I work in artificial intelligence and machine learning, focusing on statistical methods for learning from data and on approximate inference techniques for graphical models. Applications of my work include data mining and information fusion in sensor networks, computer vision and image processing, and computational biology.

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.

We organized a tutorial on combinatorial optimization in graphical models at IJCAI 2016 (Saturday, July 10th)

I was co-program chair (with Dominik Janzing) of the 2016 Uncertainty in Artificial Intelligence (UAI) conference, held June 25-29, 2016, in New Jersey.

Our solver ("ai") won first place in five categories of UAI's 2014 Approximate Inference Challenge. Congratulations also to Rina Dechter's group ("daoopt"), which won several other categories.

We co-organized the NIPS'13 workshop, "Crowdsourcing: Theory, Algorithms and Applications".

I received an NSF CAREER award, "Estimation and Decisions in Graphical Models" (IIS-1254071)

Current:

• Bobak Pezeshki
• Annie Raichev
• Nick Cohen
• Jay Alford-Lago

Graduated:

• Shreyas Devaraju (PhD, 2024)
• Noble Kennamer (PhD, 2023)
• Shivam Garg (PhD, 2022)
• Nick Gallo (PhD, 2020)
• Qi Lou (PhD, 2018)
• Wei Ping (PhD, 2016)
• David Keator (PhD, 2015)
• Sholeh Forouzan (PhD, 2015)
• Qiang Liu (PhD, 2014)
• Andrew Frank (PhD, 2013)
• Ozgur Sumer (PhD, 2012)
• Jonathan Hutchins (PhD, 2010)
• Sidharth Shekhar (MS, 2009)
• Priya Venkateshan (MS, 2011)

• UCI's Center for Machine Learning and our AI/ML seminar series
• UCI Machine Learning Dataset Repository

We gratefully acknowledge support for our current and recent research from the National Science Foundation, DARPA, Microsoft Research, the National Institute of Health, NIAMS, and UCI's Center for Complex Biological Systems.