About Me
Hello! I am an Electrical Engineering PhD student at USC, advised by Dr. Stephen Tu, and an NSF Graduate Research Fellow.
I am broadly interested in developing safe and robust methods for learning-based autonomous systems. My current research focuses on deep learning methods inspired by stochastic differential equations for solving partial differential equations, with applications to Hamilton–Jacobi reachability.
Before joining USC, I studied Aerospace Engineering at Embry-Riddle Aeronautical University, with minors in Applied Mathematics and Computer Science, and worked with Dr. Leitao Chen and Dr. Vladimir Golubev.
Outside of USC, I am best spotted pedaling up the hills of LA, searching for fish along riverbanks and lakeshores, and trekking up alpine trails. In the winter months, I migrate to the nearest snowy peaks whenever time and weather permit.
Research interests: Safe autonomy Robust learning Scientific machine learning
Updates
Presenting STEER2REACH at CDC 2026
PaperUSC Ming Hsieh ECE 20th Naming Anniversary
PosterNSF Graduate Research Fellowship
Research PlanPublications
Forward Trajectory Steering for Hamilton-Jacobi Reachability Analysis
Forward trajectory steering guides collocation sampling for learning Hamilton–Jacobi safety value functions.
Integration Matters for Learning PDEs with Backward SDEs
Stochastic Heun integration addresses discretization bias in BSDE-based deep PDE solvers.
Application of Reinforcement Learning for Autonomous Dynamic Soaring
Reinforcement learning for UAV maneuvers that extract energy from wind gradients.