2021 Research Projects
Projects are posted below; new projects will continue to be posted. To learn more about the type of research conducted by undergraduates, view the archived symposium booklets and search the past SURF projects.
This is a list of research projects that may have opportunities for undergraduate students. Please note that it is not a complete list of every SURF project. Undergraduates will discover other projects when talking directly to Purdue faculty.
You can browse all the projects on the list or view only projects in the following categories:
Big Data/Machine Learning (25)
4D Materials Science - X-ray Microtomography, Image Analysis, and Machine Learning
More information: https://engineering.purdue.edu/MSE/people/ptProfile?resource_id=239946
Accelerator Architecture Lab at Purdue (AALP): Optimizing Simulators for Advanced Processor Development
More information: https://accel-sim.github.io
Group Website: https://engineering.purdue.edu/tgrogers/group/aalp.html
More information: https://engineering.purdue.edu/tgrogers/
Accelerator Architecture Lab at Purdue (AALP): Modeling Diverse GPU Architectures in C++ Simulation
More information: https://accel-sim.github.io
Group Website: https://engineering.purdue.edu/tgrogers/group/aalp.html
More information: https://engineering.purdue.edu/tgrogers/
Advanced Vehicle Automation and Human-Subject Experimentation
The goal of this project is to describe and measure the ways in which a person interacts with advanced vehicle automation. Students will assist with multiple activities and will learn a combination of the following: how to a) develop/code advanced driving simulation scenarios, b) collect driving performance data, c) analyze driver and performance data (using methods via software packages), and d) write technical reports and/or publications. Students may also gain experience collecting and analyzing complementary physiological measures, such as eye movement data, brain activity, skin conductance, and heart rate. The students will work closely with graduate student mentors to enhance learning.
More information: https://engineering.purdue.edu/NHanCE
Advancing Pharmaceutical Manufacturing through Process Modeling and Novel Sensor Development
The flexibility of continuous processes can reduce wasted materials and facilitate scale-up more easily with active plant-wide control strategies. Ultimately, this results in cheaper and safer drugs, as well as a more reliable drug supply chain.
To fully realize the benefits of continuous manufacturing, it is important to capture the dynamics of the particulate process, which can be more complex than common liquid-based or gas-based chemical processes. In addition, effective fault detection and diagnostic systems need to be in place, so intervention strategies can be implemented in case the system goes awry.
All of these require the development of process models that leverages knowledge of the process and big data. Students in this part of the research would have a chance to gain experience in industry-leading software for process modeling (e.g. Simulink, gProms, OSI PI) and machine learning (e.g. Matlab, Python, .NET).
Most importantly, they would be able to test the models in Purdue's Newly Installed Tablet Manufacturing Pilot Plant at the FLEX Lab in Discovery Park.
Another important aspect of the research are sensors. In this project, we will be investigating the feasibility of two novel sensors: a capacitance-based sensor to measure mass flow, and a particle imaging sensor that directly captures images of the powder particles to give you a particle size distribution. We will be testing these sensors together with NIR and Raman sensors, and use data analytics to determine their feasibility of application in a drug product manufacturing process.
Analyzing educational teamwork dataset using quantitative and NLP techniques
More information: https://info.catme.org/
Automatically Detecting and Fixing Software Bugs and Vulnerabilities
Earlier work can be found here: https://www.cs.purdue.edu/homes/lintan/publications/deeplearn-tse18.pdf
More information: https://www.cs.purdue.edu/homes/lintan/
Describing the collective motion of dislocations in metals
We have two projects available, please apply for this position if you are interested in either one.
• One project will involve simulating dislocations in face centered cubic metals to extract statistical information about how they form junctions. This junctions are the physical basis of work-hardening, and this statistical information will allow us to incorporate junctions into the density-based, fluid-like model.
• Another project will involve simulating x-ray diffraction patterns in face-centered cubic metals containing dislocations in order to identify signals relevant to the fluid-like properties of the dislocations. Basic machine learning techniques will be used to identify these signals. No experience with x-ray diffraction or machine learning is needed. These results will allow experimentalists at our national labs to measure the fluid-like properties of dislocations in a lab rather than through simulations.
More information: Not yet
Developing Computational Methods to Classify Unlabeled Reactions Using Large Data Sets
More information: https://cistar.us/
Developing IoT sensors for real-time concrete strength monitoring
In this work, a novel EMI method for concrete modulus measurement will be reported. This novel NDT method can extract the dynamic modulus of concrete cylinder using only one PZT sensor. The specific activities include: (a) embedding PZT sensor in cylinder mold; (b) casting concrete in mold; (c) measuring the electrical impedance spectrum of sensor; (d) reading the resonance frequencies of the spectrum in low frequency band and (e) calculating the modulus using resonance frequencies. The orientation of sensor, the sensing range and the repeatability between different sensors will be discussed in this project. The investigation of the nature of EMI sensor-structure interaction has a broad interest to NDT and piezoelectric material community.
More information: https://engineering.purdue.edu/SMARTLab
Efficient and renewable water treatment
More information: www.warsinger.com
Enhancing Human-Robot Interaction Using Wearable Technologies
Epidemic Analysis Via Social Networks
More information: https://sites.google.com/view/philpare/home
Epidemic Modeling and Prediction with COVID-19 Dataset
There are abundant well-organized Covid-19 datasets available online, including the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. By leveraging these datasets, we plan to design a project-based learning experience that participants will model and predict epidemic spread over a nine-week schedule. The project includes five major stages: 1) data collection, 2) model selection, 3) parameters optimization, 4) model verification, and 5) prediction. The participants will learn to model and analyze epidemic processes with compartmental models, and they will get the first-hand experience using a programming language of their choice to implement the modeling, optimization, and prediction pipeline.
