Anderson Ye Zhang

• Ye Zhang (2024), Fundamental Limits of Spectral Clustering in Stochastic Block Models, IEEE Transactions on Information Theory, 70 (10).
• xin chen and Ye Zhang (2024), Achieving Optimal Clustering in Gaussian Mixture Models with Anisotropic Covariance Structures, Neural Information Processing Systems (NeurIPS) 2024. Accepted.
• Ye Zhang (2024), Exact Minimax Optimality of Spectral Methods in Phase Synchronization and Orthogonal Group Synchronization, Annals of Statistics (Accepted).
• Ye Zhang and Harrison H. Zhou (2024), Leave-one-out Singular Subspace Perturbation Analysis for Spectral Clustering, Annals of Statistics (Accepted).
• Duc Nguyen and Ye Zhang (Under Review), A Novel and Optimal Spectral Method for Permutation Synchronization.
• Chao Gao, Yandi Shen, Ye Zhang (2023), Uncertainty Quantification in The Bradley-Terry-Luce Model, Information and Inference: A Journal of the IMA, 12 (2).
• Duc Nguyen and Ye Zhang (2023), Optimal and Private Learning from Human Response Data, Artificial Intelligence and Statistics (AISTATS) 2023.
• Ye Zhang and Duc Nguyen (2022), Efficient and Accurate Learning of Mixtures of Plackett-Luce Models, AAAI Conference on Artificial Intelligence (AAAI) 2023.
• Duc Nguyen and Ye Zhang (2022), A Spectral Approach to Item Response Theory, Neural Information Processing Systems (NeurIPS) 2022.
• Chao Gao and Ye Zhang (2022), Optimal Orthogonal Group Synchronization and Rotation Group Synchronization, Information and Inference: A Journal of the IMA, 12 (2).
• All Research from Anderson Ye Zhang »

Current Courses

• CIS9990 - Thesis/dissertation Research

  For students working on an advanced research program leading to the completion of master's thesis requirements.

  CIS9990063

• 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.

  STAT4310001 ( Syllabus )

• STAT9710 - 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.

  STAT9710001 ( Syllabus )

Past Courses

• AMCS5999 - Independent Study

  Independent Study allows students to pursue academic interests not available in regularly offered courses. Students must consult with their academic advisor to formulate a project directly related to the student’s research interests. All independent study courses are subject to the approval of the AMCS Graduate Group Chair.

• AMCS9999 - Ind Study & Research

  Study under the direction of a faculty member.

• 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 - Master's Thesis

  For students working on an advanced research program leading to the completion of master's thesis 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.

• NSF CAREER Award, 2025
• ICSA New Researcher Award, 2019
• Francis J. Anscombe Award, Department of Statistics and Data Science, Yale University, 2018
• Chu Kochen Scholarship, Zhejiang University, 2012

Knowledge @ Wharton

• The EV Trade War Alone Won’t Save American Manufacturing, Knowledge @ Wharton - 02/11/2025
• AI’s Impact on Productivity and Innovation | Daniel Rock, Knowledge @ Wharton - 02/11/2025
• Jeremy Siegel: How Tariffs Could Impact the U.S. Economy, Knowledge @ Wharton - 02/10/2025
• Why Accountability Matters in AI Development and Governance, Knowledge @ Wharton - 02/07/2025
• Embracing AI: Are You a Doomer, Gloomer, Zoomer, or Bloomer?, Knowledge @ Wharton - 02/04/2025
• Can Pride Make You Look Incompetent at Work?, Knowledge @ Wharton - 02/04/2025
• Transformative Tech: Innovating for the Underserved | Valentina Assenova, Knowledge @ Wharton - 02/04/2025
• As People Still Recover From the LA Wildfires, What Can Business Leaders Do to Help?, Knowledge @ Wharton - 02/04/2025
• The Economic Impact of Trump’s Tariffs, Knowledge @ Wharton - 02/03/2025
• Building a Brand: The Entrepreneurial Lessons Learned on the Road to Cirkul’s Success, Knowledge @ Wharton - 01/30/2025