Speaker: Prof. Michael Jordan, Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California.

Title: Machine Learning: Dynamical, Statistical and Economic Perspectives

Abstract: While there has been significant progress at the interface of statistics and computer science in recent years, many fundamental challenges remain. Some are mathematical and algorithmic in nature, such as the challenges associated with optimization and sampling in high-dimensional spaces. Some are statistical, including the challenges associated with multiple decision-making. Others are economic in nature, including the need to cope with scarcity and provide incentives in learning-based two-way markets. The author will present recent progress on each of these fronts.

About the Speaker: Prof. Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Master’s in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. He is research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.