ML Meets Economics: New Perspectives and Challenges

While there has been significant progress in the theory and practice in
machine learning in recent years, many fundamental challenges remain.  Some
are mathematical in nature, such as the challenges associated with optimization
and sampling in high-dimensional spaces.  Some are statistical in nature,
including the challenges associated with multiple decision-making.  Others
are economic in nature, including the need to price services and provide
incentives in data-based markets.  And others are systems challenges, arising
from the need for highly-scalable, robust and understandable hardware and
software platforms.  I will overview these challenges, focusing on the economic

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Jordan Michael
About Michael I. Jordan

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 Masters 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. His 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.