David S. Choi

David S. Choi is an Associate Professor at Heinz College, Carnegie Mellon University, which houses the Schools of Public Policy and Information Systems. He maintains a courtesy appointment in the Department of Statistics at the same institution.

His academic credentials include a Ph.D. in Electrical Engineering from Stanford University under Benjamin Van Roy. Professional experience encompasses roles as a technical staff member at MIT Lincoln Laboratory, postdoctoral research at Harvard University's School of Engineering, and a visiting postdoc at UC Berkeley's Department of Statistics.

Choi's research centers on statistical and machine learning methodologies for network-structured data. His work pioneers community detection techniques, latent variable network modeling, unsupervised learning frameworks, and causal inference methods that account for interference in social networks. These innovations address fundamental challenges in analyzing interconnected systems across public policy, healthcare, and social sciences.

Publication analysis reveals consistent contributions to causal inference in networked experiments, particularly in characterizing interference and spillover effects. His scholarship spans network clustering algorithms, biological network modeling, and policy evaluation methodologies, with impactful work published in Journal of the American Statistical Association, PNAS, and Annals of Statistics.

No scientific awards were documented in the source material.

The available information does not reference doctoral students supervised or research grants managed by Professor Choi.

Specific research laboratories or collaborative teams were not mentioned in the provided documentation.