Adam Tauman Kalai | |
|---|---|
 | |
| Nationality | American |
| 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] .
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]
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]
Ronald Linn Rivest is a cryptographer and an Institute Professor at MIT. He is a member of MIT's Department of Electrical Engineering and Computer Science (EECS) and a member of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His work has spanned the fields of algorithms and combinatorics, cryptography, machine learning, and election integrity.
The School of Computer Science (SCS) at Carnegie Mellon University in Pittsburgh, Pennsylvania, US is a school for computer science established in 1988. It has been consistently ranked among the top computer science programs over the decades. As of 2022 U.S. News & World Report ranks the graduate program as tied for second with Stanford University and University of California, Berkeley. It is ranked second in the United States on Computer Science Open Rankings, which combines scores from multiple independent rankings.
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Parity learning is a problem in machine learning. An algorithm that solves this problem must find a function ƒ, given some samples and the assurance that ƒ computes the parity of bits at some fixed locations. The samples are generated using some distribution over the input. The problem is easy to solve using Gaussian elimination provided that a sufficient number of samples are provided to the algorithm.
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