Honored to recieve Predoctoral Honorable Mention from UConn Dept. of Comp. Sci, and Synchrony Fellowship for Fall 2025.

I am honored to have recieved the Predoctoral Honorable Mention from my department (UConn Dept. of Comp. Sci), and the esteemed Synchrony Fellowship for Fall 2025! Below is a description of my research which the Synchrony Fellowship will enable me to perform!

What practical impact will your research eventually have?

My research focuses on computer vision security, where I apply deep learning techniques to image data and investigate how to make these systems more robust and trustworthy. As AI systems are increasingly deployed in high-stakes domains like autonomous driving, facial recognition, and election technologies, ensuring their security is critical. Specifically, I’m currently working on a project with the UConn Voter Center, identifying vulnerabilities in machine learning systems used in voting systems. The ultimate goal of this work is to understand how these systems can fail — and to develop strategies that make them more secure, transparent, and resilient to attacks. In the long term, this research will help inform the design of safe, trustworthy AI in democratic infrastructure.

What do you think is the most pressing need for improving cybersecurity?

In today’s fast-moving AI landscape, one of the most pressing needs is ensuring that machine learning systems — especially those used in sensitive and critical applications — are not just performant, but also secure, interpretable, and robust to adversarial manipulation. As my work in election security highlights, it’s not enough for AI to be accurate under ideal conditions — we need it to behave reliably under stress, be resistant to attacks, and remain transparent to stakeholders. Building this kind of resilience into AI systems is key to earning public trust and preventing serious real-world failures and is a core focus of my research.