Date: Thursday, April 5, 2018

Time: 03:00pm – 04:15pm

Location:  Klaus Advanced Computing Building, Room 2443

Abstract: This talk will provide an introduction to concepts and methods for learning causal relationships in the form of causal networks from biomedical and clinical data, including solely observational data. Examples will be given of applying these methods to biomedical data. The talk will also provide pointers to software for learning causal networks from data, including data containing thousands of variables.

Bio: Gregory Cooper, M.D., Ph.D. is Professor of Biomedical Informatics and of Intelligent Systems at the University of Pittsburgh. His research focuses on the application of probabilistic modeling, machine learning, Bayesian statistics, and artificial intelligence to address biomedical informatics problems. Current research projects include causal modeling and discovery of biomedical knowledge from large datasets, learning electronic medical record systems, machine-learning-based clinical alerting, computer-aided medical diagnosis and prediction, and methods for detecting and characterizing infectious disease outbreaks.