Edward McFowland III

About

Edward McFowland's research interests—which lie at the intersection of Information Systems, Machine Learning, and Public Policy—include the development of computationally efficient algorithms for large-scale statistical machine learning and “big data” analytics. More specifically, McFowland’s research seeks to demonstrate that many real-world problems faced by organizations, and society more broadly, can be reduced to the tasks of anomalous pattern detection and discovery.

As a data and computational social scientist, McFowland’s broad research goal is bridging the gap between machine learning and the social sciences, both through the application of machine learning methods to social science problems and through the integration of machine learning and econometric methodologies.  His research on these topics has been published in leading Machine Learning and Statistics journals. Prior to joining the University of Minnesota, McFowland received a Bachelors degree, three Masters degrees, and a Doctorate degree from Carnegie Mellon University. During graduate school, he was the recipient of the Suresh Konda Research Paper Award, the William W. Cooper Doctoral Dissertation Award, an AT&T Labs fellowship, and a National Science Foundation graduate research fellowship.