Research Trajectory
My research is dedicated to building bridging the gap between classical mathematical analysis and modern deep learning. We build systems where safety and performance are provable.
Optimal Control & Uncertainty
Formulating new control laws for agents operating in non-stationary and highly uncertain environments. This work integrates HJB equations with deep reinforcement learning to provide robust decision-making frameworks.
Physics-Informed Architecture
Architecting the next breed of generative models that inherently respect Maxwell's equations, fluid dynamics, and spectral symmetries. We move beyond data-fitting to true physical consistency.
High-Performance Visualization
Developing custom kernels using WebGL and WebGPU to provide real-time, interactive exploration of high-dimensional manifold data.