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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 2017 Research Symposium Abstracts.

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:

Computer Engineering and Computer Science


Characterization of strain localization and associated failure of structural materials

Research categories:  Aerospace Engineering, Computational/Mathematical, Computer Engineering and Computer Science, Material Science and Engineering, Mechanical Systems
School/Dept.: School of Aeronautics and Astronautics
Professor: Michael Sangid
Preferred major(s): AAE, MSE, ME, CS

The research we do is building relationships between the material's microstructure and the subsequent performance of the material, in terms of fatigue, fracture, creep, delamination, corrosion, plasticity, etc. The majority of our group’s work has been on advanced alloys and composites. Both material systems have direct applications in Aerospace Engineering, as we work closely with these industries. We are looking for a motivated, hard-working student interested in research within the field of experimental mechanics of materials. The in situ experiments include advanced materials testing, using state-of-the-art 3d strain mapping. We deposit self-assembled sub-micron particles on the material’s surface and track their displacement as we deform the specimen. Coupled with characterization of the materials microstructure, we can obtain strain localization as a precursor to failure. Specific projects look at increasing the structural integrity of additive manufactured materials and increasing fidelity of lifing analysis to introduce new light weight materials into applications.


Code Optimization and GUI Development for DHM-WM Hydrologic Model

Research categories:  Computer Engineering and Computer Science, Environmental Science
School/Dept.: ABE
Professor: Margaret Gitau
Preferred major(s): Computer Engineering, Computer Science
Desired experience:   Python, GUI design, programing, and testing

The hydrologic model DHM-WM was developed to provide spatial information on hydrologic components for determining critical pollutant source areas. The spatial details provided by the model will help in the development of precise and cost-effective watershed management solutions. A great advantage of DHM-WM is in its simplicity and the small number of parameters that require calibration. However, depending on watershed configuration and computational capacity, DHM-WM can take about 1.5 hours per simulation year this being largely due to the use of ArcGIS functions in Python scripts and the sequential algorithm used in the programing. The goal of this project is to enhance DHM-WM to enable its use by a broad range of users. Specifically to: 1) improve DHM-WM’s computational efficiency by modifying the algorithms and optimizing its code; and, 2) to provide a GUI to facilitate model use.


Continuous Analysis of Many CAMeras (CAM2)

Research categories:  Computer Engineering and Computer Science
School/Dept.: Electrical and Computer Engineering
Professor: Yung-Hsiang Lu
Preferred major(s): ECE, CS
Desired experience:   ECE 264 or CS 240

This project develops the technologies to analyze real-time images and video streams from hundreds of cameras. The purpose is to detect anomaly (such as traffic accident) or emergency (such as a natural disaster). The participating students will learn computer vision, machine learning, computer system management.

More information:


Designing and testing vagal nerve stimulation with magnetic resonance imaging

Research categories:  Bioscience/Biomedical, Computer Engineering and Computer Science
School/Dept.: Biomedical Engineering, Electrical and Computer Engineering
Professor: Zhongming Liu
Preferred major(s): Electrical and Computer Engineering, Biomedical Engineering, or Biological Sciences
Desired experience:   As in the project description, a student may work on medical device design or animal experimentation. Medical Device The responsibility is designing mechanical apparatus or electric circuits in an MRI-integrated neural stimulator. A strong candidate should have strong background or interest in analog/digital circuit design and analysis, device fabrication and testing. A student in electrical and computer engineering, biomedical engineering is of particular interest. Animal Experimentation The responsibility is to perform animal experiments with cutting-edging neurotechnologies including 7-Tesla small-animal MRI, multi-channel in vivo electrophysiology, simultaneous neural stimulation, recording, and imaging. The student will be trained for animal handling, injection, and surgery. A student in biological sciences or biomedical engineering is of particular interest.

Vagal nerve stimulation is a potential way to treat various diseases and promote learning. For example, electrical stimulation to the vagus may put inflammation under control, or allow animals to learn how to walk out of a maze.

The laboratory of integrated brain imaging, along with several other labs at Purdue, is designing and optimizing new stimulators for vagal nerve stimulation, and using magnetic resonance imaging to test the designed stimulators in live rodents. This research is expected to lay the technical and physiological foundation to translation of vagal nerve stimulation to humans.

Depending on her or his background, the student can participate in either device design or animal experimentation. The student is expected to also engage in collaborative research across multiple laboratories.


Hololens Augmented Reality based Ultrasound Imaging

Research categories:  Bioscience/Biomedical, Computational/Mathematical, Computer Engineering and Computer Science, Industrial Engineering
School/Dept.: Industrial Engineering
Professor: Juan Wachs
Preferred major(s): CS, ECE, ME, IE
Desired experience:   Very good programming skills. Experience in computer graphics and vision is an advantage.

The project consists of using an ultrasound on a patient simulator and observe the medical imaging on an augmented reality headset (Hololens). This information will be used for teleconsultation.


