Resilient Behavior Through Optimization

I am a final-year PhD candidate at MIT, advised by Pablo Parrilo and Russ Tedrake, working at the intersection of mathematical optimization, robotics, and reliable autonomy. My research develops scalable algorithms for high-performance decision-making, particularly in robotics.

I am interested in the entire decision-making pipeline, from high-level modeling, high-performance solver implementations, and low-level linear algebra to enable faster optimal decisions. I specialize in semidefinite programming and sums-of-squares optimization and development of convex optimization solvers. A central theme in my work is turning mathematical structure into computation. I build algorithms that exploit polynomial, conic, and graph structure to make difficult problems tractable, and am committed to distributing robust, open-source implementations of my methods.

I wrote CCosmo, a C++ first-order conic solver family, and VEGA, a decomposition solver for Graphs of Convex Sets. I am also an active contributor to Drake’s optimization, geometry, and planning stack. I am interested in industrial research roles where rigorous optimization, scalable algorithms, and high-quality software can push the frontier of AI, robotics, autonomy, and scientific computing.