Edward  McFowland III

Edward McFowland III

Assistant Professor
Information & Decision Sciences

Education:

  • Ph.D. 2015
    Information Systems and Management Carnegie Mellon University
  • M.S. 2014
    Machine Learning, Carnegie Mellon University
  • M.Phil. 2013
    Public Policy, Carnegie Mellon University
  • M.S. 2009
    Information Systems and Management, Carnegie Mellon University
  • B.S. 2009
    Information Systems, Carnegie Mellon University

Expertise:

  • Data Mining & Machine Learning
  • Data Science & Computational Social Science
  • Heterogeneous Treatment Effects & Causal Inference
  • Anomalous Pattern Detection
  • Hypothesis Generation & Testing
  • Scan Statistics
  • Big Data Analytics

Biography

I am an Assistant Professor of Information and Decision Sciences in the Carlson School of Management, at the University of Minnesota. My 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, my 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, my broad research goal is bridging the gap between machine learning and the social sciences (e.g., economics, public policy, and management) both through the application of machine learning methods to social science problems and through the integration of machine learning and econometric methodologies.  My research on these topics has been published in leading Machine Learning and Statistics journals.

Prior to joining the University of Minnesota, I received a Bachelors degree, three Masters degrees, and a Doctorate degree from Carnegie Mellon University. During graduate school, I was the recipient of the Suresh Konda Research Paper Award, the William W. Cooper Doctoral Dissertation Award, an AT&T Labs fellowship, and an National Science Foundation graduate research fellowship.


Selected Works & Activities.

  • Conference Proceedings
    William Herlands, Edward McFowland III, Andrew G. Wilson, Daniel B. Neill. Automated Local Regression Discontinuity Design Discovery. Proc 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1512-1520, 2018.
  • Conference Proceedings
    William Herlands, Edward McFowland III, Andrew G. Wilson, Daniel B. Neill. Gaussian process subset scanning for anomalous pattern detection in non-iid data. Proc 21st International Conference on Artificial Intelligence & Statistics, PMLR 84: 425-434, 2018
  • Journal Articles
    Skyler Speakman, Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. Penalized fast subset scanning. Journal of Computational and Graphical Statistics, 25(2): 382-404, 2016. Selected for “Best of JCGS” invited session by the Editor in Chief.
  • Journal Articles
    Skyler Speakman, Edward McFowland III, and Daniel B. Neill. Scalable detection of anomalous patterns with connectivity constraints. Journal of Computational and Graphical Statistics, 24(4): 1014-1033, 2015.
  • Journal Articles
    Edward McFowland III, Skyler Speakman, and Daniel B. Neill. Fast generalized subset scan for anomalous pattern detection. Journal of Machine Learning Research, 14: 1533-1561, 2013.
  • Journal Articles
    Daniel B. Neill, Edward McFowland III, and Huanian Zheng. Fast subset scan for multivariate event detection. Statistics in Medicine 32: 2185-2208, 2013.
  • Journal Articles
    Cosma R. Shalizi and Edward McFowland III. Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations.
  • Journal Articles
    Edward McFowland III, Sriram Somanchi, Daniel B. Neill. Efficient Identification of Heterogeneous Treatment Effects in Randomized Experiments, via Anomalous Pattern Detection.
  • Journal Articles
    Edward McFowland III, Sandeep Gangarapu, Ravi Bapna, Tianshu Sun. A Prescriptive Analytics Framework for Optimal Policy Deployment using Heterogeneous Treatment Effects.
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