My research lies at the intersection of computer vision and machine learning and focuses on tackling real-world variation and scale while minimizing human supervision. I develop learning algorithms which facilitate transfer of information through unsupervised and semi-supervised model adaptation and generalization.
I am a Postdoctoral Researcher collaborating with Alyosha Efros and Trevor Darrell at UC Berkeley. I received my PhD in Electrical Engineering and Computer Science from University of California, Berkeley in Summer 2016, where I was advised by Trevor Darrell and Kate Saenko, and worked closely with many members of the Berkeley Computer Vision group. I was awarded the NSF Graduate Fellowship and the Rosalie M. Stern Fellowship. My thesis focused on transferrable representation learning for visual recognition. I received a BS in Electrical Engineering and Computer Science in 2010 from UC Berkeley.
I love to get out of the lab and travel, hike, and generally just enjoy the outdoors. My favorite recent destinations have been Glacier National Park, Alaska, and the Swiss Alps.
Feel free to contact me at jhoffman __at__ eecs.berkeley.edu.