About

Welcome! I am a final-year Ph.D. candidate in Computer Science at Vanderbilt University, advised by Dr. Soheil Kolouri. I’m passionate about developing machine learning methods that unite theoretical rigor with real-world impact. My current research focuses on enhancing the efficiency and robustness of Optimal Transport in machine learning applications, and on extending classical formulations to non-Euclidean settings. Prior to starting my Ph.D., I obtained an M.S. in mathematics from Vanderbilt University in 2021 and a B.S. in mathematics from Chongqing University in 2019.

I’d be more than happy to connect if you’re interested in my research or potential collaborations! The best way to reach me is by email.

News

• [06/05/2025] Our latest preprint “Collaborative Learning in Agentic Systems: A Collective AI is Greater Than the Sum of Its Parts” is available on arxiv.
• [06/02/2025] The preprint “Constrained Sliced Wasserstein Embedding” is available on arxiv.
• [04/29/2025] Our paper “ESPFormer: Doubly-Stochastic Attention with Expected Sliced Transport Plans” got accepted at ICML 2025!
• [02/11/2025] We had three papers accepted at ICLR 2025! Among them, the “Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data” was selected as Spotlight!
• [10/25/2024] Our paper “Wasserstein task embedding for measuring task similarities” was published at the Neural Networks Journal!