BS, Oklahoma State University; MS, University of Southern California; Ph.D., University of California
I’m an Assistant 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). I’m also a Senior AI Scientist at PAIGE, where we are developing deep learning algorithms to better detect and treat cancer.
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
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.
April 18, 2020
Student to Student: Artificial intelligence/machine learning
During an internship, Tyler Hayes used computer vision and machine learning techniques to estimate the quality of images taken from airborne image sensors. It sparked her interest to learn more about machine learning, so she applied to the Imaging Science Ph.D. program at RIT.
October 22, 2019
RIT researchers win first place in international eye-tracking challenge by Facebook Research
The team, led by three Ph.D. students from the Chester F. Carlson Center for Imaging Science, won first place in the OpenEDS Challenge focused on semantic segmentation.