Dr. Edward McFowland is a distinguished computer scientist specializing in the development of computationally efficient algorithms for large-scale machine learning applications. He currently serves as an Assistant Professor in the Technology and Operations Management Unit at Harvard Business School, where he teaches in the required curriculum. Prior to joining Harvard, he was an Assistant Professor of Information and Decision Sciences at the University of Minnesota Carlson School of Management. Dr. McFowland received his Ph.D. in Information Systems and Management from Carnegie Mellon University, where he was honored with the prestigious William W. Cooper Doctoral Dissertation Award. His academic journey includes earning three Master's degrees from Carnegie Mellon in Machine Learning, Public Policy, and Information Systems, reflecting his deep commitment to interdisciplinary scholarship at the intersection of technology and social sciences.
Dr. McFowland's research has pioneered innovative approaches to anomalous pattern detection and discovery, demonstrating how many complex real-world problems can be reduced to these fundamental computational tasks. His seminal work on 'Penalized Fast Subset Scanning' earned the Best Paper Award in the Journal of Computational and Graphical Statistics and has been widely recognized for its computational efficiency in large-scale data analysis. His algorithms have been applied to diverse social science problems including public policy analysis and economic forecasting, receiving support from major organizations such as the National Science Foundation, Adobe, Facebook, and AT&T Research Labs. With over 1,700 citations on Google Scholar, his research has established important bridges between machine learning methodologies and traditional social science inquiry, creating new pathways for computational social science.
As a data and computational social scientist, Dr. McFowland is dedicated to integrating machine learning with econometric methodologies to address pressing societal challenges. His current research continues to explore the application of advanced statistical techniques to management and policy questions, with recent work examining the impact of artificial intelligence on knowledge worker productivity. Dr. McFowland has built a robust research program that combines theoretical rigor with practical applications, mentoring students and collaborating across disciplines to expand the reach of computational methods. His vision for the future includes further developing the synergy between machine learning and social sciences to create more effective, data-driven approaches to organizational and societal decision-making.
