I am interested in building algorithms that learn from experience, to be able to perform their tasks better. Most of my current work has a strong application pull -- i.e., is motivated by some specific tasks: for example, to better understand brain tumors (from MRI scans), various cancers based on microarray data, Single Nucleotide Polymorphisms, and/or metabolic profiles. Some other projects are more technology push -- where the goal is more exploring some foundation or mathematical framework, rather than solving some application: such as learning Bayesian belief nets, active learning and addressing high-dimensional data ("large p, small n").
Probabilistic graphical models (PGMs; including Bayesian Belief Nets, Markov Random Fields, etc.) now contribute significantly to many areas, including expert systems, computer perception (vision and speech), natural language interpretation, automated decision making, and robotics. This course provides an introduction to this field, describing semantics, inference and learning, as well as practical applications of these systems. Programming assignments will include hands-on experiments with various reasoning systems. Prerequisites: one of CMPUT 340 or 418; one of STAT 141, 151, 235 or 265 or SCI 151; or consent of the instructor.Fall Term 2020