Research Interests: high-dimensional statistics, machine learning, network data analysis, nonparametric statistics
Links: Personal Website
PhD, Stanford University, 2010
BS, Peking University, 2005.
For more information, go to My Personal Page
Jian Ding, Zongming Ma, Yihong Wu, Jiaming Xu (2021), Efficient random graph matching via degree profiles, Probability Theory and Related Fields, (to appear).
Chao Gao and Zongming Ma (2020), Minimax rates in network analysis: Graphon estimation, community detection and hypothesis testing, Statistical Science, (to appear).
Zhuang Ma, Zongming Ma, Tingni Sun (2020), Adaptive estimation in two-way sparse reduced-rank regression, Statistica Sinica, (to appear).
Cedric Huchuan Xia, Zongming Ma, Zaixu Cui, Danilo Bzdok, Bertrand Thirion, Danielle S. Bassett, Theodore D. Satterthwaite, Russell T. Shinohara, Daniela M. Witten (2020), Multi‐scale network regression for brain‐phenotype associations, Human Brain Mapping, (to appear).
Zhuang Ma, Zongming Ma, Hongsong Yuan (2020), Universal latent space model fitting for large networks with edge covariates, Journal of Machine Learning Research, 21 (4), pp. 1-67.
Chao Gao, Zongming Ma, Ye Zhang, Harrison H. Zhou (2018), Community detection in degree-corrected block models, The Annals of Statistics, 46 (5), pp. 2153-2185.
Cedric Xia, Zongming Ma, Rastko Ciric, Shi Gu, Richard F. Betzel, Antonia N. Kaczkurkin, Monica E. Calkins, Philip A. Cook, Angel Garcia de la Garza, Simon Vandekar, Tyler M. Moore, David R. Roalf, Kosha Ruparel, Daniel H. Wolf, Christos Davatzikos, Ruben C. Gur, Raquel E. Gur, Russell T. Shinohara, Danielle S. Bassett, Theodore D. Satterthwaite (2018), Linked dimensions of psychopathology and connectivity in functional brain networks, Nature Communications, 9, p. 3003.
Debapratim Banerjee and Zongming Ma (Working), Asymptotic normality and analysis of variance of log-likelihood ratios in spiked random matrix models.
Chao Gao, Zongming Ma, Harrison H. Zhou (2017), Sparse CCA: Adaptive estimation and computational barriers, The Annals of Statistics, 45 (5), pp. 2074-2101.
Edward Kennedy, Zongming Ma, Matthew McHugh, Dylan Small (2017), Nonparametric methods for doubly robust estimation of continuous treatment effects, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79 (4), pp. 1229-1245.
Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications. This course may be taken concurrently with the prerequisite with instructor permission.
Written permission of instructor and the department course coordinator required to enroll in this course.
Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests. A methodology course. This course does not have business applications but has significant overlap with STAT 101 and 102. This course may be taken concurrently with the prerequisite with instructor permission.
An introduction to the mathematical theory of statistics. Estimation, with a focus on properties of sufficient statistics and maximum likelihood estimators. Hypothesis testing, with a focus on likelihood ratio tests and the consequent development of "t" tests and hypothesis tests in regression and ANOVA. Nonparametric procedures. This course may be taken concurrently with the prerequisite with instructor permission.
STAT 515 is aimed at first-year Ph.D. students and builds a good foundation in statistical inference from the first principles of probability.
This is a course that prepares PhD students in statistics for research in multivariate statistics and high dimensional statistical inference. Topics from classical multivariate statistics include the multivariate normal distribution and the Wishart distribution; estimation and hypothesis testing of mean vectors and covariance matrices; principal component analysis, canonical correlation analysis and discriminant analysis; etc. Topics from modern multivariate statistics include the Marcenko-Pastur law, the Tracy-Widom law, nonparametric estimation and hypothesis testing of high-dimensional covariance matrices, high-dimensional principal component analysis, etc.
Theory of the Gaussian Linear Model, with applications to illustrate and complement the theory. Distribution theory of standard tests and estimates in multiple regression and ANOVA models. Model selection and its consequences. Random effects, Bayes, empirical Bayes and minimax estimation for such models. Generalized (Log-linear) models for specific non-Gaussian settings.
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.
Written permission of instructor and the department course coordinator required to enroll.