Hi! I am a Ph.D. student in Computational and Applied Mathematics at the University of Chicago, where I am fortunate to be advised by Daniel Sanz-Alonso. Before coming to Chicago, I received my B.S. in Mathematics and Applied Mathematics from Shanghai Jiao Tong University.
My research lies at the intersection of applied mathematics and data science, with a particular focus on operator learning, inverse problems, data assimilation, high-dimensional probability and statistics, and Monte Carlo methods. More broadly, I am interested in how information and structure shape the limits of learning and computation.
Publications and Preprints
- Convergence rates for learning pseudo-differential operators. With D. Sanz-Alonso, (2026). [arXiv]
- High-dimensional quasi-Monte Carlo via combinatorial discrepancy. With H. Jiang and N. Kirk, (2025). [arXiv]
- Optimal estimation of structured covariance operators. With O. Al-Ghattas, D. Sanz-Alonso, and N. Waniorek, (2024). [arXiv]
- Sharp concentration of simple random tensors II: asymmetry. With D. Sanz-Alonso.
Information and Inference, 15(2), 1-46, (2026). [Journal] [arXiv] - On the estimation of Gaussian moment tensors. With O. Al-Ghattas and D. Sanz-Alonso.
Electronic Communications in Probability, 30, 1-15, (2025). [Journal] [arXiv] - Sharp concentration of simple random tensors. With O. Al-Ghattas and D. Sanz-Alonso.
Information and Inference, 14(4), 1-41, (2025). [Journal] [arXiv] [Slides] - Precision and Cholesky factor estimation for Gaussian processes. With D. Sanz-Alonso.
SIAM/ASA Journal on Uncertainty Quantification, 13(3), 1085-1115, (2025). [Journal] [arXiv] - Covariance operator estimation: sparsity, lengthscale, and ensemble Kalman filters. With O. Al-Ghattas, D. Sanz-Alonso, and N. Waniorek.
Bernoulli, 31(3), 2377-2402, (2025). [Journal] [arXiv] [Slides] - A machine learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation. With Y. Jing, L. Li, and J. Lu.
Journal of Scientific Computing, 98(5), 1-34, (2024). [Journal] - Fluctuation suppression and enhancement in interacting particle systems. With L. Li, (2022). [arXiv] [Slides]
Teaching
University of Chicago Teaching Assistant
- STAT/CAAM 31511: Monte Carlo Simulation, Spring 2026
- STAT/CAAM 31521: Applied Stochastic Processes, Spring 2025
- STAT/CAAM 31050: Applied Approximation Theory, Spring 2024
- STAT/CAAM 38100: Measure-Theoretic Probability I, Winter 2024
- STAT/CAAM 31150: Inverse Problems and Data Assimilation, Autumn 2023