Zhimei Ren

• Yu Gui, Ying Jin, Yash Nair, Zhimei Ren, ACS: An interactive framework for conformal selection.
• Ying Jin and Zhimei Ren (2025), Confidence on the focal: Conformal prediction with selection-conditional coverage, Journal of the Royal Statistical Society Series B: Statistical Methodology.
• Ying Jin, Zhimei Ren, Zhuoran Yang, Zhaoran Wang (2025), Policy learning “without” overlap: pessimism and generalized empirical Bernstein’s inequality, Annals of Statistics (accepted).
• Yu Gui, Ying Jin, Zhimei Ren (2024), Conformal alignment: Knowing when to trust foundation models with guarantees, Advances in Neural Information Processing Systems (NeurIPS).
• Ying Jin, Zhimei Ren, Emmanuel J. Candes (2023), Sensitivity analysis of individual treatment effects: a robust conformal inference approach, Proceedings of the National Academy of Sciences (PNAS).
• Ying Jin, Zhimei Ren, Zhengyuan Zhou, Sensitivity analysis under the f-sensitivity models: a distributional robustness perspective.

Current Courses

• 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 )

  STAT4310002 ( Syllabus )

• STAT9910 - Sem In Adv Appl Of Stat: Topics In Statistics

  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.

  STAT9910301 ( 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.

• 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.

• 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.

• STAT9917 - 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.

• STAT9950 - Dissertation

  Dissertation

• STAT9999 - Independent Study

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

• Rising Star in Data Science, University of Chicago, 1970
• Waterloo Student Conference Presentation Award, University of Waterloo, 1970
• First Prize of the 2018 Citadel Datathon, Stanford University, 1970

Knowledge @ Wharton

• Decoding the AI Bubble: Tulips, Dot-Coms, or Something Else?, Knowledge @ Wharton - 12/16/2025
• Can Reminders Help You Save More Money?, Knowledge @ Wharton - 12/15/2025
• How Banks Can Be Change Agents in Climate Consciousness, Knowledge @ Wharton - 12/15/2025
• Why Today’s AI Bubble May Fuel Tomorrow’s Economic Growth, Knowledge @ Wharton - 12/12/2025
• Best of 2025, Knowledge @ Wharton - 12/12/2025
• Understanding the True Costs Behind Credit Card Lending, Knowledge @ Wharton - 12/10/2025
• Best of 2025, Knowledge @ Wharton - 12/09/2025
• Why Many M&A Deals Fail — and How to Beat the Odds, Knowledge @ Wharton - 12/08/2025
• When Employees Feel Slighted, They Work Less, Knowledge @ Wharton - 12/08/2025
• How Amazon Fulfillment Centers Influence Local Economic Growth, Knowledge @ Wharton - 12/05/2025