AI-in-the-loop for Informed Healthcare by Dr. Sriraam Natarajan

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 in complex domains such as healthcare, it is necessary to make these different paths meet and enable seamless human interaction. First, I will introduce learning from rich, structured, complex and noisy data. One of the key attractive properties of the learned models is that they use a rich representation for modeling the domain that potentially allows for seam-less human interaction. 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. I will present these algorithms in the context of several healthcare problems — learning from electronic health records, clinical studies, and surveys — and demonstrate the value of involving experts during learning.

Short Bio Prof. Natarajan:

Dr. Sriraam Natarajan is a Professor and the Director for Center for ML at the Department of Computer Science at University of Texas, Dallas. He is a AAAI fellow, hessian.AI fellow and a RBCDSAI Distinguished Fellow at IIT Madras. 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 won several awards including the President’s teaching award for excellence in graduate teaching, the Young Investigator award from US Army Research Office, several industry awards and best paper awards. He was the program co-chair of AAAI 2024, SDM 2020 and ACM CoDS-COMAD 2020 conferences. He is an associate editor of MLJ, JAIR and DAMI journals. He is an elected member of AAAI executive council and a secretary-treasurer elect of IJCAI.

All are welcome!