Engineering Experts

Deepak Venugopal

The University of Memphis
United States of America

Deepak Venugopal


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 and learning in probabilistic graphical models and their first-order extensions such as Markov Logic Networks. His work has resulted in key techniques that lift approximate inference methods to first-order models and has been published in several top-tier conferences in Machine Learning and Artificial Intelligence, such as NIPS, AAAI, UAI, and EMNLP

Research Interest

Machine learning, artificial intelligence


  • Deepak Venugopal and Vasile Rus, "Joint Inference for Mode Identification in Tutorial Dialogues", COLING 2016

  • Mohammad Maminur Islam, Mohammad Khan Al Farabi and Deepak Venugopal, "Adaptive Blocked Gibbs Sampling for Inference in Probabilistic Graphical Models", IJCNN, 2017

  • Somdeb Sarkhel, Deepak Venugopal, Nicholas Ruozzi and Vibhav Gogate, "Efficient Inference for Untied MLNs", IJCAI 2017

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