Statistics Department Seminar Series: Ambuj Tewari, Professor of Statistics, University of Michigan

Abstract: The origins of the field of machine learning can be traced back to a remarkable section titled “Learning Machines” in Turing’s famous “Computing Machinery and Intelligence” paper. The paper, one of Turing’s two great papers, was published in 1950 in the philosophy journal Mind. In the past 70 years, the field has grown enormously. Technological progress has given us object recognition, speech recognition, and machine translation systems that we now take for granted. There has also been tremendous progress in understanding learning machines from the statistical, computational and mathematical viewpoints. Yet, what Turing wrote at the end of his 1950 paper remains true as ever: “We can only see a short distance ahead, but we can see plenty there that needs to be done.”

In this talk, I will provide brief non-technical descriptions of a few projects, both applied and theoretical, that my research group is working on. I also hope to convey the message that machine learning, like any technology, is amoral. Whether it is put to good or bad uses in society is up to us. If learning algorithms control access to credit, healthcare, education, and employment, it becomes an ethical obligation to examine issues related to fairness, accountability and transparency in machine learning.

Speaker Bio:

Ambuj Tewari is an associate professor in the Department of Statistics and the Department of EECS (by courtesy) at the University of Michigan, Ann Arbor. His is also affiliated with the Michigan Institute for Data Science (MIDAS). He obtained his PhD under the supervision of Peter Bartlett at the University of California at Berkeley. His research interests lie in machine learning including statistical learning theory, online learning, reinforcement learning and control theory, network analysis, and optimization for machine learning. He collaborates with scientists to seek novel applications of machine learning in mobile health, learning analytics, and computational chemistry. His research has been recognized with paper awards at COLT 2005, COLT 2011, and AISTATS 2015. He received an NSF CAREER award in 2015 and a Sloan Research Fellowship in 2017.