Hi, I am a final-year PhD student advised by Justin Solomon in Geometric Data Processing group. My research interest is in applying geometric tools to tackle problems in optimization, statistics, and computer graphics. I am grateful to have interned at Microsoft Research New England with Lester Mackey and Adobe Research with Noam Aigerman and Vova Kim during my PhD.
Before coming to MIT, I obtained my Bachelor's Degree in Computer Science and Mathematics and my Master's Degree in Mathematics, both at Stanford University. During my time at Stanford, I was fortunate to have worked with Leonidas Guibas and Daniel Bump, among many others.
Research
Self-Consistent Velocity Matching of Probability Flows
Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, LA
Sampling with Mollified Interaction Energy Descent
Conference on Learning Representations (ICLR 2023), Kigali
Learning Proximal Operators to Discover Multiple Optima
Conference on Learning Representations (ICLR 2023), Kigali
Wasserstein Iterative Networks for Barycenter Estimation
Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
Conference on Neural Information Processing Systems (NeurIPS 2021), online
Large-Scale Wasserstein Gradient Flows
Conference on Neural Information Processing Systems (NeurIPS 2021), online
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
Conference on Learning Representations (ICLR 2021), online
Continuous Regularized Wasserstein Barycenters
Conference on Neural Information Processing Systems (NeurIPS 2020), online
Supervised Fitting of Geometric Primitives to 3D Point Clouds
Oral Presentation
Conference on Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA
Branching Rules of Classical Lie Groups in Two Ways
Undergraduate honors thesis. Stanford University, 2018