Biological systems often involve complex interactions that vary over time and vary by location. Making cause-effect inference is difficult in these systems due to the inherent unpredictability and computational complexity in simulation.  This challenge is increasingly difficult when data is difficult to obtain. In this talk Dr. Basener will present use of Bayesian inference that can provide probability distributions for input parameters that lead to outcomes of interest. He will present examples using models of lung fibrosis and ecological-economic civilization models. 

Bill Basener is a Professor at the School of Data Science with a joint appointment in the Department of Systems and Information Engineering. He has authored research publications in machine learning, signal processing, image processing, dynamical systems, game theory, ecological economics, evolutionary genetics, and other applied mathematical fields, as well as a textbook on applied topology and multiple patents. The methods and software he developed for processing images in hyperspectral imaging have become the gold-standard in the field, used for processing millions of images by dozens of organizations.