan Ecosystem Modeling

Research categories:  Civil and Construction, Computational/Mathematical, Environmental Science, Mechanical Engineering, Physical Science
School/Dept.: Civil Engineering
Professor: Cary Troy
Preferred major(s): Civil, Environmental, or Mechanical Engineering
Desired experience:   Proficiency in Matlab; Good communication skills, written and oral; Exposure to differential equations

This is an NSF-funded project examining the role of turbulence in the Lake Michigan ecosystem. Particularly, the project is quantifying the interactions between water column turbulence and the ability of invasive quagga mussels to filter nutrients and plankton out of the water column. The SURF research will involve the development of a 1-D biogeochemical model that models the temporal and vertical distribution of nutrients (e.g. phosphorus), phytoplankton, and zooplankton in Lake Michigan. The successful SURF applicant will be responsible for the coding and development of the model in Matlab, as well as potentially participating in data collection on Lake Michigan and the analysis of this data.


Low-cost user-friendly biosensors for animal health

Research categories:  Agricultural, Bioscience/Biomedical, Electronics, Innovative Technology/Design, Life Science, Material Science and Engineering, Mechanical Systems
School/Dept.: Agricultural and Biological Engineering
Professor: Mohit Verma
Preferred major(s): Biomedical engineering, biological engineering, electrical engineering, mechanical engineering, or other relevant fields
Desired experience:   To be successful at this position, you should have a GPA>3.5, prior experience working in a wet lab (ideally experience with bacterial culture and DNA amplification), experience building electromechanical devices, and the ability to work in a team.

Infectious diseases are a leading cause of economic burden on food production from animals. For example, bovine respiratory diseases lead to a loss of ~$480/animal. Current methods for tackling these diseases includes the administration of antibiotics by trial-and-error. This approach leads to failure of treatment in up to one-third of the cases. In addition, it also leads to a proliferation of antibiotic resistance in pathogens.

Our research project focuses on developing a low-cost user-friendly biosensor based on paper that can detect which pathogen is causing the disease and whether it exhibits antibiotic resistance. Such a biosensor would provide a readout to the farmer or the veterinary physician and suggest which antibiotics are likely to be successful.

The SURF student will have three objectives: i) design primers for detecting pathogens associated with bovine respiratory diseases, ii) build a device for processing the sample and extracting DNA that can be amplified by the biosensor, and iii) build a device for detecting colorimetric/fluorometric output from the biosensor.

More information:


Micro/nano Scale 3D Laser Printing

Research categories:  Mechanical Engineering, Mechanical Systems, Nanotechnology
School/Dept.: Mechanical Engineering
Professor: Xianfan Xu
Preferred major(s): Mechanical Engineering, Physics, Materials Engineering, Chemical Engineering, Electrical Engineering
Desired experience:   Junior or Senior standing, GPA>3.6

The ability to create 3D structures in the micro and nanoscale is important in many fields including electronics, microfluidics, and tissue engineering and is an emerging area of research and development. This project deals with the development and testing of a setup for building microscopic 3D structures with the help of a femtosecond laser. A method known as two photon polymerization is typically 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 helps in getting the desired shape and the structures are then built in a layer by layer fashion. The setup incorporates all the steps from a designing a CAD model file to slicing the model in layers to generating the motion path of the laser needed for fabricating the structure. In order to make a solid and stable structure, investigation of better materials and optimization of the process parameters is needed. Besides, possible improvements to the control algorithms used in the setup can be done to increase the efficiency of the process and build the structures faster.


Monitoring Bacterial Contamination in Biologics

Research categories:  Agricultural, Bioscience/Biomedical, Chemical, Mechanical Systems
School/Dept.: Mechanical Engineering
Professor: Arezoo Ardekani
Preferred major(s): Biomedical engineering, chemical engineering, biological engineering

Biologics comprised 22% of major pharma companies in 2013 and is expended to grow to 32% of sales in 2023. Biologics are large complex molecules that are created by microorganisms and mammalian cells. They are polypeptides or proteins such as monoclonal antibodies, cytokines, fusion proteins used in vaccines, cell therapies, gene therapies, etc. Impurities such as aggregates, cell debris, bacterial and viral contamination can negatively impact the manufacturing process. In this project, we will focus on developing methods for monitoring bacterial contamination.


