About
I work on generative modeling for science: learned priors that help solve inverse problems governed by partial differential equations, where measurements are scarce and the physics is non-trivial.
I am a first-year Ph.D. student in Statistics & Data Science at Yale University, in the Lu Group, advised by Prof. Lu Lu. My recent work pairs diffusion-based priors with PDE-constrained optimization for full-waveform inversion, but I’m broadly interested in how generative models change the way we do inference in scientific computing.
Diffusion models · inverse problems · scientific machine learning · full waveform inversion · PDE-constrained optimization · regularization by denoising