Evan Shelhamer
Evan Shelhamer is an assistant professor at the University of British Columbia, and his research covers vision, learning, and adaptation. He led development of Caffe, an open-source deep learning framework, from version 0.1 to 1.0. With Jonathan Long and Trevor Darrell, he wrote Fully Convolutional Networks for Semantic Segmentation, published at CVPR in 2015, and the paper received a CVPR test-of-time award in 2025. He completed his PhD at UC Berkeley in 2019, worked at Adobe and Google DeepMind, and returned to academia in 2025. The IEEE PAMI Mark Everingham Prize honored Caffe in 2017.
Timeline
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2014
Leading the Caffe framework
He co-authors the Caffe paper published at ACM Multimedia in 2014, and he leads development of the open-source Caffe deep learning framework from version 0.1 to 1.0.
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2015–2025
Fully convolutional networks
With Jonathan Long and Trevor Darrell, he publishes Fully Convolutional Networks for Semantic Segmentation at CVPR in 2015, and the paper receives a CVPR test-of-time award in 2025.
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2017
The Mark Everingham Prize
The IEEE PAMI Mark Everingham Prize honors Caffe in 2017; he served as its lead developer.
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2019
A Berkeley doctorate
Advised by Trevor Darrell, he completes a PhD in computer science at UC Berkeley in 2019 and then works in industry at Adobe and Google DeepMind.
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2025
Returning to academia in Canada
In 2025 he moves to Canada and returns to academia.
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As of October 2026
An assistant professor at UBC
Working as an assistant professor at the University of British Columbia, he also holds a Canada CIFAR AI Chair at the Vector Institute. His research covers vision, learning, and adaptation.
Sources and credits
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