I am interested in developing algorithms and systems to enable reliable decision-making in urban and societal systems. Directions of research include sample-efficient reinforcement learning, distribution shift, bridging machine learning and automation science, and automation science in the context of automated vehicles. My research is motivated by the challenge of understanding and shaping the impact of autonomy on society. Ultimately, this research will inform complex decision-making, from automated vehicles and transportation systems to urban planning and public policy.
I am a postdoc with Microsoft Research AI. I will join the MIT faculty in Fall 2019, where I will be part of the Department of Civil and Environmental Engineering (CEE), the Institute for Data, Systems, & Society (IDSS), and the Laboratory for Information & Decision Systems (LIDS).
I recently completed my PhD in EECS at UC Berkeley, working at the intersection of machine learning, optimization, and mobility. My PhD research focused on mixed autonomy systems in mobility, which studies the complex integration of automation such as self-driving cars into existing urban systems. I was advised by Alexandre Bayen, and was part of the Berkeley AI Research Lab (BAIR), California Partners for Advanced Transportation Technology (PATH), the Berkeley Real-time Intelligent Secure Explainable Systems Lab (RISELab), and Berkeley DeepDrive (BDD). Before graduate school, I received a BS and MEng in EECS from MIT, where I worked with Daniela Rus, Seth Teller, and Jim Glass. I have also spent time at OpenAI, Microsoft Research, the Google X Self-Driving Car Team (now Waymo), Dropbox, Facebook, and several startups.