Graphical models are used to organize and structure probability distributions over large systems, and enable efficient approximate or exact reasoning. My group balances developing theoretical and algorithmic advances with applications to the real-world systems of our collaborators.
Algorithms. One of our main focuses is on finding maxima or computing probabilities using variational methods, including the family of belief propagation (BP) message-passing algorithms. Our contributions include analyzing the convergence and accuracy properties of BP, developing new BP-like bounds, extending BP techniques to continuous valued systems, improving the efficiency of "adaptive" or incremental inference, and extending variational algorithms to ``mixed'' inference tasks such as marginal MAP and decision making problems, including influence diagrams (or decision networks) and distributed team decision problems.
Applications. We have applied our algorithms to a wide variety of problems, including tracking and understanding data from sensor networks, efficient representations for large text corpora, computer vision and image processing, and gene expression data in biology.
I received an NSF CAREER award, "Estimation and Decisions in Graphical Models" (IIS-1254071)
I was awarded the 2013 Chancellor's Award for Excellence in Fostering Undergraduate Research, and my student Michael Vorobyov the Award for Excellence in Undergraduate Research for his honors thesis work.
Qiang Liu co-organized the workshop, "Machine Learning Meets Crowdsourcing" at ICML 2013 in Atlanta.
Our collaboration with Rina Dechter's group won the MAP task components of the 2011 Probabilistic Inference Challenge, and our group's entry was runner-up in the marginalization tasks.
Qiang Liu has received a 2011 Microsoft Research Fellowship award.