Multiphase Fluid Flows in Tight Spaces

Research categories:  Bioscience/Biomedical, Chemical, Computational/Mathematical, Physical Science
School/Dept.: Mechanical Engineering
Professor: Ivan Christov
Preferred major(s): Mechanical Engineering, Chemical Engineering, Applied Mathematics, Computational Science
Desired experience:   1. Thorough understanding of undergraduate fluid mechanics. 2. Programming experience with high-level language such as Python or MATLAB. 3. Experience with shell/command-line environments in Linux/Unix; specifically, remote login, file transfers, etc. 4. Experience researching difficult questions whose answers are not found in a textbook. 5. Desire to learn about new fluid mechanics phenomena and expand computational skillset.

Multiphase flows are fluid flows involving multiple fluids, multiple phases of the same fluid, and any situation in which the dynamics of an interface between dissimilar fluids must be understood. Examples include water displacing hydrocarbons in secondary oil recovery, a mixtures of particle-laden fluids being injected into a hydraulically fractured reservoirs ("fracking"), introduction of air into the lungs of pre-maturely born infants to re-open their liquid-filled lungs and airways, and a whole host of other physico-chemical processes in biological and industrial applications.

The goal of this SURF project will be to study, using computational tools such as ANSYS Workbench and/or the OpenFOAM platform, how multiphase flows behave in tight spaces. To accomplish this goal, the SURF student will work with a PhD student. Specifically the dynamics of interfaces between different phases and/or fluids will be studied through numerical simulation, and the effect of the flow passage geometry will be addressed. Some questions that we seek to address are whether/how geometric variations can stabilize or destabilize an interface and whether/how geometry affects the final distribution of particles in particle-laden multiphase flow passing through a constriction/expansion. Applications of these effects to biological and industrial flows will be explored quantitatively and qualitatively.

More information:


Optimization of Quantum Circuits for Noisy Environments

Research categories:  Electronics, Nanotechnology, Physical Science
School/Dept.: ECE
Professor: Andrew Weiner
Preferred major(s): Electrical Engineering, Physics or any closely related major
Desired experience:   (a) Experience using Matlab or Python for instrument control is strongly preferred. (b) Electricity and Magnetism coursework preferred

Our research group works on encoding and processing quantum information in the frequency domain. The platform we work with – biphoton frequency combs (BFCs) – are photon pairs that are entangled in time and energy (frequency). We use commercial hardware like phase modulators and pulse shapers for quantum state preparation and manipulation. Some recent demonstrations include measurement of high dimensional frequency-bin entanglement and tunable quantum gates, among others. Our current efforts are focused on developing quantum circuits to simulate the dynamics of molecules.

The SURF student’s contribution would be as follows:
(1) Develop an instrument control interface to automate the process of quantum state preparation. In particular, we often use commercial pulse shapers to “carve” BFCs from a continuous down conversion spectrum. However, carving a BFCs requires precise positioning of frequency bins in order to ensure that one passes energy-matched (anti-correlated in frequency) comb line pairs. The student would automate this process by interfacing with the pump laser, pulse shaper, and single photon detectors and implementing appropriate instrument control.

(2) What we often measure in our quantum experiments is coincident single photon detection events or, simply, coincidences. However, the number of coincidences depends on factors like loss in the experimental system, the timing jitter of single photon detectors, and the resolution of the timing electronics. The student will carry out a systematic study to evaluate the effect of these factors on the coincidence rate out of a quantum circuit and make recommendations on how to optimize the detection system for high coincidence rates or high coincidence-to-accidental ratios (analogous to signal to noise ratio).


Particle Detachment from Polymeric Substrates upon Mechanical Deformation

Research categories:  Material Science and Engineering, Mechanical Engineering
School/Dept.: Materials Engineering
Professor: Chelsea Davis
Preferred major(s): Materials Science and Engineering, Mechanical Engineering, Chemical Engineering, Physics, Polymer Science

The attachment of small particles to a soft adhesive layer is critical in the handling of energetic materials and active pharmaceutical ingredients that are often produced in powder form. An understanding of the debonding mechanisms of rigid spherical and prismatic particles from a polymer substrate due to mechanical deformation will enable the development of precisely-controlled particle adhesion and release systems. This project will investigate the debonding process of rigid microparticles from thermoplastic and elastomeric substrates subjected to various modes of mechanical deformation (specifically uniaxial tension and flexion). Our experimental approach will involve sample preparation, micromechanical testing, and imaging via optical and electron microscopy conducted by the student.


