I am a PhD student in the Applied Mathematics, Statistics and Scientific Computation (AMSC) program at The University of Maryland, College Park. I am advised by Maria Cameron and working closely with Furong Huang. My current research is in data-driven methods for understanding rare events in molecular dynamics and trustworthy machine learning. My recent research covers a broad range of topics including optimal control, model reduction, causality in machine learning and computational methods for differential equations.

Publications and preprints

• Jiaxin Yuan, Amar Shah, Channing Bentz, and Maria Cameron. Optimal control for sampling the transition path process and estimating rates. Communications in Nonlinear Science and Numerical Simulation, 2023. Accepted.
• Xiaoyu Liu, Jiaxin Yuan, Bang An, Yuancheng Xu, Yifan Yang, and Furong Huang. C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder. Conference on Neural Information Processing Systems (NeurIPS), 2023
• Xiaoyu Liu, Jiaxin Yuan, Bang An, Yuancheng Xu, Yifan Yang, Furong Huang. C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder. International Conference on Machine Learning (ICML) workshop on Structured Probabilistic Inference & Generative Modeling, 2023.

Education

• Ph.D. in Applied Mathematics, University of Maryland,
  May 2025(expected), GPA: 3.84/4.00

• B.S. in Mathematics, Minor in Economics,
  Schreyer Honors College, The Pennsylvania State University,
  May 2020, GPA: 4.0/4.0

Teaching

• Teaching assistant for summer REU at University of Maryland, 2022.
• Teaching assistant for pre-calculus, calculus I, calculus II, ordinary differential equations, elementary statistics and probability and linear algebra, 2020-2023