I received my B.S. at the University of Illinois at Urbana-Champaign (UIUC) in Aerospace Engineering in May 2004. During my years at UIUC, I worked in the Vibrations Lab, the Electric Propulsion Lab, and then interned at Boeing in Seal Beach, CA. Currently I am pursuing my M.S. at UCLA in Aerospace Engineering while working with Professor Emilio Frazzoli. My research is described below.

My research focuses on the design of efficient motion planning algorithms for autonomous vehicle systems. Some of the problems we address are NP-hard and call for approximation solutions. Specifically, we seek to construct upper bounds that are readily available for application, and are proven to be optimal, or in some cases, within a constant-factor of the optimal. Geometry, optimal control, and probability theory are some of the underpinnings of our constructions, in addition to the rich combinatorial algorithms literature.

Numerical and graphical simulations of our algorithms are created to provide a demonstration of their results. Furthermore, the ARES Lab is working with several testbeds: a set of miniature cars, and a set of UAVs. Some data and video of the UAVs is available at the bottom of Chung Hsieh's site.

1. J.J. Enright and E. Frazzoli. The Stochastic Traveling Salesman Problem for the Reeds-Shepp Car and the Differential Drive Robot. In IEEE Conf. on Decision and Control, 2006. Note: Submitted.


2. J.J. Enright, E. Frazzoli, K. Savla, and F. Bullo. On Multiple UAV Routing with Stochastic Targets: Performance Bounds and Algorithms. In Proc. of the AIAA Conf. on Guidance, Navigation, and Control, San Francisco, CA, August 2005. [PDF]


3. J. J. Enright and E. Frazzoli.UAV Routing in a Stochastic, Time-Varying Environment. In Proc. of the IFAC World Congress, Prague, Czech Republic, July 2005.[ PDF ]

"Our philosophy on the design and exposition of algorithms is nicely illustrated by the following analogy with an aspect of Michelangelo's art. A major part of his effort involved looking for interesting pieces of stone in the quarry and staring at them for long hours to determine the form they naturally wanted to take. The chisel work exposed, in a minimalistic manner, this form. By analogy, we would like to start with a clean, simply stated problem (perhaps a simplified version of the problem we actually want to solve in practice). Most of the algorithm design effort actually goes into understanding the algorithmically relevant combinatorial structure of the problem. The algorithm exploits this structure in a minimalistic manner." -Vijay Vazirani