Christopher Kanan's research lies in deep learning, with an emphasis on lifelong (continual) machine learning, bias-robust artificial intelligence, medical computer vision, and language-guided scene understanding. He has worked on online continual learning, visual question answering, computational pathology, semantic segmentation, object recognition, object detection, active vision, object tracking, and more. Beyond machine learning, he also has a strong background in eye tracking, primate vision, and theoretical neuroscience.
- Artificial Intelligence
- Deep Learning
- Computer Vision
- Cognitive Science
- Applied Machine Learning (e.g. Medical Computer Vision)