I am interested in the mathematics of data science. My current research focuses on random sampling and approximation, statistical ranking, 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. 

R. Han, and Y. Xu. A unified analysis of likelihood-based estimators in the Plackett-Luce model. Annals of Statistics, 53 (5), 2077-2102 (2025).

R. Gheissari, A. Jagannath, Y. Xu. Finding planted cliques using gradient descent. SIAM Journal on Mathematics of Data Science 7.2 (2025): 643-669.

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.

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.