I am a research scientist at Amazon. My research interests are in the areas of control, optimization, and data-based algorithms.

Before joining Amazon, I was a postdoctoral scholar at Caltech, working with Prof. Aaron Ames and Prof. Yisong Yue. My research focused on high-level planning in partially observable environments and on designing control algorithms that allow autonomous systems to perform highly dynamical maneuvers while guaranteeing safety.

During my PhD at the University of California Berkeley, I worked with Prof. Francesco Borrelli in the MPC lab. I developed the Learning Model Predictive Control (LMPC) strategy, which is a model-based policy iteration strategy. This strategy was used to teach an autonomous vehicle how to race!

A talk on my PhD and postdoc research

Learning How to Race Experiments
Listen with audio to hear the tires squealing!

Planning and Control in Partially Observable Environments
The locations of the blue boxes are unknown to the robot