More information: https://sites.google.com/view/philpare/home
Human Factors: Enhancing Performance of Nurses and Surgeons
The SURF student will participate in data collection in the operating room at Indiana University School of Medicine, data analysis and interpretation, and write his/her results for a journal publication. The student will regularly communicate his/her progress and results with faculty, graduate mentors, and surgeon collaborators.
More information: https://engineering.purdue.edu/YuGroup
IoT4Ag P3: Biophysical modeling and integration with in-situ and remotely sensed data
A new Engineering Research Center on the Internet of Things for Precision Agriculture (IoT4Ag) has recently been established to ensure food, energy, and water security by advancing technology to increase crop production, while minimizing the use of energy and water resources and the impact of agricultural practices on the environment. The center will create novel, integrated systems that capture the microclimate and spatially, temporally, and compositionally map heterogeneous stresses for early detection and intervention to better outcomes in agricultural crop production. The Center will create internet of things (IoT) technologies to optimize practices for every plant; from sensors, robotics, and energy and communication devices to data-driven models constrained by plant physiology, soil, weather, management practices, and socio-economics. We are looking to hire a cohort of SURF students to work on different activities in the center.
IoT4Ag P3: Biophysical modeling and integration with in-situ and remotely sensed data
# of students: 3, US Citizens or permanent residents only
This interdisciplinary project will focus on acquisition and processing of remotely sensed data acquired by sensors on UAVs and wheel-based vehicles, developing empirical models, and working collaboratively with teams in the College of Agriculture to integrate empirical machine learning models with biophysical modeling to detect plant stress and predict yield. The project will provide opportunities for students to learn about sensors via field-based data acquisition from remote sensing platforms, expand their understanding of techniques for processing data, use data products for applications related to cropping systems (plant breeding, production management, in-season treatments) and engage in development of hybrid models that include both data analytics and biophysically based approaches. Use of existing models may require use of APIs for data acquisition, familiarity with file types, and aptitude for functions and systems thinking.
The project will involve both field-based and computer laboratory focused research. Courses /experience in python programming, data analytics and image processing, and particularly related to remote sensing technologies, are desirable. Interest in interdisciplinary research is essential.
More information: iot4ag.us
Lithium-ion Battery Analytics
More information: https://engineering.purdue.edu/ETSL/
Measuring wetland greenhouse gas emissions with environmental Internet of Things sensors.
The student working on this project would be responsible for deploying gas sensors, which will involve fieldwork at wetlands located near Purdue. This student will also have the opportunity to analyze the data collected from these sensors with the assistance of faculty and graduate student mentors.
More information: http://www.ecosystemscience.io
Mobile Air Quality Sensors and the Internet of Things
More information: https://www.purdue.edu/discoverypark/arequipa-nexus/en/index.php
On-Line Programming Assessment
Real time analysis of viral particles for continuous processing approach
More information: https://engineering.purdue.edu/ComplexFlowLab/
Reliable Deep Learning Software
Machine learning systems including deep learning (DL) systems demand reliability and security. DL systems consist of two key components: (1) models and algorithms that perform complex mathematical calculations, and (2) software that implements the algorithms and models. Here software includes DL infrastructure code (e.g., code that performs core neural network computations) and the application code (e.g., code that loads model weights). Thus, for the entire DL system to be reliable and secure, both the software implementation and models/algorithms must be reliable and secure. If software fails to faithfully implement a model (e.g., due to a bug in the software), the output from the software can be wrong even if the model is correct, and vice versa.
This project aims to use novel approaches including differential testing to detect and localize bugs in DL software (including code and data) to address the testing oracle challenge. Good programming skills and strong motivation in research are required. Background in deep learning and testing is a plus.
More information: https://www.cs.purdue.edu/homes/lintan/
Resilient Extraterrestrial Habitat Engineering
The testbed will consider a habitat system and will aim to emulate the extreme temperature fluctuations that happen in deep space. To achieve this goal, a thermal transfer system is being developed, consisting of a chiller, an array of glycol lines, in-line heaters, actuated valves, and a series of sensors. Operated under a tuned controller, the thermal transfer system can cool or heat a certain surface area of the structure of the habitat to maintain a given temperature. However, to fully control the thermal transfer system is not straightforward. One of the critical challenges is its deep uncertainty, which results from inaccurate or long-delay sensors, variant test setup, complex controller design, etc. Therefore, a systematic study is needed to quantify the uncertainties to facilitate the thermal transfer system development. Emulation of a particular scenario considering a meteoroid impact will be performed, with random variations in the location and size of the impact and resulting consequences.
We are looking for undergraduate students to play key roles in this project, under the guidance of a graduate student and faculty members. The students are also expected to prepare a poster presentation on the results, and author a research paper if the desired results are achieved. Participating undergraduate researchers would be tasked to focus on the following research projects:
• Stochastic model for analyzing and exploring the behavior variability of the thermal transfer system, functioning in different scenarios.
• Experimental study to calibrate the developed model, involving parametric identification of the transfer system and experimental validation of the stochastic model.
• Numerical and experimental studies to detect and localize meteoroid impact and damage to the structure and other subsystems of the habitat, and use that information to make decisions regarding emergency actions to take.
• Numerical investigations to understand the limitations of fault damage detection methods when incomplete or erroneous sensor data is available.
More information: https://www.purdue.edu/rethi/
Smart Water for Smart Cities
Virtual Reality animations of blood flow in a vessel network
More information: https://engineering.purdue.edu/gomez/