I research machine learning methods for decision making under uncertainty at the Toyota Research Institute.
I previously completed a master’s degree in computer science at Stanford, where I focused on reinforcement learning and decision making as part of the Stanford Intelligent Systems Laboratory advised by Mykel Kochenderfer.
During my master’s, I interned at Adobe Research where I was advised by Hung Bui. We performed research around imitation learning and its applications in artistic domains.
As an undergraduate student I studied computer science at Vanderbilt University, where I performed autonomous UAV research with my advisor Julie Adams as part of the Human-Machine Teaming Lab. I also worked with Eugene Vorobeychik on a project dealing with social interaction analysis, which is how I originally became interested in machine learning.
MS in Computer Science, 2017
BSc in Computer Science, 2014
We propose a method for quantifying the similarity of learned reward functions without performing policy learning and evaluation.
One potential method for validating autonomous vehicles is to evaluate them in a simulator. For this to work, you need highly realistic models of human driving behavior. Existing research learned human driver models using generative adversarial imitation learning, but did so in a single-agent environment. As a result, the model fails when you execute many of the learned policies simultaneously. This research performs training in a multi-agent setting to address this problem.
This research considers the problem of predicting whether a car will suffer a collision in the time period 10-20 seconds in the future. We formulate this task as policy evaluation in a MDP with a high-dimensional, continuous state space, and a reward function dominated by rare events (collisions). We then demonstrate that simulated data and domain adaptation models can be used to improve prediction performance on real-world data.
How can UAVs with different collision avoidance strategies coordinate maneuvers so as to minimize collisions? This research presents an approach that enforces reasonable requirements on the behavior of UAVs, and as a result dramatically improves safety in dangerous encounters. The method is essentially to ensure that UAV maneuvers align with the directions of those advised by an optimal joint solution.