David Benkeser
Assistant Professor
Faculty, Biostatistics and Bioinformatics
My methodological research focuses on the theory and applications of machine learning in causal inference. Specific areas of interest include competing risks, complex longitudinal data, and theory of robust nonparametric statistical inference. My collaborative research includes work on preventive vaccines and HIV prevention.
Contact Information
1518 Clifton Road, N.E.
Atlanta , GA 30322
1518-002-3AA
Phone: 404-712-9975
Email: benkeser@emory.edu
Areas of Interest
- Causal Inference
- HIV/AIDS Prevention
- Machine Learning
- Survival Analysis
- Vaccines
Education
- B.S. 2010, University of Georgia
- MPH 2010, University of Georgia
- PhD. 2015, University of Washington
Courses Taught
- INFO 550: Data Science Toolkit
- EPI 760: Causal Inference
- EPI 760: Causal Inference
- BIOS 761: Causal Inference
- BIOS 761: Causal Inference
Publications
- Benkeser D, Cai W, van der Laan MJ, 2020, A nonparametric super-efficient estimator of the average treatment effect, Statistical Science, ,
- Millett GA, Jones AT, Benkeser D, Baral S, Mercer L, Beyrer C, Honermann B, Lankiewicz E, Mena L, Crowley J, Sherwood J, Sullivan P, 2020, Assessing Differential Impacts of COVID-19 on Black Communities, Annals of Epidemiology, ,
- Mehrotra DV, Janes HE, Fleming TR, Annunziato PW, Neuzil KM, Carpp LN, Benkeser D, Brown ER, Cho I, Donnell D, Fay MP, Fong Y, Han S, Hirsch I, Huang Y, Huang Y, Hyrien O, Juraska M, Luedtke A, Nason M, Vandebosch A, Zhou H, Cohen M, Corey L, Hartzel J, Follmann D, Gilbert PB, 2020, Clinical Endpoints for Evaluating Efficacy in COVID-19 Vaccine Trials, Annals of Internal Medicine, ,
- Benkeser D, Horvath K, Reback CJ, Rusow J, Hudgens M, 2020, Design and analysis considerations for a sequentially randomized HIV prevention trial, Statistics in Biosciences, ,
- Hejazi N, van der Laan MJ, Gilbert P, Janes H, Benkeser D, 2020, Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials, Biometrics, ,
- Benkeser D†, Diaz I†, Luedtke A†, Segal J, Scharfstein D, Rosenblum M, 2020, Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes, Biometrics, ,
- Benkeser D, Carone M, Gilbert PB, 2019, Estimating and testing vaccine sieve effects using machine learning, Journal of the American Statistical Association, 114, 1038-1049
- Benkeser D, Petersen M, van der Laan MJ, 2019, Improved small-sample estimation of non- linear cross-validated prediction metrics, Journal of the American Statistical Association, ,
- Magaret CA†, Benkeser D†, Williamson BD†, Borate BR, Carpp L, Georgiev I, Setliff I, Dingens AS, Simon N, Carone M, Simpkins C, Montefiori D, Alter G, Juraska M, Edelfsen PT, Karuna S, Mgodi NM, Edugupanti S, Gilbert PB , 2019, Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features, PLoS Computational Biology, 15,
- Ju C†, Benkeser D†, van der Laan MJ, 2019, Robust inference on the average treatment effect using the outcome highly adaptive lasso, Biometrics, ,
- Benkeser D, Carone M, van der Laan MJ, Gilbert PB, 2017, Nonparametric doubly-robust inference on the average treatment effect, Biometrika, 104, 863-880
- Neafsey D, Juraska M, Bedford T†, Benkeser D†, Valim C†, Griggs A, Lievens M, et al, 2015, Genetic diversity and protective efficacy of the RTS,S/AS01 Malaria Vaccine, New England Journal of Medicine, 373, 2025-2037