Michael Hind is a distinguished research staff member in the IBM Research AI organization. His current research passion is in the general of area of trusted AI, focusing on the fairness, explainability, and reliability of the construction of AI systems. Previously, he led departments of dozens of researchers focusing on programming languages, software engineering, cloud computing, and tools for cognitive systems. Michael’s team has successfully transferred technology to various parts of IBM and launched several successful open source projects. Previously, Michael spent seven years as an assistant/associate professor of computer science at SUNY New Paltz. Michael is an ACM Distinguished Scientist, a member of IBM’s Academy of Technology, and a former associate editor of ACM TACO. He has served on over 30 program committees, given talks at top universities and conferences, and coauthored over 40 publications. 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. He holds a PhD from NYU.
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