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

• December 2024: Our paper “Euclidean Contractivity of Neural Networks With Symmetric Weights” (link) with Veronica Centorrino, Anand Gokhale, Giovanni Russo, and Francesco Bullo was awarded the 2024 IEEE Control Systems Letters Outstanding Paper Award!
• 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!