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Gogate, Vibhav

Dr. Vibhav Gogate

Associate Professor

Co-Director of the Center for Machine Learning



  • Ph.D., Computer Science, University of California, Irvine, 2009
  • M.S. in Computer Science, University of Maine, Orono, 2002
  • B.S. in Computer Engineering, University of Mumbai, Maharashtra, India, 1999

Research Interests:

  • Machine learning
  • Artificial Intelligence
  • Data mining
  • Big data

Major Honors and Awards:

  • NSF CAREER Award, 2017
  • Outstanding Teacher Award, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, 2016
  • Winner of the PASCAL/UAI Probabilistic Inference Challenge, 2012 (won six out of six categories participated. Total categories: nine.)
  • Winner of the UAI Approximate Inference Challenge, 2010 (won four out of six categories participated. Total categories: nine.)
  • Thesis nominated by University of California, Irvine for the ACM Doctoral Dissertation award, 2009
  • Joseph Fischer Memorial Fellowship Award for Outstanding Academic Achievement in Com- puter Science at University of California, Irvine, 2004

Representative Publications:

  • Rahman and V. Gogate, “Merging strategies for sum-product networks: From trees to graphs,” In Proceedings of the Thirty-Second Conference Conference on Uncertainty in Artificial Intelligence, pages 617–626, 2016.
  • de Salvo Braz, C. O’Reilly, V. Gogate, and R. Dechter, “Probabilistic infer- ence modulo theories,” In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pages 3591–3599, 2016.
  • Rahman and V. Gogate, “Learning ensembles of cutset networks,” In AAAI conference on Artificial Intelligence, pages 3301–3307, 2016.
  • Sarkhel, D. Venugopal, T. A. Pham, P. Singla, and V. Gogate, “Scalable training of markov logic networks using approximate counting,” In AAAI conference on Artificial Intelligence, pages 1067–1073, 2016.
  • Chou, S. Sarkhel, N. Ruozzi, and V. Gogate, “On parameter tying by quantization,” In AAAI conference on Artificial Intelligence, pages 3241–3247, 2016.
  • Deepak Venugopal, Somdeb Sarkhel and Vibhav Gogate, “Just Count the Satisfied Groundings: Scalable Local-Search and Sampling Based Inference in MLNs,” In AAAI 2015.
  • Somdeb Sarkhel, Deepak Venugopal, Parag Singla and Vibhav Gogate, “An Integer Polynomial Programming Based Framework for Lifted MAP Inference,” In NIPS 2014.
  • Deepak Venugopal, Chen Chen, Vibhav Gogate and Vincent Ng, “Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features,” In Empirical Methods in Natural Language Processing Conference (EMNLP), 2014.
  • Happy Mittal, Prasoon Goyal, Vibhav Gogate and Parag Singla, “New Rules for Domain Independent Lifted MAP Inference,” In NIPS 2014.
  • Deepak Venugopal and Vibhav Gogate, “Scaling-up Importance Sampling for Markov Logic Networks,” In NIPS 2014.

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