About me

Hello! My name is Sasha and I am an assistant professor in the Department of Mechanical Engineering at Rice University. 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 the reliable control of complex engineering systems. I am fascinated by systems where classical control methods fail to provide the desired performance and am excited about developing new control strategies for these systems. I was awarded my Ph.D. in mechanical engineering at the University of California, Santa Barbara (UCSB) where I was advised by Francesco Bullo. As a Ph.D. student, I was awarded the NSF Graduate Research Fellowship and the UCSB Chancellor’s Fellowship. In Spring 2020, I graduated with a B.S. in mechanical engineering and a B.S. in mathematics from the University of Maryland (UMD).

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.

Recent Updates

• June 2025: I have defended my Ph.D. thesis titled “Contraction Theory in Control, Learning, and Optimization” (pdf) and am starting as an Assistant Professor at Rice University in the Department of Mechanical Engineering!
• June 2025: Our paper “Time-varying convex optimization: A contraction and equilibrium tracking approach,” (link) has been published in the IEEE Transactions of Automatic Control! This is joint work with Veronica Centorrino, Anand Gokhale, Giovanni Russo, and Francesco Bullo.
• 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!