About me

I am a Ph.D. candidate in mechanical engineering at the University of California, Santa Barbara (UCSB) where I am advised by Francesco Bullo. My research is on modern data-driven engineering problems at the intersection of control, machine learning, and optimization. I am broadly interested in robust nonlinear control theory and modern machine learning methods to enable reliable control of complex engineering systems. I was awarded the NSF Graduate Research Fellowship in Spring 2021. In Spring 2020, I graduated with a B.S. in mechanical engineering and a B.S. in mathematics from the University of Maryland (UMD). While at UMD, I worked with Yancy Diaz-Mercado on multi-robot coverage control and its applications.

As a Ph.D. student, I interned at Toyota Research Institute. While an undergraduate student at UMD, I worked internships at NASA Goddard Space Flight Center, MIT Lincoln Lab, and The Johns Hopkins University Applied Physics Lab.

Outside of academics, I am a competitive chess player with titles of chess expert and candidate master and a US Chess Federation rating of 2162. I am also an ultramarathon distance runner, having run a 50 mile race (and hoping to run other long-distance events in the future).

Recent Updates

• November 2024: Our paper “Non-Euclidean Monotone Operator Theory and Applications” (link) has appeared in the Journal of Machine Learning Research!
• October 2024: I gave a talk at UC Berkeley on contraction theory, optimization-based control, and imitation learning (slides).
• September 2024: Our paper “Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees” (link) with Sean Jaffe, Deniz Lapsekili, Ambuj Singh, and Francesco Bullo was accepted to NeurIPS!
• June 2024: I am an intern at Toyota Research Institute within their Human Interactive Driving Group. I will be working on problems surrounding robustness to uncertainty.
• December 2023: I am traveling to New Orleans for NeurIPS 2023, where I will give an oral presentation at the Associative Memory & Hopfield Networks workshop based on the paper “Retrieving k-Nearest Memories with Modern Hopfield Networks” (link) which was done in collaboration with Sean Jaffe, Ambuj Singh, and Francesco Bullo.
• June 2023: Our 2022 ACC article “Non-Euclidean Contractivity of Recurrent Neural Networks” (link) with Anton Proskurnikov and Francesco Bullo, received the 2023 O. Hugo Schuck Best Paper Award from the American Automatic Control Council!
• May 2023: We posted a preprint on Contracting Dynamics for Time-Varying Convex Optimization - read about how we can use contraction theory to track optimal trajectories in time-varying optimization problems!