Ph.D., University of Texas, Dallas, 2015
Machine learning, artificial intelligence
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.