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McKelvey School of Engineering

Human-Allied Artificial Intelligence

Friday, October 29, 2021 | 11:00 AM - 12:00 PM

Stephen F. & Camilla T. Brauer Hall, Brauer 12
6548 Forest Park Pkwy, St. Louis, MO 63112, USA

Dr. Sriraam Natarajan
Professor of Computer Science, The University of Texas at Dallas

Historically, Artificial Intelligence has taken a symbolic route for representing and reasoning about objects at a higher-level or a statistical route for learning complex models from large data. To achieve true AI, it is necessary to make these different paths meet and enable seamless human interaction. First, I briefly will introduce learning from rich, structured, complex and noisy data. Next, I will present the recent progress that allows for more reasonable human interaction where the human input is taken as “advice” and the learning algorithm combines this advice with data. The advice can be in the form of qualitative influences, preferences over labels/actions, privileged information obtained during training or simple precision-recall trade-off. Finally, I will outline our recent work on "closing-the-loop" where information is solicited from humans as needed that allows for seamless interactions with the human expert. While I will discuss these methods primarily in the context of probabilistic and relational learning, I will also present our recent results on reinforcement learning and demonstrate how human input can be effectively used to create appropriate abstractions to guide RL.

Event Type



McKelvey School of Engineering


Science & Technology

Computer Science & Engineering


Event Contact


Speaker Information

Sriraam Natarajan is a Professor and the Director for Center for ML at the Department of Computer Science at University of Texas Dallas and a distinguished Faculty Fellow of Robert Bosch Center for Data Science and AI at IIT Madras. He was previously an Associate Professor and earlier an Assistant Professor at Indiana University, Wake Forest School of Medicine, a post-doctoral research associate at University of Wisconsin-Madison and had graduated with his PhD 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 is a AAAI senior member and has received the Young Investigator award from US Army Research Office, Amazon Faculty Research Award, Intel Faculty Award, XEROX Faculty Award, Verisk Faculty Award and the IU trustees Teaching Award from Indiana University and multiple best paper awards.

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