Anderson Ye Zhang

Anderson Ye Zhang
  • Alfred H. Williams Faculty Scholar
  • Assistant Professor of Statistics and Data Science
  • Assistant Professor of Computer and Information Science (secondary appointment)

Contact Information

  • office Address:

    427 Academic Research Building
    265 South 37th Street
    Philadelphia, PA 19104

Research Interests: Network analysis, Mean field variational inference, Clustering and mixture models, Spectral analysis, Ranking and synchronization

Links: Personal Website



Ph.D. in Statistics and Data Science, Yale University, 2018
B.Sc. in Statistics, Zhejiang University, 2013

Academic Positions Held

Assistant Professor, Department of Statistics and Data Science, University of Pennsylvania, 2019-
William H. Kruskal Instructor, Department of Statistics, University of Chicago, 2018-2019

Continue Reading



All Courses

  • CIS8990 - PhD Independent Study

    For doctoral students studying a specific advanced subject area in computer and information science. The Independent Study may involve coursework, presentations, and formally gradable work comparable to that in a CIS 5000 or 6000 level course. The Independent Study may also be used by doctoral students to explore research options with faculty, prior to determining a thesis topic. Students should discuss with the faculty supervisor the scope of the Independent Study, expectations, work involved, etc. The Independent Study should not be used for ongoing research towards a thesis, for which the CIS 9990 designation should be used.

  • CIS9990 - Thesis/Diss Research

    For students pursuing advanced research to fulfill PhD dissertation requirements.

  • MATH4990 - Supervised Study

    Study under the direction of a faculty member. Intended for a limited number ofmathematics majors.

  • STAT4310 - 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 1010 and 1020. This course may be taken concurrently with the prerequisite with instructor permission.

  • STAT9710 - Intro To Linear Stat Mod

    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.

  • STAT9910 - Sem in Adv Appl of Stat

    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.

  • STAT9990 - Independent Study

    Written permission of instructor and the department course coordinator required to enroll.

Awards and Honors

  • ICSA New Researcher Award, 2019
  • Francis J. Anscombe Award, Department of Statistics and Data Science, Yale University, 2018