Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY
Michael Hind

Michael Hind
Distinguished Research Staff Member, IBM Research AI


Michael Hind is a Distinguished Research Staff Member in the IBM Research AI organization in Yorktown Heights, New York. 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. 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, a former Associate Editor of ACM TACO, has served on over 30 program committees, given talks at top universities and conferences, and co-authored 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.


9:00am12:30pm Tuesday, April 16, 2019
Implementing AI
Location: Regent Parlor
Secondary topics:  Deep Learning and Machine Learning tools, Ethics, Privacy, and Security
Rachel Bellamy (IBM Research), Kush Varshney (IBM Research), Karthikeyan Natesan Ramamurthy (IBM), Michael Hind (IBM Research AI)
Learn to use and contribute to the new open-source Python package AI Fairness 360 directly from its creators. Architected to translate new developments from research labs to data science practitioners in industry, this is the first comprehensive toolkit with metrics to check for unwanted bias in datasets and machine learning models, and state-of-the-art algorithms to mitigate such bias. Read more.