Photonic Component Design for Quantum and Classical Information Processing

Research categories:  Electronics, Nanotechnology, Physical Science
School/Dept.: ECE
Professor: Andrew Weiner
Preferred major(s): Electrical Engineering, Physics or any closely related major
Desired experience:   (a) U.S. citizenship, (b) Electricity and Magnetism coursework preferred

Photons are ideal carriers of quantum information because they are robust against decoherence and are compatible with fiber optic networks. Our research group works on encoding and processing quantum information in the frequency domain. One limitation of conventional or bulk optical equipment is that these devices have high optical losses, which is a major issue for applications in the quantum regime. We recently designed photonic integrated circuits to implement elementary quantum gates and carry out operations like parallel single qubit rotations.

The SURF student’s contribution would be as follows:
(1) Design and simulate photonic elements (microresonators for generation of Kerr and quantum frequency combs, pulse shapers, etc.) for our next round of chip fabrication. The student will be given performance specifications and be expected to use analytical expressions, as well as FDTD and/or FEM simulation tool, and come up with recommendation for appropriate device geometries.

(2) Characterize on-chip optical devices/systems and relate actual performance in our first batch of chips to the original design specifications. Depending on the student’s level of experience, he/she will collect data from our testbed, compare it to the design specifications, and draw appropriate inferences from the data.


Preliminary Design of a 10-passenger Regional Electric Aircraft

Research categories:  Aerospace Engineering, Mechanical Engineering
School/Dept.: ME
Professor: Nicole Key
Preferred major(s): AAE or ME
Desired experience:   Students who have taken Aircraft design related courses are strongly encouraged to apply.

The goal of the project is to explore the design envelope for regional electric aircrafts. The project starts with mission requirements including input information of range and maximum takeoff weight. Preliminary design of the aircraft will be performed including trade studies.


Preparing engineers to address climate change and its implications on sustainability: modeling impact of college experiences on students

Research categories:  Civil and Construction, Educational Research/Social Science
School/Dept.: Engineering Education
Professor: Allison Godwin
Preferred major(s): All STEM majors invited to apply
Desired experience:   Some experience in statistics and programming languages is preferred. All other skills including human subject research ethics, statistical analysis in R, data management, will be taught.

Engineers are an essential part of solving the effects of climate change and must not only be aware of the issues but empowered to make change to reduce and shift the impact of humans on the planet. This research investigates engineering students' experiences during undergraduate programs that predict their beliefs about climate change and empowerment to address its related implications for sustainability in their careers. This study is the first of its kind to explore how experiences in college impact students' climate change beliefs and interest to address related implications for sustainability. This project is a collaborative effort between the Virginia Tech Charles E. Via, Jr. Department of Civil and Environmental Engineering Myers-Lawson School of Construction and the Purdue University School of Engineering Education.

This SURF research project uses national survey data from ~4,000 senior engineering design students collected in 2018 along with 7,673 first-year student responses collected in 2014 to model how student experiences during undergraduate education may influence their understanding of climate change and desire to address sustainability in their future engineering careers. The SURF student will use multilevel modeling (this modeling technique will be taught to any interested student) to analyze how student beliefs, student experiences, and institutional contexts may influence students attitudes and actions over time. The student will learn complex statistics in the programming language R, analyze data and interpret findings, and write up their results for journal publication. The student will also interface with faculty and another undergraduate summer research student at Virginia Tech.


Processing of innovative satellite remote sensing data for ocean and snow remote sensing

Research categories:  Aerospace Engineering, Computer Engineering and Computer Science, Electronics, Environmental Science, Physical Science
School/Dept.: AAE
Professor: James Garrison
Preferred major(s): ECE, AAE, Physics, EAPS
Desired experience:   Good programming skills, signal processing (ECE 301 or AAE301). Experience with software defined radio (USRP) will be a plus.

Reflectometry is a new approach to Earth remote sensing in microwave frequencies, using reflections of Global Navigation Satellite System (GNSS, e.g. GPS, Galileo, etc ...) signals from land and ocean surfaces as illumination source in a bistatic radar configuration. Through observing measurable changes in the properties of these signals, various features of the reflecting surfaces can be inferred.

