PhD, Stanford University, 2010
BS, Peking University, 2005.
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Chao Gao, Zongming Ma, Anderson Ye Zhang, Harrison Zhou (2017), Achieving optimal misclassification proportion in stochastic block models, Journal of Machine Learning Research.
Chao Gao, Zongming Ma, Harrison H. Zhou (2016), Sparse CCA: Adaptive estimation and computational barriers, The Annals of Statistics.
Edward Kennedy, Zongming Ma, Matthew McHugh, Dylan Small (2016), Nonparametric methods for doubly robust estimation of continuous treatment effects, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
Chao Gao, Yu Lu, Zongming Ma, Harrison Zhou (2016), Optimal estimation and completion of matrices with biclustering structures, Journal of Machine Learning Research, 17 (161), pp. 129.
Tony Cai, Xiaodong Li, Zongming Ma (2016), Optimal rates of convergence for noisy sparse phase retrieval via thresholded Wirtinger flow, The Annals of Statistics, 44, pp. 22212251.
Dan Yang, Zongming Ma, Andreas Buja (2016), Rate optimal denoising of simultaneously sparse and low rank matrices, Journal of Machine Learning Research, 17 (92), pp. 127.
Zongming Ma and Yihong Wu (2015), Volume ratio, sparsity, and minimaxity under unitarily invariant norms, IEEE Transactions on Information Theory, 61, pp. 69396956.
Chao Gao, Zongming Ma, Zhao Ren, Harrison Zhou (2015), Minimax estimation in sparse canonical correlation analysis, The Annals of Statistics, 43, pp. 21682197.
Mark G. Low and Zongming Ma (2015), Discussion: Frequentist coverage of adaptive nonparametric Bayesian credible sets , The Annals of Statistics, 43, pp. 14481454.
Zongming Ma and Yihong Wu (2015), Computational barriers in minimax submatrix detection, The Annals of Statistics, 43 (3), pp. 10891116.
Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications.
Graphical displays; one and twosample confidence intervals; one and twosample hypothesis tests; one and twoway ANOVA; simple and multiple linear leastsquares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodnessoffit tests. A methodology course. This course does not have business applications but has significant overlap with STAT 101 and 102.
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 MarcenkoPastur law, the TracyWidom law, nonparametric estimation and hypothesis testing of highdimensional covariance matrices, highdimensional principal component analysis, etc.
Sloan Research Fellowship, 2016 NSF CAREER Award, 2014
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