UBC Computer Science Associate Professor Leonid Sigal is the new Canada Research Chair in Computer Vision and Machine Learning. He becomes one of 5 Canada Research Chairs in Computer Science at UBC.
As Canada Research Chair in Computer Vision and Machine Learning, Dr. Leonid Sigal is focusing on building computer algorithms that can automatically and intelligently recognize, catalogue and understand image and video content. One of the most important aspects of building such algorithms is “learning,” where computers learn visual patterns and build complex visual models from copious data.
Sigal and his research team are working on a branch of learning algorithms known as deep learning or deep neural networks: biologically inspired algorithms and architectures that systematically learn to extract hierarchical representations and abstractions from data to help with visual cognition tasks. Such algorithms are at the core of most recent artificial intelligence advances, from speech recognition to face and image tagging.
Sigal and his team are focusing on two cornerstone research challenges: the ability to efficiently learn from large-scale, heterogeneous (e.g., visual, lingual) data that may not be carefully curated or labeled; and the ability to learn models that are semantic and interpretable.