Weijing Tang's Homepage

Weijing Tang 唐沩婧

Assistant Professor
Department of Statistics & Data Science
Carnegie Mellon University

Email: weijingt AT andrew DOT cmu DOT edu
Address: 132B Baker Hall, 5000 Forbes Avenue, Pittsburgh, PA

About Me

I am an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University. Prior to joining CMU in 2023, I was a Postdoctoral Research Fellow in Biostatistics at Harvard University, working with Tianxi Cai. In 2022, I received a Ph.D. in Statistics from the University of Michigan, where I was fortunate to be advised by Ji Zhu. In 2016, I received a B.S. in Mathematics from Tsinghua University.

I am broadly interested in developing statistical methodology and theory for data-centric AI, multi-source statistical learning, network analysis, and survival analysis with applications to health and social sciences. My research is largely motivated by challenges arising from analyzing massive and complex datasets for interdisciplinary research.

Selected Awards

- 2025 Honorable mention of best paper at the ICML 2025 Workshop DataWorld
- 2023 ProQuest Distinguished Dissertation Award, University of Michigan
- 2021 Student Paper Award, Statistical Learning and Data Science Section, ASA
- 2021 Distinguished Student Paper Award, ENAR International Biometric Society
- 2020 Grand Prize, COVID-19 Data Challenge, American Heart Association
- 2020 Student Paper Award, Nonparametric Statistics Section, ASA
- 2013-2016 Tsinghua Xuetang Talents Program in Mathematics, Tsinghua University

Selected Papers (full list)

- K. Lee*, Z. Liu*, W. Tang, and Y. Zhang. Faithful Group Shapley Value. Advances in Neural Information Processing System (2025). (NeurIPS'25). [arXiv]
  (Honorable mention for best paper, ICML 2025 Workshop DataWorld.)
- W. Tang and J. Zhu. Population-level Balance in Signed Networks. Journal of the American Statistical Association: Theory and Methods (JASA) (2024), 120 (550), 751-763. [Journal][arXiv][Code]
  (Student Paper Award, 2021 ASA Section on Statistical Learning and Data Science.)
- W. Tang, K. He, G. Xu, and J. Zhu. Survival Analysis via Ordinary Differential Equations. Journal of the American Statistical Association: Theory and Methods (JASA) (2023), 118(544), 2406-2421. [Journal][arXiv][Code]
  (Student Paper Award, 2020 ASA Section on Nonparametric Statistics and ENAR 2021 Distinguished Student Paper Award.)

My research is partially supported by NSF, CMU Dietrich College Seed Grant, and Jane Street Research Gift.

News

- Congratulations to Ziqi Liu for winning the 2026 ASA Student Paper Award from the Statistical Learning and Data Science Section! He will present our work on Representation Learning with Blockwise Missingness and Signal Heterogeneity in an award session at JSM 2026 in Boston. (Jan 2026)
- I'm serving as an Area Chair for AISTATS 2026! (Sep 2025)
- Our work on group data valuation received an honorable mention of best paper at the ICML 2025 Workshop DataWorld! (July 2025)
- Phoebe Lam (Psychology) and I have been awarded the Dietrich College Seed Grant for our collaborative project on lifecourse data integration! (Apr 2025)