March 20, 2019
This event is open to the public and registration is not required. Part of the Operations Research Seminar Series coordinated by Merve Bodur.
Computational social science has grown to answer diverse challenges with the support from data science, machine learning, and generative models. These challenges often involve understanding latent profiles and dynamics.
This talk will provide a number of computational methodologies and case studies on such understanding. Examples include understanding the latent health-care profiles of a population with a probabilistic model, exploring why politicians vote on a certain bill with a deep generative model and regenerating a housing market with an agent based model automatically calibrated to match the real world.
Along these studies, we observe how the generative models, i.e. Probabilistic Graphical Models, Deep Generative Models, and Agent-Based Models, can be fused to investigate the challenges that we see in our society.
Il-Chul Moon received his Ph.D. in Societal Computing from the Institute of Software Research, School of Computer Science, Carnegie Mellon University in 2008. He is currently an Associate Professor at the Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology. His research interests include the overlapping area of computer science, management, sociology, and operations research, and also command and control analyses, health-care analyses, and disaster management.
The Operations Research (OR) seminar series brings together graduate students, faculty and researchers from the University of Toronto community to interact with prominent scholars in the field of OR. Seminars feature visiting scholars from around the world as well as professors and post-docs. Topics include all variants of OR theory and their applications. Questions? Contact Merve Bodur at email@example.com