I am fourth-year PhD candidate at Stanford University in the Robotic Exploration Lab.

My research focuses on game-theoretic optimization and optimization for robots that make and break contact with their environments.
The main applications of my work are planning for autonomous vehicles and robot locomotion.

Learning Objective Functions

We coupled an online estimation technique and a fast dynamic game solver to allow an autonomous driving vehicle to optimize its trajectory while learning the objective functions of the cars in its surroundings. It allows the autonomous vehicle to quickly identify the desired speed, desired lane and aggresiveness level of the vehicles it is interacting with. This gained information further improves the trajectory prediction ability of the autonomous vehicle.

Computing Robustness Guarantees

We developed an algorithm relying on a novel sampling-based technique to efficiently compute regions of finite time invariance, commonly refered to as “invariant funnels”. We apply our algorithm to a spacecraft atmospheric entry problem where the goal is to identify the landing zone that the spacecraft is guaranteed to reach under uncertain atmospheric disturbances.

Solving Dynamic Games

We developed fast solver for constrained dynamic games and applied it to complex autonomous driving scenarios. It generates complex driving behaviors where vehicles negotiate and share the responsibility for avoiding collisions.

Optimizing nonsmooth objective functions

We applied the Alternating Direction Method of Multipliers (ADMM) to solve optimal control problems with L1-norm cost. One application of this work is minimum-fuel trajectory optimization for satellites.

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Contact me at

[my first name] [lc] [at] stanford [dot] edu.