Erich Kummerfeld, PhD

Causal Analytics
Data Visualization
Health Data Science
Predictive Analytics
Erich Kummerfeld

8-101 Phillips-Wangensteen Building
516 Delaware St. SE
Minneapolis, MN 55455
United States

Dr. Kummerfeld's primary research interest is in statistical and machine learning methods for discovering causal relationships, with a special focus on discovering causal latent variable models. His work includes (1) developing novel algorithms for discovering causal relationships and latent variables, (2) proving theorems about the properties of causal discovery and latent variable discovery algorithms, (3) performing benchmark simulation studies to evaluate features of the algorithms that are difficult or impossible to evaluate by other means, and (4) applying these novel algorithms to health data in order to inform the development of new treatments.


Research Assistant Professor

Ph.D., Logic, Computation and Methodology, Carnegie Mellon University

M.S., Logic, Computation and Methodology, Carnegie Mellon University

B.A., Applied Mathematics, Hampshire College