Michael Hind
Distinguished Research Staff Member, IBM Research AI Department
Town of Somers, New York, United States
Summary
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Rockstar🎓
Top SchoolMichael Hind is a Distinguished Research Staff Member at IBM Research's AI Department with over two decades of research and leadership experience in programming technologies, dynamic optimization, and the software lifecycle for AI. He leads work on AI explainability, fairness, and the broader societal implications of AI, bringing an unusually deep blend of technical rigor and applied ethics to industrial research. A long-standing member of the IBM Academy of Technology, he has led and mentored large teams of researchers and steered organizational programs that move AI from prototype to production. His career traces back through academia to a PhD in Computer Science from NYU, evidencing a sustained commitment to foundational research as well as practical systems. Based in Somers, New York, he combines institutional influence with hands-on research leadership that shapes how AI systems are built, explained, and governed.
11 years of coding experience
24 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at New York UniversityBachelor of Arts (B.A.), Mathematics and Computer Science, Bachelor of Arts (B.A.), Mathematics and Computer Science at SUNY New PaltzMaster of Science (MS), Computer Science, Master of Science (MS), Computer Science at NYU
Github Skills (15)
algorithms10
xai10
machine-learning10
artificial-intelligence10
discrimination10
deep-learning10
python10
machine-learning-models10
ai10
bias-detection10
r10
interpretable-machine-learning9
transparency8
privacy-enhancing-technologies8
data-science5
Programming languages (2)
Github contributions (5)
michaelhind/SIGPLAN.github.io
May 2015 - Jun 2015
Contributions:8 PRs, 4 pushes, 5 branches in 1 month
Trusted-AI/AIF360
Sep 2018 - Mar 2020
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Contributions:28 commits, 33 PRs, 32 pushes in 1 year 6 months
machine-learning-modelsaifairness-aifairness-testingfairness-awareness-model
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