While my time at the Rowland Institute has been amazing, my (five-year, non-renewable) term here is up, and it’s time for me to move on.
Luckily, I won’t have to move far, as I will be joining the Dept. of Molecular and Cellular Biology and the Center for Brain Science over on the main campus here at Harvard. I’m extremely excited by the new opportunities that joining this community opens up, and I very much look forward to starting this fall!
I participated today in an interesting discussion about face recognition technology and related privacy issues on National Public Radio with host Marty Moss-Coane and U Penn Law Professor Anita Allen. Thanks to the entire Radio Times crew at WHYY for the stimulating discussion.
A link to a recording of the live show can be found here
I’m helping to organize a workshop at the IEEE Computer Vision and Pattern Recognition conference this year in Colorado Springs on “Biologically Consistent Computer Vision.” We received an impressive array of submissions, and the workshop promises to be a good opportunity for those interested in biologically-inspired vision to come together and share ideas. See the workshop site for more details.
An online streaming version of a talk that Nicolas and I made in September at the GPU Technology Conference is now available here. In it, we talk about our work using GPUs to tackle large-scale face recognition problems.
Also, be sure to check out Nicolas’ talk on GPU Metaprogramming Techniques.
We’re grateful and honored to have just been selected to receive a Google Research Award to help support our work with biologically-inspired vision systems. The Google Research Award program is part of Google’s broader effort to engage and support academic faculty pursuing research topics of mutual interest. We’re looking forward to working more closely with Google in the future. See the official announcement here.
In our new paper in PLoS Computational Biology, we describe our efforts to use GPU-accelerated high-throughput screening to find powerful new, biologically-inspired computer vision models.
Also check out the ResearchCast describing the work:
In our new paper in PNAS, we present evidence that rats can perform high-level transformation-tolerant visual object recognition, an ability not traditionally ascribed to rodents. We believe that the availability of rats as a model system for high-level has great potential to accelerate our understanding of high-level vision, by providing a simpler, more accessible example system to study.