NeurIPS
Momentum Multi-Marginal Schrödinger Bridge Matching
Neural Information Processing Systems, 2025
PhD Student in Machine Learning
Georgia Institute of Technology•ACDS Lab
I focus on incorporating optimality and domain-specific structures into diffusion models, aiming to deepen theoretical insights and create scalable algorithms. My research lies at the intersection of dynamic optimal transport and stochastic optimal control, contributing mainly in the field of Schrödinger Bridge for applications in generative modeling, unpaired image translation, crowd navigation, and opinion depolarization.
Current focus:
Schrödinger Bridge
Non-linear Diffusion Models
Generative Modeling
Optimal Transport
Recent Updates
NeurIPS 2025: Momentum Multi-Marginal Schrödinger Bridge Matching
Our latest work on mult-marginal Schrödinger Bridge Matching has been accepted at NeurIPS 2025.
Looking for Summer 2026 internship opportunities
Seeking research internships in machine learning, optimal transport, diffusion models.
New preprint: Momentum Multi-Marginal Schrödinger Bridge Matching
Our latest work on mult-marginal Schrödinger Bridge Matching is now available on arXiv.
ICLR Oral 2025: Feedback Schrödinger Bridge Matching
Our latest work has been accepted as an Oral presentation at ICLR 2025. This paper introduces feedback mechanisms to improve generative modeling performance.
New preprint: Feedback Schrödinger Bridge Matching
Our latest work on semi-supervised Schrödinger Bridge Matching is now available on arXiv. This paper introduces feedback mechanisms to improve generative modeling performance.
Paper accepted at ICLR 2024
Our work "A Robust Differential Neural ODE Optimizer" was accepted to the International Conference on Learning Representations (ICLR 2024).
Get In Touch
I'm always interested in discussing research collaborations, or anything related to optimal transport and diffusion models.