BS, Oklahoma State University; MS, University of Southern California; Ph.D., University of California
I’m a professor in the Carlson Center for Imaging Science at the Rochester Institute of Technology (RIT). I’m also Associate Director of RIT’s Center for Human-aware AI (CHAI), and Affiliate Faculty in RIT’s Computer Science Department. I am a member of the McNair Scholars Advisory Board, part of RIT’s Division of Diversity and Inclusion. I direct the Machine and Neuromorphic Perception Laboratory (a.k.a., kLab). My lab’s main focus is basic research in task-driven scene understanding and lifelong machine learning. Our recent work has focused on visual question answering (VQA) and incremental learning in deep networks. We also do applied research in using deep learning to solve problems in computer vision. In the past, I have worked on semantic segmentation, object recognition, object detection, active vision, object tracking, and more. Beyond machine learning, I also have a strong background in eye tracking, primate vision, and theoretical neuroscience.
In the News
March 23, 2021
New AI from RIT researchers can play Starcraft II; project is DARPA-funded
WROC-TV talks to Christopher Kanan, assistant professor in the Chester F. Carlson Center for Imaging Science, about an artificial intelligence project.
March 12, 2021
RIT researchers helping to develop artificial intelligence systems capable of playing 'Starcraft II'
A team of researchers that develops artificial intelligence systems is putting its work to a unique new test: creating machines capable of playing the popular video game Starcraft II. Researchers think it could be an important stepping stone to advancing practical solutions such as self-driving cars, service robots, and other real-world applications.
April 23, 2020
Fixing the forgetting problem in artificial neural networks
An RIT scientist has been tapped by the National Science Foundation to solve a fundamental problem that plagues artificial neural networks. Christopher Kanan, an assistant professor in the Chester F. Carlson Center for Imaging Science, received $500,000 in funding to create multi-modal brain-inspired algorithms capable of learning immediately without excess forgetting.