Anderson Ye Zhang

• Ye Zhang (2025), Tightness of SDP and Burer-Monteiro Factorization for Phase Synchronization in High-Noise Regime, SIAM Journal on Optimization (Accepted).
• Duc Nguyen and Ye Zhang (2025), A Novel and Optimal Spectral Method for Permutation Synchronization, IEEE Transactions on Information Theory, 71 (5), pp. 3779-3801.
• Ye Zhang and Harrison H. Zhou (2024), Leave-one-out Singular Subspace Perturbation Analysis for Spectral Clustering, Annals of Statistics, 52 (5), pp. 2004-2033.
• Ye Zhang (2024), Exact Minimax Optimality of Spectral Methods in Phase Synchronization and Orthogonal Group Synchronization, Annals of Statistics, 52 (5).
• 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.
• Ye Zhang and Duc Nguyen (2023), Efficient and Accurate Learning of Mixtures of Plackett-Luce Models, AAAI Conference on Artificial Intelligence (AAAI) 2023.
• 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.
• Duc Nguyen and Ye Zhang (2022), A Spectral Approach to Item Response Theory, Neural Information Processing Systems (NeurIPS) 2022.
• All Research from Anderson Ye Zhang »

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.

• AMCS9950 - Dissertation

  Allows for a PhD student to be enrolled full-time to work exclusively on research, writing and preparing his/her doctoral thesis and defense. All required coursework (20 CUs) must be completed, and the student must have passed his/her thesis proposal/oral candidacy examination prior to being enrolled.

• AMCS9990 - Masters Thesis

  For students writing a Master's Thesis to fulfill the program's requirements. All required coursework (8 CUs) must be completed prior to being enrolled.

• AMCS9999 - Ind Study & Research

  Study under the direction of a faculty member.

• CIS9990 - Master's Thesis

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

• CIS9999 - Independent Study Research

  For Computer and Information Science doctoral students studying a specific advanced subject area. Students should discuss with the faculty supervisor the scope of the independent study/research and know the expectations and work involved.

• GCB7990 - Independent Study

  Independent study course

• 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 - Theory of Statistics

  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 is for graduate students who wish to learn about current research frontiers. It covers advanced topics in probability, statistical theory and methods, applied statistics, data science and artificial intelligence. Specific topics vary from year to year and emphasize both theoretical foundations and applications.

• STAT9950 - Dissertation

  Dissertation

• Sloan Research Fellowship in Mathematics, 2025
• NSF CAREER Award, 2025
• New Researcher Award, The 11th ICSA International Conference, 2019
• Francis J. Anscombe Award, Department of Statistics and Data Science, Yale University, 2018
• IBM Student Paper Award, The 29th New England Statistical Symposium, 2015
• Chu Kochen Scholarship, Zhejiang University, 2012

Knowledge @ Wharton

• Oil, AI, and the Fed: What’s Next for Markets?, Knowledge @ Wharton - 07/31/2026
• Marketing Baby Tech to Modern Parents, Knowledge @ Wharton - 07/30/2026
• Can the Sixers’ New Superteam Work?, Knowledge @ Wharton - 07/29/2026
• Why Prediction Markets Are More Than a Passing Trend, Knowledge @ Wharton - 07/29/2026
• Transforming Health Care Logistics With Low-Cost AI, Knowledge @ Wharton - 07/28/2026
• Can an Acquisition Buy a Company a Second Chance?, Knowledge @ Wharton - 07/28/2026
• The New Strategy Behind Revenue Growth, Knowledge @ Wharton - 07/24/2026
• What the World Cup Teaches Brands About Cultural Marketing, Knowledge @ Wharton - 07/22/2026
• How Constraints Shape Creativity and Athletic Development, Knowledge @ Wharton - 07/22/2026
• Why Consumers Are Still Spending Despite Inflation, Knowledge @ Wharton - 07/21/2026