Christopher Kanan Headshot

Christopher Kanan

Associate Professor

Chester F. Carlson Center for Imaging Science
College of Science

585-475-5665
Office Location

Christopher Kanan

Associate Professor

Chester F. Carlson Center for Imaging Science
College of Science

Education

BS, Oklahoma State University; MS, University of Southern California; Ph.D., University of California

Bio

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.

 

 

585-475-5665

Areas of Expertise

Currently Teaching

IMGS-799
1 - 4 Credits
This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in their graduate studies.
IMGS-890
1 - 6 Credits
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
IMGS-891
0 Credits
Continuation of Thesis
IMGS-684
3 Credits
This course will review neural networks and related theory in machine learning that is needed to understand how deep learning algorithms work. The course will include the latest algorithms that use deep learning to solve problems in computer vision and machine perception, and students will read recent papers on these systems. Students will implement and evaluate one or more of these systems and apply them to problems that match their interests. Students are expected to have taken multiple computer programming courses and to be comfortable with linear algebra and calculus. No prior background in machine learning or pattern recognition is required.
IMGS-699
0 Credits
This course is a cooperative education experience for graduate imaging science students.

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