I am interested in the mathematical foundations of data science. My current research focuses on topics including randomized approximation (particularly for problems arising from stochastic simulation and computational finance), statistical ranking and social choice models, and optimization-based uncertainty quantification. I am often problem-driven and eager to learn new ideas, which I believe is one of the most rewarding aspects of research.

I am always looking for skilled and motivated students at UK to work with. Ideally, I seek candidates with a foundation spanning a subset of linear algebra, mathematical analysis, probability, and statistics, along with a reasonable amount of coding experience (though this can be compensated with tools like ChatGPT). If you are interested, please send me your CV and a brief description of your research interests.
 

R. Han, B. Kramer, D. Lee, A. Narayan, Y. Xu. An approximate control variates approach to multifidelity distribution estimation. SIAM/ASA Journal on Uncertainty Quantification, 12, no. 4 (2024): 1349-1388.

K. Craig, B. Osting, D. Wang, and Y. Xu. Wasserstein Archetypal Analysis. Applied Mathematics & Optimization, 90(2), 36 (2024).

N. Cheng, OA Malik, Y. Xu, S. Becker, A. Doostan, and A. Narayan. Subsampling of parametric models with bifidelity boosting. SIAM/ASA Journal on Uncertainty Quantification 12.2 (2024): 213-241.

R. Han, Y. Xu, and K. Chen. A general pairwise comparison model for extremely sparse networks. Journal of the American Statistical Association, 118, no. 544 (2023): 2422-2432.

Y. Xu, and A. Narayan. Randomized weakly admissible meshes. Journal of Approximation Theory 285 (2023): 105835.