Chunming Zhang

       Professor of Statistics

       BIOGRAPHY

       RESEARCH

       STUDENT

       TEACHING

       PROFESSIONAL ACTIVITY



Position Held:

  • 2010-now: Professor of Statistics, University of Wisconsin-Madison.
  • 2005-2010: Associate Professor of Statistics, University of Wisconsin-Madison.
  • July-August 2001: Research Fellow, Australian National University.
  • 2000-2005: Assistant Professor of Statistics, University of Wisconsin-Madison.

Education:

Research Interests: My research interests encompass a broad spectrum of areas, spanning statistical methods in computational neuroscience, biostatistics, and financial econometrics, as well as the analysis of neuroimaging, spatial, and temporal data. Additionally, my work explores multiple hypotheses testing, large-scale simultaneous inference, dimension reduction, high-dimensional inference, non-parametric and semi-parametric modeling and inference, functional and longitudinal data analysis, robust statistics, and traffic forecasting in transportation.

  • Medical imaging data analysis;
  • Statistical methods for neuroinformatics and bioinformatics;
  • Multiple testing; large-scale simultaneous inference and applications;
  • Statistical methods for financial econometrics;
  • Dimension reduction and high-dimensional inference;
  • Non- and semi-parametric modeling & inference;
  • Robust statistics;
  • Functional & longitudinal data analysis;
  • Traffic forecasting in transportation

Selected Publications (in Research): (Co-authors noted as (s) for students and (a) for the thesis advisor)

  1. Zhang, C.M., Zhu, L.X., and Shen, Y.B.(s) (2023). "Robust estimation in regression and classification methods for large dimensional data," Machine Learning, 112(9), 3361-3411. (This paper focuses on the classification of Lymphoma and Colon cancer data.)
  2. Guo, R.S.(s), Zhang, C.M., and Zhang, Z.J. (2020). "Maximum Independent Component Analysis with application to EEG data," Statistical Science, 35(1), 145-157. (Special Issue on Statistics and Science, with the Guest Editor David Siegmund). (This paper focuses on brain EEG data.)
  3. Liu, J.(s), Zhang, C.M., and Page, D. (2016). "Multiple testing under dependence via graphical models," Annals of Applied Statistics, 10(3), 1699-1724. (This paper focuses on GWAS on breast cancer.)
  4. Zhang, C.M., Chai, Y.(s), Guo, X.(s), Gao, M.(s), Devilbiss, D.M., and Zhang, Z. (2016). "Statistical learning of neuronal functional connectivity," Technometrics, 58(3), 350-359. (Special Issue on Big Data) (This paper focuses on neuron spike train data.)
  5. Du, L.(s) and Zhang, C.M. (2014). "Single-index modulated multiple testing," Annals of Statistics, 42(4), 1262-1311. (This paper focuses on prostate cancer data.)
  6. Yu, T.(s), Zhang, C.M., Alexander, A.L., and Davidson, R.J. (2013). "Local tests for identifying anisotropic diffusion areas in human brain with DTI," Annals of Applied Statistics, 7(1), 201-225. (This paper focuses on brain Diffusion Tensor Imaging data.)
  7. Zhang, C.M., Fan, J.(a), and Yu, T.(s) (2011). "Multiple testing via FDRL for large-scale imaging data," Annals of Statistics, 39(1), 613-642. (This paper focuses on brain fMRI data.)
  8. Zhang, C.M., Jiang, Y.(s), and Chai, Y.(s) (2010). "Penalized Bregman divergence for large-dimensional regression and classification," Biometrika, 97(3), 551-566. (This paper focuses on cardiac arrhythmia data.)
  9. Zhang, C.M. and Yu, T.(s) (2008). "Semiparametric detection of significant activation for brain fMRI," Annals of Statistics, 36(4), 1693-1725. (This paper focuses on brain fMRI data.)
  10. Hall, P., Minnotte, M.C., and Zhang, C.M. (2004). "Bump hunting with non-Gaussian kernels," Annals of Statistics, 32(5), 2124-2141.
  11. Zhang, C.M. (2003). "Calibrating the degrees of freedom for automatic data smoothing and effective curve checking," Journal of the American Statistical Association, 98(463), 609-628.
  12. Fan, J.(a) and Zhang, C.M. (2003). "A reexamination of diffusion estimators with applications to financial model validation," Journal of the American Statistical Association, 98(461), 118-134. (This paper focuses on financial time series data.)
  13. Fan, J.(a), Zhang, C.M., and Zhang, Jian (2001). "Generalized likelihood ratio statistics and Wilks phenomenon," Annals of Statistics, 29(1), 153-193. (correction, 2002, 30(6), 1811-1811.)

Professional Service:

 Editorial Boards:

  • Associate Editor for Annals of Statistics (2007-2009; 2019-2021);
  • Associate Editor for Journal of the American Statistical Association (2011-2013; 2014-2017; 2023-);
  • Associate Editor for Journal of Statistical Planning and Inference (2012-2018);
  • Associate Editor for Journal of Data Science (2020-);
  • Associate Editor for The New England Journal of Statistics in Data Science (2020-)
  • Guest Associate Editor for Statistica Sinica (2005)
 Statistical Society:
  • Awards Committee (2023-2025), International Chinese Statistical Association.
  • Chair (2022), ICSA Award Committee, International Chinese Statistical Association.
  • Chair-Elect (2022) and Chair (2023), Section on Nonparametric Statistics, American Statistical Association.
  • Program Co-Chair, Scientific Committee for the 2019 IMS-China International Conference on Statistics and Probability, July 6-10, 2019, Dalian, China.
  • Publication Committee (2015-2017), International Chinese Statistical Association.
  • Program Committee (2014-2015), 2015 Joint Statistical Meetings, Representative for Section on Nonparametric Statistics, American Statistical Association.
  • Program Chair-Elect (2014) and Program Chair (2015), Section on Nonparametric Statistics, American Statistical Association.
  • Award Committee, Student Paper Competition (December 2013-January 2014), for JSM 2014 Boston, Section on Statistics in Imaging, American Statistical Association.
  • Charting Committee, The International Society for NonParametric Statistics (ISNPS) (2012-2015).
  • Board of Directors (2009-2011), International Chinese Statistical Association.
  • Committee on Nominations (August 2009-August 2010), Institute of Mathematical Statistics.

Teaching: STAT-609 (Fall 2023; Fall 2024); STAT-351 (Spring 2024)

  • STAT-351 (Introductory Nonparametric Statistics).
  • STAT-609 (Mathematical Statistics I).
  • STAT-610 (Mathematical Statistics II).
  • STAT-732 (Large Sample Theory of Statistical Inference)
  • STAT-809 (Non-Parametric Statistics).
 
Statistics | UW Home