I am an Associate Professor in the School of Statistics and Data Science at Nankai University. Previously, I served as an Associate Professor in the School of Statistics and the Academy of Statistics and Interdisciplinary Sciences at East China Normal University (2021-2024), and as an Assistant Professor in the School of Statistics and Data Science at Nankai University (2018-2021). I received the Ph.D. in Statistics (2018), the M.Sc. in Statistics (2015), and the B.Sc. in Mathematics and Applied Mathematics (2012), all from Nankai University.

Research

My research interests include change-point detection, high-dimensional inference, and predictive inference.

Contact

Email: ghwang.nk@gmail.com

Publications

Journal

X. Cui, H. Geng, G. Wang, Z. Wang, and C. Zou (2026). ART: distribution-free and model-agnostic changepoint detection with finite-sample guarantees Journal of the Royal Statistical Society Series B: Statistical Methodology.

C. Qian, G. Wang, and C. Zou (2025). Reliever: relieving the burden of costly model fits for changepoint detection. Journal of Machine Learning Research, 26, 1−57.

Y. Liu, Y. Jia, G. Wang, Z. Wang, and C. Zou (2026). Prediction-powered model checking via predictiveness comparisons. Journal of Systems Science and Complexity, 39, 115–135.

L. Shi, G. Wang, and C. Zou (2024). Low-rank matrix estimation in the presence of change-points. Journal of Machine Learning Research, 25, 1–71.

J. Cui, G. Wang, F. Song, X. Ma, and C. Zou (2025). Robust multi-task regression with shifting low-rank patterns. Acta Mathematica Sinica, English Series, 41, 677–702.

G. Wang, L. Feng, and P. Zhao (2024). New approaches for testing slope homogeneity in large panel data models. Communications in Mathematics and Statistics.

G. Wang and L. Feng (2023). Computationally efficient and data-adaptive changepoint inference in high dimension. Journal of the Royal Statistical Society Series B: Statistical Methodology, 85, 936–958.

J. Cui, G. Wang, C. Zou, and Z. Wang (2023). Change-point testing for parallel data sets with FDR control. Computational Statistics \& Data Analysis, 182, 107705.

M. Wen, G. Wang, C. Zou, and Z. Wang (2024). Activation discovery with FDR control: application to fMRI data. Statistica Sinica, 34, 1625–1647.

L. Peng, G. Wang, and C. Zou (2023). Measuring, testing, and identifying heterogeneity of large parallel datasets. Statistica Sinica, 33, 2787–2808.

G. Wang and C. Zou (2023). cpss: an R package for change-point detection by sample-splitting methods. Journal of Quality Technology, 55, 61–74.

G. Wang, C. Zou, and P. Qiu (2022). Data-driven determination of the number of jumps in regression curves. Technometrics, 64, 312–322.

C. Zou, G. Wang, and R. Li (2020). Consistent selection of the number of change-points via sample-splitting. Annals of Statistics, 48, 413–439.

G. Wang, C. Zou, and G. Yin (2018). Change-point detection in multinomial data with a large number of categories. Annals of Statistics, 46, 2020–2044.

G. Wang, Z. Wang, and C. Zou (2017). Comparison of a large number of regression curves. Journal of Multivariate Analysis, 162, 122–133.

A. Amiri, M. Koosha, A. Azhdari, and G. Wang (2015). Phase I monitoring of generalized linear model-based regression profiles. Journal of Statistical Computation and Simulation, 85, 2839–2859.

G. Wang, C. Zou, and Z. Wang (2013). A necessary test for complete independence in high dimensions using rank-correlations. Journal of Multivariate Analysis, 121, 224–232.

Conference

G. Hu, F. Liu, M. Gong, G. Wang, and L. Peng (2025). Learning imbalanced data with beneficial label noise. ICML 2025.

G. Chen, Y. Jia, G. Wang, and C. Zou (2024). Zipper: addressing degeneracy in algorithm-agnostic inference. NeurIPS 2024 (spotlight).

H. Chen, Y. Jia, G. Wang, and C. Zou (2024). Uncertainty quantification for data-driven change-point learning via cross-validation. AAAI 2024.