Martin Wainwright

Martin Wainwright is a Professor at the University of California at Berkeley with joint appointments in the Department of Statistics and the Department of Electrical Engineering and Computer Sciences (EECS). His research spans high-dimensional statistics, information theory, statistical machine learning, and optimization theory. He has made significant contributions to understanding computational and statistical trade-offs in high-dimensional settings, as well as developing advanced message-passing algorithms for graphical models.

His educational background includes a Bachelor's degree in Mathematics from the University of Waterloo and a Ph.D. in EECS from MIT. His work has been recognized with prestigious awards such as the COPSS Presidents' Award (2014), IEEE Joint Paper Award (2012), and Sloan Research Fellowship (2005). He has advised numerous prominent researchers, including Nihar Shah, John Duchi, and Yuchen Zhang.

Publications by Wainwright reflect trends in machine learning, high-dimensional data analysis, and graphical model inference. Notable works include advancements in Markov Chain Monte Carlo algorithms, pairwise comparison models, and distributed computation methods. He has also contributed extensively to signal processing and LDPC codes.

• COPSS Presidents' Award (2014)
• IEEE Joint Paper Award (2012)
• Institute of Mathematical Statistics Fellow (2011)
• NSF CAREER Award (2006)
• Okawa Research Grant (2005)
• Sloan Research Fellow (2005)