Ocean surface winds is the most developed application for GNSS-Reflectometry (GNSS-R), with the launch of the CYGNSS constellation by NASA in 2016. CYGNSS data has been collected during the 2017 and 2018 Hurricane seasons, showing some capability for wind field measurements at a high spatial resolution. New models and algorithms are required, however, to optimally process these data and extract wind vectors with high sensitivity, especially at the higher wind speeds present in hurricanes. Development of these new models and algorithms requires the collection of high-quality data under carefully controlled conditions along with in situ training data provided by independent sources. With this goal in mind, Purdue has developed a wideband GNSS-R signal recorder which will be flown on the P-3 “Hurricane Hunter” aircraft operated by NOAA. This aircraft is capable of operating in extremely high winds and penetrating the Hurricane eye wall, in order to collect data inside developing tropical cyclones. GNSS-R data collected in this experiment will be compared with wind speed observations from other instruments on the P-3 aircraft, other satellite data, and model results. These comparisons will be used to develop and improved model for the extraction of ocean winds from CYGNSS and future satellite missions.

Snow Water Equivalent (SWE) is a representation of the total water stored in the snow pack. This is an important climate variable for the prediction of fresh water supplies as well as applications such as hydroelectric power. A new application of GNSS-R is measuring SWE as a change in phase of the reflected signal, a result of the slower propagation of the signal through the snow layer. Spaceborne measurements of SWE using GNSS-R have never been conducted. Special collections of CYGNSS data were conducted this year, in which raw signals (no on-board processing or compression) were collected in arcs spanning snow-covered regions in the Himalayan mountains.

SURF projects are proposed to support these two research goals for CYGNSS data. Both will involve extensive programming and data processing, using a “software defined radio” method that essentially implements all signal processing in software to operate on the full-spectrum of the recorded signal.

Applicants should have very strong programming skills, some knowledge of basic signal processing.


Programming 3D and environmental data acquisition into iFly -- a mobile iOS app

Research categories:  Computer Engineering and Computer Science, Life Science
School/Dept.: Entomology
Professor: Trevor Stamper
Preferred major(s): Computer science or engineering, or biological sciences
Desired experience:   Must have programming knowledge in Swift programming language. Mobile device iOS programming experience is highly desired.

The student researcher will be programming in Swift language on the iFly project to allow environmental sensor systems and 3D sensing systems to input data directly into the app. Student researcher will also be improving other functions of the software to build a better user experience.


Reducing Ocean Pollution By Understanding the Formation and Stability of Shipboard Emulsions

Research categories:  Agricultural, Chemical, Environmental Science, Material Science and Engineering
School/Dept.: Materials Engineering
Professor: Kendra Erk
Preferred major(s): MSE, ChemE, EEE, and related fields of science and engineering
Desired experience:   Enthusiasm for chemistry, and interest in materials, environmental, or chemical engineering. Prior experience with emulsions would benefit the project but are not required.

Bilge water is a collection of waste fluids onboard a ship and is a major source of pollution in marine environments. All waste fluids onboard (including oil, grease, fuel, detergents, etc.) are collected in the ship’s bilge until it can be treated and released into the ocean. Treatment techniques remove some of the pollutants but have a difficult time removing oil when it is in the form of an oil-in-water “shipboard emulsion” with nanoscale droplets. Consequently, oils and detergents are released into the environment. The goal of this project is to study the formation and composition of bilge water emulsions to ultimately prevent emulsion formation and improve treatment techniques. In this project, the SURF student will create and characterize model bilge water emulsions with emphasis on understanding the formation mechanisms and stability of these emulsions. The SURF student will have the opportunity to learn many different characterization techniques specific to nanoscale oil-water emulsions, including optical microscopy, dynamic light scattering, zeta potential measurements, interfacial tension measurements and more, by working closely with a current Ph.D. student in MSE and faculty in MSE and EEE (Profs. Erk, Howarter, and Martinez).


Remote sensing of soil moisture and forest biomass using P-band Signals of Opportunity: Model development and experimental validation

Research categories:  Agricultural, Aerospace Engineering, Electronics, Environmental Science, Physical Science
School/Dept.: AAE
Professor: James Garrison
Preferred major(s): ECE, AAE, Physics, ABE
Desired experience:   Basic signal processing (AAE 301 or ECE 301 or equivalent) desired. Students should know how to use basic hand tools, and be willing to work outdoors in agricultural or forest environments. A drivers license and reliable access to a car is required for field work.

