Michael Hind  Michael Hind photo         

contact information

Academy of Technology LogoDistinguished Research Staff Member
IBM Research AI, IBM Thomas J. Watson Research Center, Yorktown Heights, NY USA
  +1dash914dash945dash2712

links

Professional Associations

Professional Associations:  ACM SIGPLAN

more information

More information:  Twitter

profile


Michael Hind is a Distinguished Research Staff Member in the IBM Research AI department at the T.J. Watson Research Center in Yorktown Heights, New York.

Michael received his Ph.D. from New York University in 1991.  After 2+ years at IBM Research, working on the PTRAN (automatic parallelization) and other projects, he spent 6 years an assistant and associate professor of computer science at the State University of New York at New Paltz, as well as concurrently holding various positions at IBM Research. In 1998, Michael became a fulltime Research Staff Member at the IBM Research, working on the Jalapeno project, the project that produced the open source Jikes RVM, a self-optimizing Java virtual machine. In 2000 and 2007, he became the manager of the Dynamic Optimization Group and Senior Manager of the Programming Technologies Department at IBM Research, respecitively.  In 2014, he became a Distinguised Research Staff member, and in 2016 became the Senior Manager of the Cognitive Software Lifecycle Department at IBM Research.  In 2017, he became passionate about Explainability, Reliability, and Bias of AI and is now focusing his time on these topics with his amazing colleagues.

Michael is an ACM Distinguished Scientist, a member of the IBM Academy of Technology, a former associate editor of ACM TACO, and a past member of ACM SIGPLAN's Executive Committee.  He has served on over 30 program committees, given talks at top universities and conferences, and co-authored over 40 publications. He received a SIGPLAN Most Influential Paper award (for his OOPSLA 2000 paper) and was part of the Jikes RVM team that received the SIGPLAN Software Award in 2012. His research interests include explainability and bias in AI and larger societal implications for AI, the software lifecycle for creating, deploying, and maintain AI applications, programming models and their implementations, static and dynamic development tools, and middleware for emerging commercial paradigms.

 



Publications, Awards and Other Activities, Invited Presentation, Tutorials and Courses, Program Committees


Awards, Services, and Other Activities

 

Invited Talks

 

Tutorials and Courses

 

Program Committees