Zongming Ma

Zongming Ma
  • Associate Professor of Statistics, Director of Ph.D. Program in Statistics

Contact Information

  • office Address:

    468 Jon M. Huntsman Hall
    3730 Walnut Street
    Philadelphia, PA 19104

Research Interests: high-dimensional statistics, machine learning, network data analysis, nonparametric statistics

Links: Personal Website

Overview

Education

PhD, Stanford University, 2010
BS, Peking University, 2005.

Academic Positions Held

Wharton: 2010-present

For more information, go to My Personal Page

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Research

Teaching

Current Courses

  • STAT515 - Advanced Statistical Inference I

    STAT 515 is aimed at first-year Ph.D. students and builds a good foundation in statistical inference from the first principles of probability.

    STAT515001 ( Syllabus )

  • STAT991 - Seminar In Advanced Application Of Statistics

    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.

    STAT991302

Past Courses

  • STAT102 - Introductory Business Statistics

    Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications.

  • STAT431 - Statistical Inference

    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.

  • STAT515 - Advanced Statistical Inference I

    STAT 515 is aimed at first-year Ph.D. students and builds a good foundation in statistical inference from the first principles of probability.

  • STAT925 - Multivariate Analysis: Theory

    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.

  • STAT971 - Introduction to Linear Statistical Models

    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.

  • STAT991 - Seminar in Advanced Application of Statistics

    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.

Awards and Honors

  • Sloan Research Fellowship, 2016
  • NSF CAREER Award, 2014

Activity

Latest Research

Chao Gao, Zongming Ma, Anderson Ye Zhang, Harrison H. Zhou (2018), Community detection in degree-corrected block models, The Annals of Statistics, 46 (5), pp. 2153-2185.
All Research

Awards and Honors

Sloan Research Fellowship 2016
All Awards