Evan Shelhamer
About
Appointed Canada CIFAR AI Chair – 2025
Evan Shelhamer is a Canada CIFAR AI Chair at the Vector Institute and an assistant professor at the University of British Columbia. His research focuses on visual recognition and adaptation – how to identify and locate useful information in image data and then update when the data changes. Shelhamer focuses on test-time adaptation for updating new and different data during deployment, and self-supervised learning for learning from massive amounts of data without expert annotations. Recently, he has been working more on AI for science and sustainability through remote sensing and data collected from satellites.
Awards
- Test-of-Time Award for Caffe, ACM Multimedia, 2024
- Mark Everingham Prize, IEEE, 2017
- Best Paper Honorable Mention for Fully Convolutional Networks, CVPR, 2015
- Open Source Award for Caffe, ACM Multimedia, 2014
- NSF Graduate Research Fellowship, 2012-2015
Relevant Publications
- Tseng, G., Fuller, A., Reil, M., Herzog, H., Beukema, P., Bastani, F., Green, J.R., Shelhamer, E., Kerner, H. and Rolnick, D. (2025) “Galileo: Learning Global & Local Features of Many Remote Sensing Modalities” ICML.
- Croce, F., Gowal, S., Brunner, T., Shelhamer, E., Hein, M. and Cemgil, T. (2022) “Evaluating the Adversarial Robustness of Adaptive Test-time Defenses” ICML.
- Wang, D., Shelhamer, E., Liu, S., Olshausen, B. and Darrell, T. (2021) “Tent: Fully Test-Time Adaptation by Entropy Minimization” ICLR.
- Long, J., Shelhamer, E. and Darrell, T. (2015) “Fully Convolutional Networks for Semantic Segmentation” CVPR.
- Yangqing, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S. and Darrell, T. (2014) “Caffe: Convolutional architecture for fast feature embedding” ACM Multimedia, pp. 675-678.