I'm a PhD candidate in Computer Science at the University of Massachusetts Amherst, where I'm fortunate to be advised by Professor Daniel Sheldon. My primary research focus is on differential privacy, specifically making differential privacy practical for real-world applications – developing general-purpose private inference methods and optimizing privacy-utility trade-offs. I'm broadly interested in privacy-preserving machine learning and artificial intelligence.

Before my PhD, I earned an MS in Computational Design at Carnegie Mellon University as a Fulbright Scholar, then worked as a Machine Learning Software Engineer at Cadence in Pittsburgh. During my doctoral studies, I've completed Research and Research Engineering internships at Google (2021), Meta (2022), and LinkedIn (2025), applying my work to production ML systems.

In my free time, I like to surround myself with beauty – whether through live classical music, curated vintage pieces, carefully designed spaces, or the artistry and challenge of FromSoftware games.