Past Ph.D Students
 Past Post-Doctoral Students
 Present Ph.D Students

Present Ph.D Students

Disha Ghandwani
Anav Sood
James Yang


Graduated Ph.D Students

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 2022.

23. Elizabeth Chin, 2022 (co-advised with Euan Ashley)
Statistical and Algorithmic Approaches for Health Policy and Fairness
Asst. Prof, Johns Hopkins Biostatistics 2022.

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.

18. Charles Zheng, 2017
Supervised Learning of Representations
(co-advided with Jonathan Taylor, and mentored by Yuval Benjiamini)
Researcher in Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health.

17. Hristo Spassimirov Paskov, 2016
Learning with N-grams: from Massive Scales to Compressed Representations.
(co-advised with John Mitchell, CS).
Arthur Samuel Award for best dissertation in Computer Science at Stanford.
Research group at Apple

16. Qingyuan Zhao, 2016
Topics in Causal and High Dimensional Inference
University Lecturer in Statistics, University of Cambridge

15. Will Fithian, 2015
Topics in Adaptive Inference
Associate Professor, University of California, Berkeley.

14. Jason Lee, 2015
Selective Inference and Learning Mixed Graphical Models
(co-advised with Jonathan Taylor)
Associate professor of Electrical and Computer Engineering and Computer Science (secondary), Princeton University.

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.
Associate Professor, Operations Research and Statistics group at MIT Sloan School of Management

11. Donal McMahon, 2009
Research Synthesis for Multiway Tables of Varying Shapes and Size
Group Manager of Data Platforms and Head of Data Science at, Austin, Texas

10. Ping Li, 2007
Stable Random Projections and Conditional Random Sampling, Two Sampling Techniques for Modern Massive Datasets
Distinguished Engineer, LinkedIn.

9. Gill Ward, 2007
Statistics in Ecological Modeling: Presence-Only Data and Boosted Mars
Google (Youtube), Mountain View.

8. Mee-Young Park, 2006
Generalized Linear Models with Regularization
Quantitative Analyst, Google, Mountain View

7. Hui Zou, 2005
Some Perspectives of Sparse Statistical Modeling
Professor, Department of Statistics, University of Minnesota

6. Saharon Rosset, 2003
Topics in Regularization and Boosting
(co-advised with Jerome Friedman)
Professor, School of Mathematical Sciences, Tel Aviv University

5. Ji Zhu, 2003
Flexible Statistical Modelling
Professor, Department of Statistics, University of Michigan

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

3. Gareth James, 1998
Majority Vote Classifiers: Theory and Applications
Vice Dean for Faculty and Academic Affairs, E. Morgan Stanley Chair in Business Administration, Professor of Data Sciences and Operations, Marshall School of Business, USC

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

Lukasz Kidzinski, 2016-2019 (Mobilize Center)
Ph.D Universite Libre de Bruxelles
AI Researcher at See

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

Eva Cantoni, 1999-2000
Associate Professor, Faculty of Economics and Social Science, University of Geneva.

 © copyright 2003 Trevor Hastie - All rights reserved.