I develop statistical methods and efficient algorithms for learning from complex modern data.
I am an IRSA Faragher Distinguished Postdoctoral Fellow in the School of Statistics at the University of Minnesota. Previously, I received my Ph.D. in Statistics from the University of Iowa, advised by Boxiang Wang. Before that, I earned an M.S. in Statistics from the University of California, Davis and a B.S. in Information and Computation Science from Jilin University.
Robust learning, kernel quantile regression, imbalanced classification, and transfer learning.
Efficient optimization and reproducible software for high-dimensional problems.
Transfer learning for clustering and methods for complex, structured data.
01 / UPDATES
Attended the New Researchers Conference.
Taught STAT 5021: Statistical Analysis at the University of Minnesota. Teaching
QuanDA appeared at NeurIPS 2025. Received the NeurIPS 2025 Scholar Award. Publication
Joined the University of Minnesota as an IRSA Faragher Distinguished Postdoctoral Fellow.
02 / SELECTED WORK
Journal of Computational and Graphical Statistics, 35(1), 395–405. Published online in 2025.
Efficient computation for kernel quantile regression, including non-crossing quantile curves.
Advances in Neural Information Processing Systems 38.
Quantile-based classification for imbalanced data with many features and few observations.
Proceedings of the 41st International Conference on Machine Learning, PMLR 235, 47865–47884.
Finite smoothing for accurate, efficient optimization of non-smooth statistical learning problems.
03 / OPEN-SOURCE SOFTWARE
R packages that bring statistical methodology into practice.
Quantile-based discriminant analysis for high-dimensional imbalanced classification.
Fast kernel quantile regression with support for non-crossing quantile curves.
Regularization paths for penalized quantile regression in high dimensions.
Efficient solution paths for penalized support vector machines.
ACADEMIC BACKGROUND
PDF · 3 pages · Provided August 2026
LET’S CONNECT
I welcome conversations about statistical learning, quantile methods, and research collaborations.
tang1015@umn.edu School of Statistics · University of Minnesota