Los Angeles, California, United States
I'm an incoming CS PhD student at Columbia University advised by Yunzhu Li. My research focus is developing effective mechanisms for training policies with minimal data and training foundation models that leverage diverse, multimodal data (i.e. tactice sensing, vision, etc.) to enable generalization and robustness in real-world environments.
Incoming Graduate Research Assistant at the RoboPIL Lab. Advised by Yunzhu Li.
Quality diversity optimization in multi-agent domains. Advised by Stefanos Nikolaidis.
Safety and robustness for robot foundation models. Advised by Daniel Seita.
Causality inference for spatiotemporal data. Advised by John Krumm.
QuantSC is an educational student organization at USC for students to explore the intersections of computer science, math, and economics through quantitative finance.
Developed PL/SQL procedures and ETL pipelines (Informatica) to process and upload investment data, with testing, deployment (Azure DevOps), and Agile collaboration.
Worked with ASU and Navajo Technical University to help teachers integrate computer science into classrooms. Developed culturally relevant curriculum and led training sessions.