Root Zone Soil Moisture (RZSM), defined as the water profile in the top meter of soil where most plant absorption occurs, is an important environmental variable for understanding the global water cycle, forecasting droughts and floods, and agricultural management. No existing satellite remote sensing instrument can measure RZSM. Sensing below the top few centimeters of soil, often through dense vegetation, requires the use of microwave frequencies below 500 MHz, a frequency range known as “P-band”. A P-band microwave radiometer would require an aperture diameter larger than 10 meters. Launching such a satellite into orbit will present big and expensive technical challenge, certainly not feasible for a low-cost small satellite mission. This range for frequencies is also heavily utilized for UHF/VHF communications, presenting an enormous amount of radio frequency interference (RFI). Competition for access to this spectrum also makes it difficult to obtain the required license to use active radar for scientific use.

Signals of opportunity (SoOp) are being studied as alternatives to active radars or passive radiometry. SoOp re-utilizes existing powerful communication satellite transmissions as “free” sources of illumination, measuring the change in the signal after reflecting from soil surface. In this manner, SoOp methods actually make use of the very same transmissions that would cause interference in traditional microwave remote sensing. Communication signal processing methods are used in SoOp, enabling high quality measurements to be obtained with smaller, lower gain, antennas.

Under NASA funding, Purdue and the Goddard Space Flight Center have developed an airborne prototype P-band remote sensing instrument to demonstrate the feasibility of a future satellite version. Complementing this technology development, a field campaign will be conducted for its third year the Purdue Agricultural research fields. This campaign will make reflected signal measurements from towers installed over instrumented fields. Measurements will be obtained over bare soil first, and then throughout the corn or soybean growth cycle. Complementing these remote sensing measurements, a comprehensive set of ground-truth data will also be collected for use in developing models and verifying their performance.

In Spring 2019 an additional experiment, using a small Unpiloted Aerial Vehicle (UAV), will be conducted in a forested area in collaboration with the School of Forestry and Natural Resources (FRN).

Work under this project will involve installing microwave electronic equipment in the field, writing software for signal and data processing, and making field measurements of soil moisture and vegetation properties.

Students interested in this project should have good programming skills and some experience with C, python and MATLAB. They should also have a strong background in basic signal processing. Experience with building computers or other electronic equipment will also be an advantage. Students should be willing to work outdoors and have an interest in applying their skills to solving problems in the Earth sciences, environment, or agriculture.

The project will involve regular travel to and from the local research field, so students should have a driving license and access to a car.


SMART (Social Media Analytics Response Toolkit)

Research categories:  Computational/Mathematical, Computer Engineering and Computer Science, Innovative Technology/Design
School/Dept.: Electrical and Computer Engineering
Professor: David Ebert
Preferred major(s): Computer Science, Electrical and Computer Engineering
Desired experience:   For this project, the ideal candidate will have good working knowledge of some of the modern web development technologies, including client-side technologies such as HTML5, SVG, JavaScript, AJAX, and DOM, and D3 as well as server-side components such as PHP, Tomcat, MySQL, etc. Experience in web services development and web based visualization APIs is a plus. Students should have a GPA of 3 or higher.

This visual analytics application provides interactive (Twitter) social media analysis and visualization capabilities through topic extraction, combination of filters, cluster analysis and stream categorization. Analysts can also create custom classifiers to extract social media messages relevant to specific events or topics. Many first responder groups in the U.S. use this platform.


Sensing the Human Factors in Laparoscopic and Robotic Surgery

Research categories:  Bioscience/Biomedical, Computer Engineering and Computer Science, Industrial Engineering, Mechanical Systems
School/Dept.: Industrial Engineering
Professor: Denny Yu
Preferred major(s): Industrial Engineering, other
Desired experience:   Human Factors, Matlab, Machine Learning, Healthcare, Medical Device Design

Work-related musculoskeletal disorders (MSDs) among surgeons are becoming more common. The purpose of this project is to use sensors to measure ergonomic risks and assess interventions to surgeons during laparoscopic and robotic surgery. This work will leverage sensing technology (e.g., motion tracking, pressure map, electromyography) to monitor surgeons’ ergonomics to ultimately develop recommendations on minimizing MSDs and how to better design an operating room.

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.


