The Center for Machine Learning is housed in the Department of Computer Science within the Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas. Our mission is to foster excellent research and development of machine learning algorithms motivated by challenges from real work domains ranging from precision health to natural language understanding, from biology to social network analysis and from vision to mobile health.

    Our core team consists of researchers whose expertise lie in relational models, probabilistic modeling, combinatorial optimization, active learning, logic-based learning, human-in-the-loop learning, reinforcement learning, supervised learning and data mining. The research emphasis of the center implies it is synergistic with the Human Language Technology Research Institute and the Cyber Security Research and Education Institute. Furthermore, the center will promote outreach and educational activities in machine learning and AI, such as a summer school for students from other universities who are interested in this area.

    The Center for Machine Learning will promote education in machine learning at the undergraduate and graduate levels, through core faculty developing and teaching courses in three primary areas:

    » Artificial Intelligence
    » Machine Learning
    » Advanced Machine Learning topics including Probabilistic Graphical Models and Statistical Relational AI

    The Center for Machine Learning will lead many professional development and outreach activities that include, but are not limited to, a summer school, a yearly workshop, and community outreach events. The center will aim to obtain an NSF REU funding site award for machine learning and AI. The center will coordinate with the department outreach to foster relationships with local schools and colleges to advance the machine learning education.

    For more information on outreach activities, click here.

    The Center for Machine Learning will benefit from the department’s support and a potential return on overhead from grants. For more information on the Center for Machine Learning’s funding, click here.




    Dr. Sriraam Natarajan

    Dr. Sriraam Natarajan is a Professor at the Department of Computer Science at the University of Texas Dallas and a distinguished faculty fellow of Robert Bosch Center for Data Science and AI at IIT Madras. He is a senior member of AAAI and director of the Center for Machine Learning. He was previously an Associate Professor and an Assistant Professor at Indiana University, Wake Forest School of Medicine, a post-doctoral research associate at the University of Wisconsin-Madison and had graduated with his Ph.D. from Oregon State University. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Biomedical Applications. He has received the Young Investigator award from US Army Research Office, Amazon Faculty Research Award, XEROX Faculty Award and the IU trustees Teaching Award from Indiana University. He is an editorial board member of MLJ, JAIR and DAMI journals and is the electronics publishing editor of JAIR.


    Dr. Rishabh Iyer

    Dr. Rishabh Iyer is currently an assistant professor at the University of Texas at Dallas. Prior to this, he was a Research Scientist at Microsoft., and during his time at Microsoft, several of his algorithms and innovations have been shipped in Microsoft products including Microsoft Office and Bing ads. He finished his Ph.D. from the University of Washington, Seattle. He has received a number of awards including best paper awards at ICML and NIPS in 2013, an honorable mention at CODS-COMAD 2021, a Microsoft Ph.D. Fellowship Award, a Facebook Ph.D. Fellowship Award, and the Yang Outstanding Doctoral Student Award from the University of Washington. He is interested in several aspects of machine learning including discrete and convex optimization, submodular optimization, data efficient machine learning, robust learning, feature selection and data subset selection, data programming, and data summarization.


    Co - Director

    Dr. Vibhav Gogate

    Dr. Vibhav Gogate is an Associate Professor in the Computer Science Department at the University of Texas at Dallas and co-director of the Center for Machine Learning. He got his Ph.D. at the University of California, Irvine in 2009 and then did a two-year post-doc at the 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 is a recipient of the National Science Foundation (NSF) CAREER award and the co-winner of 2010 and 2012 UAI inference competitions.

    Nicholas Ruozzi

    Dr. Nicholas Ruozzi

    Dr. Nicholas Ruozzi is an Associate Professor in the Department of Computer Science at the University of Texas at Dallas.  He was previously a postdoctoral researcher and Adjunct Professor at Columbia University and a postdoctoral researcher at Ecole Polytechnique Federale de Lausanne (EPFL) in Lausanne, Switzerland.  He obtained his Ph.D. at Yale University.  His research interests include statistical machine learning, probabilistic graphical models, approximate inference and learning, and optimization.  His work has been funded by the National Science Foundation (NSF) and the Defense Advanced Research Projects Agency (DARPA).


    Dr. Feng Chen

    Dr. Feng Chen is an Associate Professor at the Department of Computer Science at the University of Texas Dallas . He was previously an Assistant Professor at the University at Albany – SUNY and a post-doctoral researcher at Carnegie Mellon University.  He obtained his Ph.D. at Virginia Tech. His research interests include large-scale data mining, network mining, and machine learning, with a focus on event and pattern detection in massive, complex networks. His current research includes applications in disease outbreak detection in disease surveillance networks, societal event detection/forecasting in social networks, cyber-attack detection in computer networks, and subnetwork marker detection in biological networks, among others. His research has been funded by NSF, NIH, ARO, IARPA, and the U.S. Department of Transportation. He is a recipient of the National Science Foundation (NSF) CAREER award.


    Dr. Yu Xiang

    Yu Xiang is an Assistant Professor in the Department of Computer Science at the University of Texas at Dallas. Before joining UT Dallas, he was a Senior Research Scientist at NVIDIA from 2018 to 2021. He received his Ph.D. in Electrical Engineering from the University of Michigan at Ann Arbor in 2016. He was a Postdoctoral Researcher at Stanford University and at the University of Washington from 2016 to 2017, and was a visiting student researcher in the Stanford Artificial Intelligence Laboratory from 2013 to 2016. He received an M.S. degree in Computer Science from Fudan University in 2010 and a B.S. degree in Computer Science from Fudan University in 2007. Yu’s research focuses on robotics and computer vision. He is interested in studying how robots can acquire various skills in perception, planning and control through learning, and integrate these skills in a systematic way to conduct tasks in human environments autonomously.