Evan Shelhamer is an assistant professor at the University of British Columbia in Vancouver and a faculty member at the Vector Institute. He earned his PhD at UC Berkeley in 2019 advised by Prof. Trevor Darrell. While there he was the lead developer of the Caffe open-source deep learning framework from version 0.1 to 1.0. Before the PhD, he earned dual degrees in computer science (artificial intelligence concentration) and psychology at UMass Amherst advised by Erik Learned-Miller. Following the PhD, he worked in industry as a research scientist for five years at Google DeepMind and Adobe across Cambridge (MA), London (UK), and San Francisco (CA). In 2025 he came to Canada and returned to academia.

His research is on vision, learning, and adaptation: how to identify and locate useful information in visual data like images and then update when the data changes. His work and service have received international awards including the best paper honorable mention at CVPR’15 and test-of-time award at CVPR’25 for fully convolutional networks, and the Mark Everingham award at ICCV’17, the open-source award at MM’14, and the test-of-time award at MM’24 for Caffe. The purpose of his work is to equip machine learning models and the scientific community to look twice, keep updating, and never stop learning.