Smart Manufacturing using IoT and Machine Learning

Research categories:  Computer Engineering and Computer Science, Innovative Technology/Design, Mechanical Engineering
School/Dept.: Mechanical Engineering
Professor: Martin Jun
Preferred major(s): Mechanical Engineering, Computer Engineering, or Computer Science
Desired experience:   Virtual reality programming, mechatronics, CAD design and programming for graphics, signal processing and data analysis, machining, etc.

Autonomous operation and decision making during manufacturing processes and production are important. Using IoT technologies, machine-to-machine, machine-to-human communication and data generation are achieved and machine learning algorithms are used for data analysis and decision making. The student will work on virtual reality (VR) based visualization of data achieved from IoT devices connected to CNC machine and robots and analyze data using machine learning.


Structural and Functional Analysis of Signaling Pathways in Cancer

Research categories:  Bioscience/Biomedical, Life Science
School/Dept.: Biological Sciences
Professor: John Tesmer
Preferred major(s): Biology/Biochemistry related
Desired experience:   Freshman Chemistry and Organic Chemistry lab experience is desirable. Freshman level biology at minimum.

Undergraduate researchers in the lab will work alongside graduate students and postdoctoral fellows to decipher the molecular mechanisms of proteins involved in signal transduction from G protein coupled receptors to enzymes in the cell that control tumor growth and metastasis. Our lab uses X-ray crystallography, cryo-EM microscopy, and a battery of other biochemical and biophysical techniques to study proteins that we produce directly in our own lab. Trainees will emerge from our lab with advanced training in molecular biology, protein expression, and the purification of macromolecular complexes, and will receive an introduction to cutting edge biophysical techniques used to probe protein structure.


The Arequipa Nexus Sustainable Viticulture

Research categories:  Agricultural, Computational/Mathematical, Computer Engineering and Computer Science, Environmental Science, Innovative Technology/Design
School/Dept.: Electrical and Computer Engineering
Professor: David Ebert
Preferred major(s): Flexible: Computer Science, Food Science, Agronomy, Environmental Science, GIS, Electrical and Computer Engineering
Desired experience:   We are looking for applicants with a strong background in either of the following: GIS (Geographic Information Systems), food sciences, agronomy (soil oriented), web development or python programming (e.g. HTML/JavaScript, Leaflet, D3). Students should have a GPA of 3 or higher. Applicants with Spanish fluency are encouraged to apply.

The Universidad Nacional de San Agustín (UNSA) in Arequipa, Peru and Purdue through Discovery Park’s Center for the Environment (C4E) have partnered to create a new research, education and innovation institute to work together on key challenges for a sustainable future for the citizens of Arequipa. The Nexus Institute applies collaborative, data-driven, interdisciplinary science, technology and innovation to help chart a new course toward a sustainable future. Our lab works with key stakeholder groups to develop data, provide (winery and vineyard farm) guidelines, simulation models, and decision support tools for vineyard management through state-of-the-art data sets, GIS and remote sensing, and environmental decision tools. We are also developing a system to provide farmers with more accurate information than previously possible, helping growers to optimize crop yields and minimize use of water and other resources. The system will be first tested in Peru to create precision agriculture-based viticulture test-beds.


ThermoConc as a Building Envelope for Electricity Generation and Space Heating and Cooling

Research categories:  Material Science and Engineering, Mechanical Systems, Physical Science
School/Dept.: Civil Engineering
Professor: Ming Qu
Preferred major(s): Material engineering or mechanical engineering
Desired experience:   1. Good skills with experiments and data acquisition; 2. Good writing and presentation skills; 3. Solid background in thermoelectric theory.

NSF research project aims to create a new strategy to reduce building energy consumption while enhancing thermal comfort by using thermoelectric concrete envelope to heat or cool indoor space without the need of additional power source. The student will help to characterize the new TE Concrete and evaluate the performance of ThermoConc both theoretically and practically.


Unlocking the role of heat shock proteins in postmortem protein degradation of beef muscles

Research categories:  Agricultural
School/Dept.: Animal Sciences
Professor: Brad Kim
Preferred major(s): Animal Sciences/Food Science/ABE/Biochemistry or closely related
Desired experience:   Previous lab working experience will be desirable.

