I am a PhD student in the CSE department at UCSD. I started in Fall 2020 and did my undergraduate degree in computer science and math at Carleton College. My advisor is Yusu Wang and currently, I work on research relating to approximation algorithms for optimal transport, neural networks, and geometric algorithms/problems.

Publications

Neural approximations of Wasserstein distance via a universal architecture for symmetric and factor-wise group invariant functions (S. Chen, Y. Wang, accepted to NeurIPS 2023)

Learning Ultrametric Trees for Optimal Transport Regression (S. Chen, P. Tabaghi, Y. Wang, accepted to AAAI 2024)

The Weisfeiler-Lehman Distance: Reinterpretation and Connetion GNNs (S. Chen, S. Lim, F. Mémoli, Z. Wan, Y. Wang, ICML workshop: Topology, Algebra, and Geometry in Machine Learning (2023))

Weisfeiler-Lehman meets Gromov-Wasserstein (S. Chen, S. Lim, F. Mémoli, Z. Wan, Y. Wang, accepted to ICML 2022)

Approximation algorithms for 1-Wasserstein distance between persistence diagrams (S. Chen, Y. Wang, accepted to SEA 2021)

Links (labmates, collaborators, friends, and enemies)

Sowmya Manojna Narasimha, Dhruv Kohli, Zhengchao Wan, Tristan Brugere, Jesse He, Riley Nerem