Interconnecting Blockchains and Cryptocurrencies

Research categories:  Computer Engineering and Computer Science
School/Dept.: Computer Science
Professor: Aniket Kate
Preferred major(s): CS, CE
Desired experience:   Cryptography, Distributed Systems, Security/Privacy

Cryptocurrencies such as Bitcoin and Ethereum have emerged as a paradigm shift for the way payment systems work today. Cryptocurrencies rely on the blockchain, a technology that has been proven useful in a vast number of applications other than monetary transactions. Many companies today are tailoring the blockchain technology to their business logic and successfully developing applications for credit settlement networks, supply chain, IoT and beyond. However, these separate efforts are leading to incompatible individual systems. This contrasts with our highly interconnected world and it is inevitable to see that soon these blockchains will need to operate with each other, effectively forming a network of blockchains where transactions can flow through a sequence of blockchains, similar how the network of networks (i.e., the Internet) works today. In this project, we will design and evaluate the tools required to move money the same way as the information moves today, therefore enabling the Internet of Value.


Network for Computational Nanotechnology (NCN) / nanoHUB

Research categories:  Chemical, Computational/Mathematical, Computer Engineering and Computer Science, Electronics, Material Science and Engineering, Mechanical Systems, Nanotechnology, Other
Professor: NCN Faculty
Preferred major(s): Electrical, Computer, Materials, Chemical or Mechanical Engineering; Chemistry; Physics; Computer Science; Math
Desired experience:   Serious interest in and enjoyment of programming; programming skills in any language. Physics coursework.

NCN is looking for a diverse group of enthusiastic and qualified students with a strong background in engineering, chemistry or physics who can also code in at least one language (such as Python, C or MATLAB) to work on research projects that involve computational simulations. Selected students will typically work with a graduate student mentor and faculty advisor to create or improve a simulation tool that will be deployed on nanoHUB. Faculty advisors come from a wide range of departments: ECE, ME, Civil E, ChemE, MSE, Nuclear E, Chemistry and Math, and projects may be multidisciplinary. To learn about this year’s research projects along with their preferred majors and requirements, please go to the website noted below.

If you are interested in working on a nanoHUB project in SURF, you will need to follow the instructions below. Be sure you talk about specific NCN projects directly on your SURF application, using the text box for projects that most interest you.

1) Carefully read the NCN project descriptions (website available below) and select which project(s) you are most interested in and qualified for. It pays to do a little homework to prepare your application.

2) Select the Network for Computational Nanotechnology (NCN) / nanoHUB as one of your top choices.

3) In the text box for Essay #2, where you describe your specific research interests, qualifications, and relevant experience, you may discuss up to three NCN projects that most interest you. Please rank your NCN project choices in order of interest. For each project, specify the last name of the faculty advisor, the project, why you are interested in the project, and how you meet the required skill and coursework requirements.

For more information and examples of previous research projects and student work, click on the link below.


Purdue AirSense: An Air Pollution Sensing Network for West Lafayette

Research categories:  Agricultural, Chemical, Civil and Construction, Computer Engineering and Computer Science, Electronics, Environmental Science, Innovative Technology/Design, Mechanical Systems, Nanotechnology, Physical Science
School/Dept.: Civil Engineering
Professor: Brandon Boor
Preferred major(s): The position is open to students from all STEM disciplines.
Desired experience:   Proficient in Python, Java, MATLAB; experience with Raspberry Pi or Arduino.

Air pollution is the largest environmental health risk in the world and responsible for 7 million deaths each year. We are presently developing a new air pollution sensing network for the Purdue campus to monitor and analyze air pollutants in real-time. We are recruiting an undergraduate student to assist with the development of our Raspberry Pi-based air quality sensor module. You will be responsible for integrating the Raspberry Pi with air quality sensors, developing laboratory calibration protocols, building an environmental enclosure for the sensors, creating modules on our website for real-time data analysis and visualization, and maintaining state-of-the-art aerosol instrumentation at our central air quality monitoring site at the Purdue Agronomy Center for Research and Education (ACRE).


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

Research categories:  Agricultural, Aerospace Engineering, Computer Engineering and Computer Science, Electronics, Environmental Science, Physical Science
School/Dept.: AAE
Professor: James Garrison
Preferred major(s): ECE, Physics, Geophysics, With appropriate coursework: AAE, ABE, Civil, Geomatics,
Desired experience:   Signal processing; Programming: C, Python, MATLAB; Electronic hardware experience preferred; Drivers license and access to car required.

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 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 in the Purdue Agricultural research fields is being planned. 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.

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. Preference will be given to students who have an interest in applying their skills to solving problems in the Earth sciences, environment, or agriculture.

