David S. Choi

David S. Choi

I'm an associate professor in Heinz College, which is part of Carnegie Mellon University, and contains the Schools of Public Policy and Information Systems. I also have a courtesy appointment in the Department of Statistics.

My research interests are statistics and machine learning in network settings. This includes community detection, other network models involving latent variables or unsupervised learning, and causal inference in the presence of social network effects (i.e., interference between units).

I was a graduate student in the Department of Electrical Engineering at Stanford University, where my advisor was Benjamin Van Roy. I've also worked as a technical staff member at MIT Lincoln Laboratory, as a postdoc in the School of Engineering at Harvard University, and as a visiting postdoc in the Department of Statistics at UC Berkeley.

Curriculum Vitae

Working Papers

  1. Estimating the prevalance of peer effects and other spillovers. In revision.
    Note: revisions will be extensive -- current approach is significantly different (and more powerful) than that of latest arxiv preprint.

Recent Publications

  1. New estimands for experiments with strong interference. To appear in Journal of the American Statistical Association

  2. Balancing Weights for Region-level analysis: the effect of medicaid expansion on the uninsurance rate among states that did not expand medicaid. (with Max Rubinstein and Amelia Haviland) Annals of Applied Statistics, 17(2), 2023

  3. Constructing local cell-specific networks from single-cell data. (with Xuran Wang and Kathryn Roeder) Proceedings of the National Academy of Sciences, 118(51), 2021.

  4. APOE and TREM2 regulate amyloid responsive microglia in Alzheimer's disease. (with A. Nguyen, K. Wang, G. Hu, X. Wang, Z. Miao, J.A. Azevedo, E. Suh, V.M. Van Deerlin, K. Roeder, M. Li, and E.B. Lee) Acta Neuropathologica, 140, 477-493, 2020.

  5. Clustering ensembles of social networks. (with Tracy Sweet and Abby Flynt) Network Science , 7(2), 2019.

  6. Global spectral clustering in dynamic networks. (with Fuchen Liu, Liu Xie, and Kathryn Roeder) Proceedings of the National Academy of Sciences, 115(5), 2018. [code]

  7. Co-clustering of non-smooth graphons. Annals of Statistics , 45(4), 1488-1515, 2017

  8. Estimation of monotone treatment effects in network experiments. Journal of the American Statistical Association , 112 (519), 1147-1155, 2017. Errata: there is a sign error in Eq. (28)

  9. Co-clustering separately exchangeable network data. (with Patrick Wolfe) Annals of Statistics , 42(1), 29-63, 2014.

  10. Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels. (with Peter Bickel, Xiangyu Chang, and Hai Zhang) Annals of Statistics, 41(4), 1922-1943, 2013.

  11. Stochastic blockmodels with growing number of classes. (with Patrick Wolfe and Edoardo Airoldi) Biometrika, 99(2), 273-284, 2012.

  12. Confidence sets for network structure. (with Edoardo Airoldi and Patrick Wolfe) Statistical analysis and data mining, 4(5), 461-469, 2011.
    • Conference version in Neural Information Processing Systems 24, 2011. [pdf]

Technical Reports

  1. Using Exposure Mappings as Side Information in Experiments with Interference.

  2. A Semidefinite Program for Structured Blockmodels.

davidch at andrew.cmu.edu
Hamburg Hall 2118B, 5000 Forbes Avenue, Pittsburgh, PA 15213