Trevor Darrell
Trevor Jackson Darrell is an American computer scientist and professor at the University of California, Berkeley.[1][2] He is known for his research on computer vision and machine learning[3][4] and is one of the leading experts on topics such as deep learning[5] and explainable AI.[6]
Trevor Darrell | |
|---|---|
| Nationality | American |
| Alma mater | MIT |
| Scientific career | |
| Fields | Computer Science |
| Institutions | University of California, Berkeley |
| Doctoral advisor | Alex Pentland |
| Website | https://people.eecs.berkeley.edu/~trevor/ |
Darrell's group at UC Berkeley developed the Caffe deep learning library.[7]
Education
- 1996, Ph.D., Massachusetts Institute of Technology
- 1991, M.S., Massachusetts Institute of Technology
- 1988, B.S.E., University of Pennsylvania
References
- "Faculty homepage".
- "Top H-Index For Scientists in University of California, Berkeley". www.guide2research.com. Retrieved 2019-04-13.
- "Trevor Darrell - Google Scholar Citations". scholar.google.com. Retrieved 2019-04-13.
- "DBLP: Trevor Darrell".
- "BBC World Service - The Forum, Deep Learning". BBC. Retrieved 2019-04-13.
- Kuang, Cliff (2017-11-21). "Can A.I. Be Taught to Explain Itself?". The New York Times. ISSN 0362-4331. Retrieved 2019-04-13.
- Jia, Yangqing; Shelhamer, Evan; Donahue, Jeff; Karayev, Sergey; Long, Jonathan; Girshick, Ross; Guadarrama, Sergio; Darrell, Trevor (2014). "Caffe: Convolutional Architecture for Fast Feature Embedding". Proceedings of the 22Nd ACM International Conference on Multimedia. MM '14. New York, NY, USA: ACM: 675–678. arXiv:1408.5093. doi:10.1145/2647868.2654889. ISBN 9781450330633.
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