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Vibhav Gogate
Probabilistic Graphical Models
Statistical Relational Learning
Probabilistic Programming
Artificial Intelligence Inference Competitions
About
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 completed a postdoctoral fellowship at the University of Washington. His research focuses on artificial intelligence, machine learning, probabilistic graphical models, statistical relational learning, and probabilistic programming. He is a recipient of the NSF Faculty Early Career Development (CAREER) Award and has co-won international AI inference competitions.
Research Performance Summary
2004-2025
Active Research Span
First Recorded Paper
Counting-based look-ahead schemes for constraint satisfaction
Year:
2004
Citations:
55
Venue:
International Conference on Principles and Practice of Constraint
Latest Recorded Paper
Learning to Condition: A Neural Heuristic for Scalable MPE Inference
Year:
2025
Citations:
0
Venue:
arXiv preprint arXiv:2509.25217
Last 10 Years Publication Activity
This timeline shows the professor's yearly publication activity.
Publication Venues and Collaboration
Journal, Conference, and Book Publication Breakdown
Top Coauthors
Gogate
Rahman
Dechter
Research Impact by Period
Papers
6
Citations
20
Avg. Citations / Paper
3.3
H-Index
2
Papers
32
Citations
492
Avg. Citations / Paper
15.4
H-Index
10
Papers
34
Citations
753
Avg. Citations / Paper
22.1
H-Index
13
Papers
37
Citations
1485
Avg. Citations / Paper
40.1
H-Index
20
Papers
12
Citations
261
Avg. Citations / Paper
21.8
H-Index
6
Papers
2
Citations
63
Avg. Citations / Paper
31.5
H-Index
2
Contact and Professional Links
Detected Research Keywords
Markov Logic Networks
Probabilistic Graphical Models
Lifted Map Inference
Tractable Probabilistic Models
Explainable Activity Recognition
Activity Recognition Videos
Most Probable Explanation
Learning Tractable Probabilistic
Probabilistic Theorem Proving
Anchoring Bias Affects