Students
Present Ph.D Students
Graduated Ph.D Students
28. Disha Ghandwani, 2025
Scalable Statistical Models for Personalized Recommendation: Random Slopes and Latent Structure
Member of Research Staff, Uber, San Francisco.
27. Anav Sood, 2025
New Perspectives on Dimensionality and Selective Inference
Member of Research Staff, OpenAI, San Francisco.
26. James Yang, 2025
Fast and Scalable Solvers for Penalized Regression with Sparsity
Quantitative researcher, DE Shaw, New York.
25. Swarnadip Ghosh, 2022 (co-advised with Art Owen)
Scalable Inference for Crossed Random Effects Models
Radix Trading, NY. Quantitative Researcher, 2022.
24. Elena Tuzhilina, 2022
Advances in Multivariate Statistics and its Applications
Asst. Prof, University of Toronto.
23. Elizabeth Chin, 2022 (co-advised with Euan Ashley)
Statistical and Algorithmic Approaches for Health Policy and Fairness
Asst. Prof, Johns Hopkins Biostatistics Department.
22. Zijun Gao, 2022
Applications of Machine Learning to some Statistical Inference Problems
PostDoc Cambridge University (Qingyuan Zhao) 2022,
Asst. Prof, University of Southern California 2023.
21. Junyang Qian, 2020
Large-Scale Statistical Learning Methods and Algorithms
PDT Partners, New York.
20. Rakesh Achanta, 2019
Boosting like Path Algorithms For L1 Regularized Infinite Dimensional Convex Neural Networks
Volunteer, Sadhguru's Isha Foundation
19. Ya Le, 2018
Topics in Statistical Learning with a Focus on Large-Scale Data
Data Science team, Google Brain.
OpenAI researcher on chatGPT5 team, till launch 2025.
AI team, Meta.
18. Charles Zheng, 2017 (co-advised with Jonathan Taylor, and mentored by Yuval Benjiamini)
Supervised Learning of Representations
Researcher in Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health.
17. Hristo Spassimirov Paskov, 2016 (co-advised with John Mitchell, CS)
Arthur Samuel Award for best dissertation in Computer Science at Stanford.
Learning with N-grams: from Massive Scales to Compressed Representations.
Staff Research Scientist, Hillspire.
16. Qingyuan Zhao, 2016
Topics in Causal and High Dimensional Inference
Professor of Statistics,
Statistical Laboratory, Department of Pure Mathematics and Mathematical Statistics,
University of Cambridge. www.statslab.cam.ac.uk/~qz280/
15. Will Fithian, 2015
Topics in Adaptive Inference
Associate Professor, Statistics Department, University of California, Berkeley.
14. Jason Lee, 2015 (co-advised with Jonathan Taylor)
Selective Inference and Learning Mixed Graphical Models
Associate Professor of Electrical Engineering and Statistics, UC Berkeley.
13. Michael Lim, 2013
The Group Lasso: Two Novel Applications.
Quantitative finance, The Voleon Group, San Francisco (2017 - )
12. Rahul Mazumder, 2012
Topics in Sparse Multivariate Statistics.
Professor, Operations Research and Statistics group at MIT Sloan School of Management. http://www.mit.edu/~rahulmaz/
11. Donal McMahon, 2009
Research Synthesis for Multiway Tables of Varying Shapes and Size
Chief AI Officer, Lightspeed, Austin, Texas
10. Ping Li, 2007
9. Gill Ward, 2007
Statistics in Ecological Modeling: Presence-Only Data and Boosted Mars
UX Research Lead for Fitbit Mobile and Privacy Foundations
8. Mee-Young Park, 2006
Generalized Linear Models with Regularization
Director of Data Science, Google Cloud.
7. Hui Zou, 2005
Some Perspectives of Sparse Statistical Modeling
Professor, Department of Statistics, University of Minnesota
http://www.stat.umn.edu/~hzou
6. Saharon Rosset, 2003 (co-advised with Jerome Friedman)
Topics in Regularization and Boosting
Professor, School of Mathematical Sciences, Tel Aviv University
http://www.tau.ac.il/~saharon
5. Ji Zhu, 2003
Flexible Statistical Modelling
Professor, Department of Statistics, University of Michigan
http://www.stat.lsa.umich.edu/~jizhu/
4. Mu Zhu, 2001
Feature Extraction and Dimension Reduction with Applications to Classification and the Analysis of Co-occurrence Data
Professor, Statistics Department, University of Waterloo, Canada
http://www.stats.uwaterloo.ca/~m3zhu
3. Gareth James, 1998
Majority Vote Classifiers: Theory and Applications
John. H. Harland Dean and Professor of Information Systems and Operations Management, Goizueta Business School, Emory University, Atlanta, GA.
2. Dan Rubinstein, 1997
Discriminative versus Informative Learning
CEO and co-founder of Reflectivity (acquired by Texas Instruments)
CEO at Physera, Inc., Palo Alto | Product Management at Google | Facebook | Palantir.
1. Neil Crellin, 1996
Modeling Image Sequences, with Particular Applications to FMRI Data
Site Reliability Manager, Google, Mountain View
Past Post-Doctoral Students
Xiao Wu, 2022 (Stanford Data Science Initiative)
Assistant Professor Of Statistics, Columbia University, New York.
Lukasz Kidzinski, 2016 - 2019 (Mobilize Center)
Ph.D Universite Libre de Bruxelles
AI Researcher at
Saliency.ai. See kidzinski.com
Julia Viladomat, 2011 - 2013
Ph.D Universidad Carlos III de Madrid
Data Scientist Adobe Labs
Dirk Ormoneit, 2000 - 2001
Director of Research, Bluecrest Capitol Management, London
http://robotics.stanford.edu/~ormoneit
Eva Cantoni, 1999 - 2000
Associate Professor, Faculty of Economics and Social Science, University of Geneva.
http://www.unige.ch/ses/dsec/staff/faculty/Cantoni-Eva.html