Edward McFowland

Edward McFowland III is an Assistant Professor in the Technology and Operations Management Unit at Harvard Business School. He teaches the first-year TOM course in the required curriculum.

Professor McFowland’s research interests – which lie at the intersection of Machine Learning, Information Systems, and Management—include the development of computationally efficient algorithms for large-scale statistical machine learning and “big data” analytics. As a data and computational social scientist, Professor McFowland aims to bridge the gap between machine learning and the social sciences (e.g., economics, public policy, and management). His work has been published in leading management, machine learning, and statistics journals, and has been supported by Adobe, Facebook, PNC Bank, AT&T Research Labs, and the National Science Foundation.

Professor McFowland earned his Ph.D. in Information Systems and Management from Carnegie Mellon University. He also holds Masters degrees in Machine Learning, Public Policy, and in Information Systems from Carnegie Mellon University. Prior to joining HBS, Professor McFowland taught at the University of Minnesota Carlson School of Management.

AREAS OF INTEREST

• analytics
• decision-making
• information technology
• machine learning
• networks

AWARDS & HONORS

• Recipient of a 2021 Fairness in AI Grant from the National Science Foundation and Amazon.
• A recipient of the 2021 Mary and Jim Lawrence Fellowship for contributions in enhancing the intellectual environment of the Carlson School of Management at University of Minnesota.
• Won the Best Reviewer Award at the 2019 Conference on Information Systems and Technology.
• Winner of a 2018 Facebook Computational Social Science Methodology Research Award.
• Runner Up for the 2018 Best Paper Award at the INFORMS Workshop on Data Science for "Using Data-Mined Variables in Causal Inference Tasks: A Random Forest Approach to the Measurement Error Problem" with Mochen Yang, Gordon Burtch, and Gediminas Adomavicius.
• Recipient of a 2018 Adobe Faculty Research Award, a grant to fund data science research.
• Recipient of a 2017 Adobe Faculty Research Award, a grant to fund data science research.
• Winner of the 2016 Journal of Computational and Graphical Statistics Best Paper Award for "Penalized Fast Subset Scanning" (2016) with Skyler Speakman, Sriram Somanchi, and Daniel B. Neill.
• Winner of the 2015 William W. Cooper Doctoral Dissertation Award from Carnegie Mellon University.