Research Experience for Undergraduates (REU) Research Projects

Privacy-Preserving Sharing of Correlated Data

Local Differential Privacy (LDP) is a state-of-the-art framework for preserving individual privacy during data sharing with untrusted data collectors, making it a promising technology for privacy-preserving sharing of sensitive data types such as location or graph data. By perturbing data before sharing, LDP provides plausible deniability for individuals, ensuring that private details remain confidential. To date, only a limited set of tasks—such as frequency estimation, heavy hitters, frequent itemset mining, marginal release, and range queries—have been demonstrated using LDP.

This project aims to explore the applicability of existing LDP techniques to graph data, with a focus on enhancing privacy in graph sharing. Graph data, commonly used in domains like social networks, healthcare, and finance, represents entities as nodes (e.g., people, devices) and their interactions or relationships as edges. Sharing these graphs directly can expose sensitive information, so privacy-preserving techniques are critical. Controlled randomness can protect the privacy of individuals while maintaining essential patterns within the graph data. Through this project, students will gain insight into privacy-enhancing technologies and methods for secure data sharing. Additionally, they will acquire hands-on experience in programming by implementing state-of-the-art privacy-preserving techniques.

Understanding Student Behavior While Using Generative AI for Coding

Generative AI tools, such as ChatGPT, have become increasingly popular among students for educational purposes. A recent study reported that approximately 40% of college students have used ChatGPT as part of their higher education experience. Generative AI can be highly beneficial in learning difficult concepts. However, it also poses risks of misuse, such as completing assignments such as essays and writing computer programs. This is particularly concerning in Computer Science, where Generative AI can produce complex code in response to user prompts. This research aims to examine students' physiological responses as they engage with Generative AI for writing computer programs.

In the first phase, the research will focus on designing a study to collect data in a laboratory setting. Study participants will be asked to generate Python programs of varying complexity, both with and without the assistance of Generative AI. Throughout the study, physiological responses - such as eye-gaze movement, body motion, and facial expressions - will be recorded. In the next phase of the research, machine learning algorithms will then be developed to identify physiological patterns associated with the use of Generative AI tools.

Through this project, students will gain exposure to computer science research, including data analysis, algorithm development, model evaluation, and results presentation. Additionally, they will develop soft skills such as teamwork, problem-solving, and adaptability to different approaches.

Utilizing Interpolation Functions to solve Hyperbolic Systems

A shock tube is a device to understand shock waves and other examples of gas dynamics that are waves. These waves can be expressed mathematically by Euler’s equation, which is an example of a hyperbolic system. Hyperbolic systems are a challenge because there are nonlinearities in their boundary conditions and these systems can be solved by various means to include interpolation functions.

I’m working on a book, Fundamentals of Shock Waves, and this work supports the book and also an experimental apparatus I hope to design, build and use for experimentation. This work is also important to further the understanding of gas dynamics around Euler’s equation and the use of interpolation functions in solving differential equations.