Hi! I'm a third-year undergraduate at the Georgia Institute of Technology studying Mathematics. I am fortunate to work with Prof. Yongxin Chen and Prof. Amirali Aghazadeh on the mathematical foundations of machine learning. Outside of research, I enjoy photography, writing, reading, listening to music, and bouldering. In Summer 2025, I participated in a REU at Williams College; this summer I'm a Quantitative Trading Intern at Susquehanna International Group (SIG). I also serve as the lead of quantitative research for Trading @ Georgia Tech.
Research
My research centers on reasoning about machine learning from mathematical first principles, with a particular focus on high-dimensional probability, optimal transport, and geometric deep learning. Previously, I have worked on structure-preserving generative models, including diffusion on Riemannian manifolds, normalizing flows, geometric flow matching, sampling acceleration for discrete processes, and finite-sample convergence for Sequential Monte Carlo on non-log-concave targets.
My current focus is on sequential minimax games and their connections to martingale optimal transport, work that appeared at the ICML 2026 NExTGame Workshop. I also work on uncertainty quantification for neural surrogates of Wasserstein gradient flows. My earlier work on entropic martingale optimal transport appeared at the ICLR 2026 Workshop on Advances in Financial AI.
I am also interested in differential geometry, options pricing, and algorithmic game theory.
Publications
- E. Chen. "Sequential Minimax Games as Stacked Martingale Optimal Transport". International Conference on Machine Learning (ICML) NExTGame Workshop, 2026.
- E. Chen. "Entropically Regularized Martingale Optimal Transport with L1 Relaxation". International Conference on Learning Representations (ICLR) Workshop on Advances in Financial AI, 2026. paper
Contact
ec [at] gatech [dot] edu / linkedin