UC Berkeley - PhD in Statistics & Robotics
Harvard University Class of 2018
"ML does ML"

I am a 2nd year PhD student in Statistics with a focus on robotics at UC Berkeley, grateful to be advised by Prof. Claire J. Tomlin and Prof. Zachary N. Sunberg. I am interested in the intersection of control theory, sequential decision making, and machine learning, with applications in robotics.

My research goal is to develop algorithms and systems that enable physical robots to operate intelligently, safely, and efficiently, with provable guarantees.

Currently, my research trajectory consists of two distinct approaches. First, my research aims to develop MDP and POMDP algorithms that tackle realistic assumptions and frameworks - such as having continuous observation and action spaces - which combine provable theoretical convergence guarantees as well as computational efficiency. Second, my research attempts to integrate robust optimal controls with computer vision and machine learning, which enables agents to navigate in unknown environments or in the presence of other agents with unknown intentions.