Korea Advanced Institute of Science and Technology
Computational Social Science with Latents
Computational social science has grown to answer diverse challenges with the support from data science, machine learning, and generative models. These challenges seem to have a specific problem to solve, but the true solution only will be achieved when we understand the latent profiles and dynamics. This talk with provide a number of computational methodologies and case studies on such understanding. For example, we understand the population latent health-case profile with a probabilistic modeling; we look into why our politicians vote on a certain bill with a deep generative model; and we regenerate a housing market agent-based model to automatically calibrate the simulation to match the real world. Along with these studies, we observe how the generative models from the probabilitistic modeling, the deep generative neural networks, and the agent-based model can be fused to investigate the challenges that we see in our society.
Il-Chul Moon received his Ph.D. degree from the School of Computer Science, Carnegie Mellon University in 2008. He is currently an Associate Professor with the Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea. His research interests include the overlapping area of computer science, management, sociology, and operations research, and also military command and control analysis, counterterrorism analysis, intelligence analysis, and disaster management.