Michael Hind is a Distinguished Research Staff Member in the IBM Research AI department in Yorktown Heights, New York. His current research passion is in the general of area of Trusted AI, focusing on the fairness, explainability, transparency, and reliability of the construction of AI systems.

Michael has led dozens of researchers focusing on programming languages, software engineering, cloud computing, and tools for AI systems. Michael's team has successfully transferred technology to various parts of IBM and launched several successful open source projects, Jikes RVM, X10, WALA, OpenWhisk, and more recenty AI Fairness 360 and AI Explainability 360. After receiving his Ph.D. from NYU in 1991, Michael spent 7 years as an assistant/associate professor of computer science at SUNY - New Paltz.

Michael is an ACM Distinguished Scientist, and a member of IBM's Academy of Technology. He has co-authored over 50 publications, served on over 50 program committees, and given many keynotes and invited talks at top universities, conferences, and government settings. His 2000 paper on Adaptive Optimization was recognized as the OOPSLA'00 Most Influential Paper and his work on Jikes RVM was recognized with the SIGPLAN Software Award in 2012.

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