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I’m a first-year Ph.D. student at UMass Amherst in Computer Science (CICS) in the Resource-Bounded Reasoning Lab advised by Shlomo Zilberstein. I was recently a research intern this summer at the Center for Human-Compatible Artificial Intelligence (CHAI) / Berkeley Artificial Intelligence Research (BAIR) at UC Berkeley advised by Stuart Russell and Justin Svegliato on building ethically-compliant autonomous systems using constrained deep reinforcement learning.

Previously, I had some fun and exciting internships at CHAI (Summer-Fall 2022) building a formal framework for computationally bounded agents, Moravian University (Summer 2021) working on extremal and spectral graph theory, and Michigan State University (Summer 2020) working on geometric deep learning.

My research interests lie in deep reinforcement learning, constrained reinforcement learning, bounded optimality, AI safety, and applications to robotics.

Fun Fact: My Erdös number will be a 4




  • Our paper on formalizing an intelligent system as a composition of contract algorithms has been accepted to IROS-2023!


  • I recently graduated summa cum laude from Marist College, majoring in applied mathematics and data science with a minor in computer science!


  • I was awarded the Excellence in Mathematics Award for graduating as the top mathematics student at Marist College.


  • I accepted a computer science MS/PhD offer at UMass Amherst in the Resource-Bounded Reasoning Lab directed by Shlomo Zilberstein!



  • Visiting scholar at the Center for Human-Compatible AI (CHAI) at UC Berkeley on building well-founded AI using metareasoning.


  • Our paper on using combinatorics to solve a combinatorial geometry problem has been accepted to MAA Mathematics Magazine!



  • Visiting scholar at Moravian University as part of an NSF REU on computational methods in discrete mathematics working on spectral and extremal graph theory.



  • Visiting scholar at Michigan State University as part of an NSF REU on experimental mathematics working on geometric deep learning.