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Robert Elias Schapire is an American computer scientist renowned for his contributions to machine learning theory and its applications. He was formerly a computer science professor at Princeton University before joining Microsoft Research. His research focuses on theoretical and applied machine learning, with particular emphasis on ensemble learning.
Robert Elias Schapire | |
|---|---|
| Alma mater | Brown University Massachusetts Institute of Technology |
| Known for | AdaBoost |
| Awards | Gödel Prize (2003) Paris Kanellakis Award (2004) |
| Scientific career | |
| Fields | Computer science |
| Institutions | Microsoft Research AT&T Labs Princeton University |
| Thesis | The design and analysis of efficient learning algorithms (1991) |
| Doctoral advisor | Ronald Rivest |
| Website | http://rob.schapire.net/ |
Career
Schapire's most significant contribution to computer science is the development of boosting, a fundamental ensemble learning algorithm that has revolutionized machine learning. His doctoral dissertation, The design and analysis of efficient learning algorithms, earned him the ACM Doctoral Dissertation Award in 1991.[1] In 1996, collaborating with Yoav Freund, he invented the AdaBoost algorithm, a breakthrough that led to their joint receipt of the Gödel Prize in 2003.
Schapire was elected an AAAI Fellow in 2009.[2] In 2014, he was elected a member of the National Academy of Engineering for his contributions to machine learning through the invention and development of boosting algorithms.[3] In 2016, he was elected to the National Academy of Sciences.[4]
Selected works
Books
- Robert Schapire; Yoav Freund (2012). Boosting: Foundations and Algorithms. MIT. ISBN 978-0-262-01718-3.
References
External links
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