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


Restructuring computer systems software for the IoT era

Research categories:  Computer Engineering and Computer Science
School/Dept.: ECE
Professor: Felix Lin
Preferred major(s): Any -- as long as you are interested in computing
Desired experience:   Strong passion in programming and hacking

While the mobile computers are still flourishing, we are quickly embracing a variety of new computing platforms, such as wearable devices, IoT, and augmentation reality headsets. These platforms challenge multiple fundamental assumptions made by today’s system software. In this project, you will involve in redefining the operating systems (OS) for these computing platforms so they can be smarter, faster and cooler.

This project will give you a lot of fun in hacking of OS, hypervisor, and various modern hardware.


Self-Learning Mobile Hydraulic Equipment

Research categories:  Agricultural, Computer Engineering and Computer Science, Educational Research/Social Science, Mechanical Systems
School/Dept.: Agricultural & Biological Engineering/Mechanical Engineering
Professor: Monika Ivantysynova
Desired experience:   Senior, MATLAB, statistics, Excel, proficiency in presentation skill, and a basic understanding of instrumentation. A knowledge in hydraulics is a plus.

Failures rarely occur at convenient times, especially on mobile equipment, such as excavators, tree skidders, agricultural tractors, mining equipment, airplanes, etc. Hydraulic failures in the field often cause costly repairs that also result in significant machine downtime. The failures can potentially be life-threatening. Manufacturers and equipment operators desire a solution to predict failures before they occur. This area of research is known as prognostics. The machine compares real-time data and stored data to determine the “health” of the hydraulic pumps and motors. The SURF student would assist the graduate student mentor in collecting machine data from mobile hydraulic machines and create an algorithm to determine the “healthy” state of the hydraulic pumps and motors. Data analysis, data clustering, and machine self-learning are topics that will be used in this research.

Please note: Research lab location is in Lafayette. Student is responsible for their own transportation.


Stochastic Storm Generation of Storms and Their Inner Structure

Research categories:  Agricultural, Civil and Construction, Computer Engineering and Computer Science, Environmental Science
School/Dept.: Agricultural & Biological Engineering
Professor: Bernie Engel
Preferred major(s): Agricultural engineering, environmental engineering, computer science

Advanced field and watershed scale hydrologic models for engineering design, soil erosion, land use planning, and global-change research require detailed continuous temporal and spatial inputs of precipitation to execute the hydrologic processes integrated into their formulations. Accurate estimates of processes such as infiltration, runoff routing, and water quality algorithms need precipitation values on the order of minutes apart. In the United States, the National Oceanic and Atmospheric Administration (NOAA) collects 15-min time increment precipitation data in ~2000 locations. However, observed precipitation is yet rarely available in many sites and lack spatial coverage. In ongoing research, a stochastic storm generator developed at Purdue University allows generating storm characteristics such as inter-event time, duration, and volume, as well as within-storm intensities using the available 15-min resolution data. The current project proposes to extend the application of the current version of the storm generator from a single station to a more detailed network of meteorological stations. The final goal seeks to perform a test of available interpolation method between the statistical parameters defining the available locations so that time series of precipitation data in ungauged areas can be generated.


1. Collect short-time increment precipitation from NOAA and other sources. The SURF student will learn how to search available precipitation data available in the different agencies.
2. Organize and run a clean-up data analysis. The SURF student will deal with different files containing precipitation data and formats as well as its spatial representation by GIS tools.
3. Identify independent storms over the time period. The SURF student will be able to learn how to run Python, MATLAB, and R scripts and to understand the concepts defining independent rainfall events.
4. Fit storm characteristics (time between storms, duration, and volume) to a suitable storm distribution. The SURF student will be able to perform statistical distribution fitting and how to measure the goodness of fit of the available procedure in the storm generator.
5. Generate correlated storm characteristics by Monte Carlo numerical simulation implemented in a stochastic storm generator develop at the National Soil Erosion Research Laboratory (NSERL). The SURF student will experience the use of complex mathematical algorithms incorporated into the storm generator.
6. Characterize storm patterns of the observed storms.
7. Identify representative patterns of storms by cluster analysis over the storm patterns data. The SURF student will explore the concept of machine learning and cluster analysis.
8. Generate storms patterns by Monte Carlo numerical simulation also implemented in a stochastic storm generator develop at the NSERL. The SURF student will continue experiencing the use of complex mathematical algorithms incorporated into the storm generator.
9. Propose an interpolation method of the storm parameters between the stations previously analyzed. The SURF student will apply available spatial interpolation methods in precipitation statistical parameters.


Virtual Reality Robotic Model using Gaming Technologies

Research categories:  Bioscience/Biomedical, Computational/Mathematical, Computer Engineering and Computer Science, Industrial Engineering
School/Dept.: Industrial Engineering
Professor: Juan Wachs
Preferred major(s): ECE, CS
Desired experience:   Very good programming skills

The student will have to develop an environment that can be visualized with the VIBE wearable headset and in which he can control a virtual and real robot to grasp objects and move around the environment.