Hi! I currently work on reduced order ocean/weather/fluid prediction (via neural operators), optical simulations (RCWA), and generative tessellations (via parameterized PINNs). I’m interested in PINNs, L-Conv (based on Lie Algebra), and other parameterized manifold learning approaches that learn the smallest possible, arbitrary resolution network. These networks are generally promising surrogates or inverters for MCMC (Markov-Chain-Monte-Carlo) physics simulations. I also compose music and (try) to develop procedural worlds/games; please see my (yet-to-be-updated) website for more!
Interests:

• Deep Learning Surrogates
• Inverse Problems
• Reduced Order Models
• Uncertainty Quantification
• Generative Modeling