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PROFESSORS IN PROBABILISTIC GRAPHICAL MODELS

Showing page 1 of 1 — 11 professors available publicly

Michael Jordan

computational statistics probabilistic graphical models Bayesian nonparametrics

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters in Mathematics from Arizona State University, and ...

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Mathias Niepert

Efficient and Controllable Generative AI Geometric and Physics-Aware Deep Learning Probabilistic Graphical Models

Mathias Niepert is a professor (W3) at the University of Stuttgart, an ELLIS fellow, and a faculty member at the International Max Planck Research School for Intelligent Systems (IMPRS-IS). He leads the Machine Learning and Simulation Lab (MLS Lab), with affiliations to the AI In...

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Mustafa Bilgic

Active Learning Interactive Machine Learning Interpretable Machine Learning

Dr. Mustafa Bilgic is a Professor of Computer Science and Chair of the Department of Computer Science at the Illinois Institute of Technology in Chicago. He also serves as Director of the Machine Learning Laboratory and leads the Master’s in Artificial Intelligence program. Dr....

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Erik Sudderth

Probabilistic Graphical Models Probabilistic Programming Nonparametric Bayesian Methods

Erik B. Sudderth is Professor of Computer Science and Statistics, and Chancellor’s Fellow, at the University of California, Irvine. He directs the UC Irvine Center for Machine Learning and Intelligent Systems, as well as the HPI Research Center in Machine Learning and Data Scie...

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Deepak Venugopal

Probabilistic Graphical Models Markov Logic Networks Lifted Approximate Inference

Dr. Deepak Venugopal joined the Department as an assistant professor in Fall 2015 after completing his PhD in computer science at the University of Texas at Dallas. Dr. Venugopal's primary research interest is in developing fast, scalable, and accurate algorithms for inference...

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Vibhav Gogate

Probabilistic Graphical Models Statistical Relational Learning Probabilistic Programming

Vibhav Gogate, Ph.D. is a Professor in the Department of Computer Science at The University of Texas at Dallas and Co-Director of the Center for Machine Learning. He earned his Ph.D. in Information and Computer Science from the University of California, Irvine in 2009 and complet...

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Arthur Choi

Bayesian Networks Probabilistic Graphical Models Explainable AI

I joined the Computer Science Department at Kennesaw State University in Fall 2021. I was formerly a researcher in the Computer Science Department at UCLA, and formerly adjunct faculty at Santa Monica College. My research interests are in areas of artificial intelligence and m...

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William Turkett

Probabilistic Reasoning Bayesian Networks Uncertainty Quantification

Dr. William Turkett is an Associate Professor in the Department of Computer Science at Wake Forest University. He received his Ph.D. in 2004 from the Department of Computer Science and Engineering at the University of South Carolina, where his research was in the area of probabil...

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Rebecca Morrison

dynamical systems probabilistic graphical models uncertainty quantification

Assistant Professor, Dept. of Computer Science, University of Colorado Boulder, 2018 – present Affiliate Faculty Member, Robotics Program Postdoc, Dept. of Aeronautics and Astronautics, Massachusetts Institute of Technology, 2016 – 2018 Intern, Sandia National Laboratories...

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Zhe Zeng

Probabilistic Graphical Models Statistical Relational Learning Deep Generative Models

Zhe Zeng is an assistant professor in the Department of Computer Science. Prior to that, she was a Faculty Fellow in the Computer Science Department at New York University, working with Andrew Gordon Wilson. She received her Ph.D. in Computer Science at the University of Californ...

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Mohammad Ali Javidian

Classical Causal Inference Quantum Entropic Causal Inference Probabilistic Graphical Models

I am an assistant professor in computer science at Appalachian State University (ASU). My research interests include classical/quantum causal inference and probabilistic graphical models for decision making under uncertainty. My research seeks to develop theoretically sound, reli...

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