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Martin J. Wainwright

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Biography Research Papers Teaching Prospective Students Group members Directions to Campus  

Department of Electrical Engineering and Computer Sciences
Department of Statistics
University of California at Berkeley

Contact information

EECS information:

263 Cory Hall
Berkeley, CA 94720
Phone: (510) 643-1978
Fax: (510) 643-7846
Email: x@y with x=wainwrig
Statistics information:

421 Evans Hall #3860
Berkeley, CA 94720-3860
Phone: (510)-643-1975
Fax: (510) 642-7892
Email: x@y with x=wainwrig

Research affiliations and useful links

  • BLISS: Berkeley Laboratory for Information and System Sciences

  • BAIR: Berkeley Artificial Intelligence Lab

    Some seminars and reading groups

    Upcoming seminars in the Department of Statistics.
    Signal Processing, Networking and Communication Seminar in the Department of EECS.

    Research areas

  • Optimization in statistical settings
  • High-dimensional statistics
  • Ranking, crowd-sourcing and related problems
  • Statistics and privacy
  • Non-parametric statistics
  • Graphical models and variational methods


  • T. Hastie, R. Tibshirani and M. J. Wainwright (2015). Statistical Learning with Sparsity: the Lasso and Generalizations. Chapman and Hall/CRC Press, Series in Statistics and Applied Probability.

  • M. J. Wainwright and M. I. Jordan (2008). Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, Vol. 1, Numbers 1--2, pp. 1--305, December 2008.


    High-dimensional statistics
    Optimization in statistical settings
    Ranking problems
    Statistics and privacy
    Non-parametric statistics
    Graphical models and message-passing
    Coding, data compression, algorithms
    Statistical image processing
    Statistical approaches to biological vision

    Tutorial materials

  • Tutorial Materials on High-Dimensional Statistics:
  • Slides from lectures (PDF)
  • Statistical Science Paper (PDF)

  • Tutorial Materials on Graphical Models, Variational Methods and Message-Passing
    Machine Learning Summer School, Kyoto, Japan. September 2012
  • Slides (Part I) Basics, max-product and LP relaxation
  • Slides (Part II) Sum-product, variational formulation
  • Slides (Part III) Learning graphical models from data
  • Rough lecture notes: On factorization, Markov properties, Hammersley-Clifford, message-passing algorithms, junction tree, and basic aspects of graphical model estimation.
  • Wainwright and Jordan monograph: More advanced material on exponential families, duality, and variational methods.

  • Current group members

    Graduate students

  • Raaz Dwivedi
  • Ashwin Pananjady
  • Mert Pilanci
  • Nihar Shah
  • Yuting Wei
  • Fanny Yang
  • Yuchen Zhang

    Postdoctoral researchers

  • Aaditya Ramdas
  • Yun Yang


  • Alekh Agarwal Research Scientist, Microsoft Research, New York.
  • Arash Amini Assistant Professor, Dept. of Statistics, UCLA
  • Sivaraman Balakrishnan, Assistant Professor, Dept. of Statistics, CMU
  • Joseph Bradley Databricks
  • Yudong Chen, Assistant Professor, ORIE, Cornell University
  • Alexandros D.G. Dimakis Associate Professor, Department of ECE, UT Austin
  • John Duchi, Assistant Professor, Departments of Statistics and EE, Stanford University
  • Pamela Lee, Associate, Gibson, Dunn & Crutcher
  • Po-Ling Loh, Assistant Professor, Department of Statistics, Univ. Pennsylvania
  • Matt Johnson, Postdoc, Harvard University
  • Johannes Lederer, Assistant Professor, University of Washington
  • Sahand Negahban Assistant Professor, Department of Statistics, Yale University.
  • XuanLong Nguyen Associate Professor, Department of Statistics, Univ. Michigan
  • Nima Noorshams, Qualcomm Research
  • Jonas Peters Group leader, Max Planck Institute, Tuebingen
  • Garvesh Raskutti Assistant Professor, Department of Statistics, Univ. Wisconsin-Madison
  • Pradeep Ravikumar Associate Professor, Department of CS, UT Austin
  • Prasad Santhanam Assistant Professor, Department of ECE, University of Hawaii
  • Mahdi Soltanolkotabi, Assistant Professor, Department of EE, University of Southern California
  • Sameer Vermani, Engineer, Qualcomm
    Last updated 09/2014