Martin Wainwright is the Cecil H. Green Professor in Electrical Engineering and Computer Science and Mathematics at MIT, and affiliated with the Laboratory for Information and Decision Systems and Statistics and Data Science Center. He joined the MIT faculty in July 2022 from the University of California at Berkeley, where he held the Howard Friesen Chair with a joint appointment between EECS and Statistics.
Wainwright received his Bachelor’s degree in Mathematics from the University of Waterloo, Canada, and his PhD in Electrical Engineering and Computer Science at MIT, for which he was awarded the George M. Sprowls Prize in 2002. He is broadly interested in statistics, machine learning, information theory and optimization. He has received a number of awards and recognition including an Alfred P. Sloan Foundation Fellowship, best paper awards from the IEEE Signal Processing Society, the IEEE Communications Society, and the IEEE Information Theory and Communication Societies, the Medallion Lectureship and Award from the Institute of Mathematical Statistics, and the COPSS Presidents’ Award from the Joint Statistical Societies. He was a Section Lecturer with the International Congress of Mathematicians in 2014 and received the Blackwell Award from the Institute of Mathematical Statistics in 2017. He has co-authored several books, including on graphical models with Michael Jordan, on sparse statistical modeling with Trevor Hastie and Rob Tibshirani, and a solo-authored book on high dimensional statistics.
- 2019: High-dimensional statistics: A non-asymptotic viewpoint. M.J. Wainwright. Cambridge University Press. Errata page
- 2015: Statistical Learning with Sparsity: the Lasso and Generalizations. T. Hastie, R. Tibshirani and M.J. Wainwright. Chapman and Hall/CRC Press, Series in Statistics and Applied Probability.
- 2008: Graphical models, exponential families, and variational inference. M.J. Wainwright and M.I. Jordan. Foundations and Trends in Machine Learning, Vol. 1, Numbers 1-2, pp. 1-305, December 2008.
- 2023: Kernel-based off-policy estimation without overlap: Instance optimality beyond semiparametric efficiency. Wenlong Mou, Peng Ding, Martin J. Wainwright, and Peter L. Bartlett.
- 2022: Krylov-Bellman boosting: Super-linear policy evaluation in general state spaces. Eric Xia and Martin J. Wainwright.
- 2022: Optimally tackling covariate shift in RKHS-based nonparametric regression. Cong Ma, Reese Pathak, and Martin J. Wainwright. Annals of Statistics, to appear.
- 2021: Optimal policy evaluation using kernel-based temporal difference methods. Yaqi Duan, Mengdi Wang, and Martin J. Wainwright.
- 2018: Minimax Optimal Procedures for Locally Private Estimation. John C. Duchi, Michael I. Jordan, and Martin J. Wainwright. Journal of the American Statistical Association.
- 2014: Constrained forms of statistical minimax: Computation, communication and privacy. M.J. Wainwright. In proceedings of the International Congress of Mathematicians, Seoul, Korea.