Adam Tauman Kalai
Adam Tauman Kalai | |
|---|---|
| Nationality | United States |
| Alma mater | Harvard University Carnegie Mellon University |
| Scientific career | |
| Fields | Computer Science, Artificial Intelligence |
| Institutions | Toyota Technological Institute at Chicago Georgia Tech Microsoft Research |
| Doctoral advisor | Avrim Blum |
Adam Tauman Kalai is an American computer scientist who specializes in Machine Learning and works as a Senior Principal Researcher at Microsoft Research New England[1][2][3].
Education and career
Kalai graduated from Harvard University in 1996 and received a PhD from Carnegie Mellon University in 2001, where he worked under doctoral advisor Avrim Blum. He did his postdoctoral study at the Massachusetts Institute of Technology before becoming a faculty member at the Toyota Technological Institute at Chicago and then the Georgia Institute of Technology. He joined Microsoft Research in 2008.[2][3]
Contributions
Kalai is known for his algorithm for generating random factored numbers (see Bach's algorithm), for efficiently learning learning mixtures of Gaussians, for the Blum-Kalai-Wasserman algorithm for learning parity with noise, and for the intractability of the folk theorem in game theory.
More recently, Kalai is known for identifying and reducing gender bias in word embeddings, which are a representation of words commonly used in AI systems.[1][4]
References
- ^ a b Pinkerton, Byrd (August 12, 2016), He's Brilliant, She's Lovely: Teaching Computers To Be Less Sexist, National Public Radio (NPR), retrieved 2019-01-28
- ^ a b Artificial Intelligence and Statistics Conference, 2016, retrieved 2019-01-28
- ^ a b Adam Kalai, Senior Principal Researcher, Microsoft, retrieved 2019-12-03
- ^ Gholipour, Bahar (March 10, 2017), Algorithms Learn From Us, and We Can Be Better Teachers, NBC, retrieved 2019-09-01
External links
- Adam Tauman Kalai publications indexed by Google Scholar
| Authority control databases: Academics |
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