PHD - Information and Computer Science
University of California Irvine - 2009
M.S. - Computer Science
University of Maine, Orono - 2002
B.S. - Computer Engineering
University of Mumbai - 1999
Honors and Awards
Co-winner of the UAI Approximate Inference Challenge, 2010 (won four out of six categories participated).
Thesis nominated by University of California, Irvine for ACM Doctoral Dissertation award, 2009.
Co-winner of Probabilistic Inference Evaluation, 2008.
Joseph Fischer Memorial Fellowship Award for Outstanding Academic Achievement in Computer Science at University of California, Irvine, 2004.
Graduate Fellowship, University of California, Irvine, 2002-2003.
Machine learning, Artificial Intelligence, Data mining and Big data.
Vibhav Gogate and Rina Dechter, Importance Sampling based Estimation over AND/OR
Search Spaces for Graphical Models, Articial Intelligence Journal, 2011. 2011 - Publication
Vibhav Gogate and Rina Dechter, Sampling-based Lower Bounds for Counting Queries,
Submitted to Intelligenza Articiale, 2011. 2011 - Publication
Robert Mateescu, Kalev Kask, Vibhav Gogate and Rina Dechter, Iterative Join Graph
Propagation algorithms, Journal of Articial Intelligence Research, Volume 37, Pages:
279-328, 2010 2010 - Publication
Vibhav Gogate is an Assistant Professor in the Computer Science Department at the University of Texas at Dallas. He got his Ph.D. at University of California, Irvine in 2009 and then did a two-year post-doc at University of Washington. His research interests are in artificial intelligence, machine learning and data mining. His ongoing focus is on probabilistic graphical models, their first-order logic based extensions such as Markov logic and probabilistic programming. He has published over 25 papers in top-tier conferences and journals such as AAAI, UAI, NIPS, AISTATS, AIJ and JAIR. He is the co-winner of the last two probabilistic inference competitions - the 2010 UAI approximate inference challenge and the 2012 PASCAL probabilistic inference competition.
UT Dallas assistant professor of computer science Dr. Vibhav Gogate
has earned a National Science Foundation Faculty Early Career Development (CAREER) Award
for his work to improve a type of computer algorithm used in artificial intelligence and machine learning.
Gogate’s award, which will run for five years, will support his work to develop new scalable approaches for learning and inference in Markov logic networks
“MLNs are used in many artificial intelligence sub-fields, such as computer vision, robotics, natural language processing and computational biology,” Gogate said. “Algorithms developed in this proposal can be immediately leveraged in these domains. We intend to use MLNs to solve much larger and harder reasoning problems than is possible today.”