Providing consistently high quality and wholesome meat products to consumers is crucial to the continued success of the meat industry. The purpose of this research is to determine the role of small heat shock protein (HSP) in postmortem protein degradation of muscles. Anti-apoptotic functions of HSP have been well identified, but its potential impact on endogenous proteolytic enzyme activity is largely unknown. This study will determine the involvement of HSP in postmortem protein degradation of beef muscles. Student will have hands-on experience by performing assays to observe and quantifying the presence of small heat shock proteins present in samples, and interpreting results. Student will assist graduate students in any way needed, especially as is relevant to studies in small heat shock proteins.


Using Polymer Science to Make a Better Dirt Road

Research categories:  Agricultural, Chemical, Civil and Construction, Environmental Science, Material Science and Engineering
School/Dept.: School of Materials Engineering
Professor: Kendra Erk
Desired experience:   Enthusiasm for chemistry and an interest in materials research. Prior experiences with soils would be a benefit to the project but are not required.

The majority of roadways in rural communities and developing countries are unpaved “dirt” roads, which typically become impassable and unsafe during inclement weather. Soil stabilization techniques can be used to increase the strength and durability of dirt roads, including mixing clays, resins, and polymer emulsions with soils to form a high-toughness composite. However, these techniques are only effective over weeks and months – not years – and composite performance is reduced by extreme weather events including droughts and floods. Thus, to increase the safety and well-being of individuals living in isolated communities both in the US and around the world, there is a critical need to design durable, low-cost dirt roads that are resilient to traffic and weather. During the course of this summer project, the SURF student will: (1) learn about the limitations of polymer-based stabilization methods for natural roadways in arid and semi-arid climates; (2) determine how the physical and chemical interactions of polymers in the presence of water, salts, and soils impact the mechanical properties and toughness of polymer-soil composites; and (3) develop material design strategies to create durable and self-healing polymer-based materials and coatings that can be applied to polymer-soil composites and, thus, to natural roadways. Through this project, students will develop knowledge and important skills in organic chemistry and synthesis as well as material design and mechanical testing of composites.


Using Unmanned Aircraft Systems (UAS) to Monitor Crop Development

Research categories:  Agricultural, Computational/Mathematical, Environmental Science
School/Dept.: Agricultural and Biological Engineering
Professor: Keith Cherkauer
Preferred major(s): Agricultural Engineering, Environmental Engineering, Civil Engineering
Desired experience:   Knowledge of programming (e.g., MatLab or Python), electrical circuits, and digital imagery is desired but not required.

Global food production must continue to increase in order to support a growing world population. The integration of data science, sensors and automated sensing platforms into breeding programs allows science and engineering to work together to increase the speed and accuracy of seed selection for future development into commercial products. Unmanned Aircraft Systems (UAS) provide a platform to collect very-high resolution remote sensing image data from fields frequently during the growing season. The student selected for this project will work with an experienced team of graduate students and faculty to collect imagery and supporting ground reference data from multiple crop fields. They will learn how to setup ground targets, collect additional ground reference data (including soil moisture and leaf porosity measurements), manage large datasets, and process imagery to extract metrics per plot, which can be correlated to the specific seed variety in each plot within the field containing each experiment. As part of this project, the student will be responsible for collecting ground reference data and processing UAS imagery. They will use their data to assess the usefulness of one metric used for monitoring crop development that will be selected at the start of the summer.


Using Vesicular Dispersions for Stabilizing Suspensions of Dense Particles Against Sedimentation

Research categories:  Chemical
School/Dept.: Chemical Engineering
Professor: David Corti
Preferred major(s): Chemical Engineering, Chemistry
Desired experience:   Thermodynamics, physical chemistry

For many applications of colloidal dispersions or suspensions, such as inks and paints, the dispersed particles must remain suspended for long times, to maintain their expected performance. While this is often accomplished by preventing the agglomeration (sticking together) of the particles, which remain suspended by Brownian motion, the dense particles that are often used in some inks, may still settle rapidly even if they are prevented from agglomerating. We previously developed a general method for preventing dense particles from settling by using close-packed vesicular dispersions of the double-chain surfactant DDAB (didodecyldimethyl-ammonium bromide). In this project, the SURF student will help investigate the ability of DDAB vesicles prepared at different salt concentrations to stabilize high density particles. In addition, the student will help study the thermophysical properties and phase behavior of DDAB solutions as a function of the salt concentration. Working with a Ph.D. candidate, who specializes in this area, the student will learn various experimental techniques for characterizing colloidal and vesicular dispersions, including densitometry and polarizing light microscopy. The student should have a good understanding of basic thermodynamics and physical chemistry.