Dr. Abhirup (Abhi) Datta is an Associate Professor in the Department of Biostatistics
at Johns Hopkins Bloomberg School of Public Health.
Dr. Datta’s research focuses on developing statistical methods for geospatial data with applications to air pollution, forestry, ecology, infectious disease modeling, and on Bayesian hierarchical models for multi-source data with applications to global health. Dr. Datta has published in esteemed statistical or scientific journals like Journal of the American Statistical Association (JASA), Annals of Statistics (AoS), Annals of Applied Statistics (AoAS), Biostatistics, Biometrics, and the Proceedings of the National Academy of Sciences (PNAS). In 2021, Dr. Datta was awarded the Young Statistical Scientist Award (YSSA) – a prestigious award by the International Indian Statistical Association (IISA) for early and mid career researchers.
Dr. Datta has been leading highly innovative and impactful research in spatial statistics and machine learning for analysis of large and complex environmental data. His work on Nearest Neighbor Gaussian Processes (NNGP) has become one of the most widely used methods for scalable analysis of massive geospatial data. The original NNGP paper led by Dr. Datta is one of the top-5 most cited papers published in the Journal of the American Statistical Association from 2016-2020. Dr. Datta was awarded the 2021 Early Investigator Award by the American Statistical Association (ASA) Section on Statistics and the Environment (ENVR) for contributions to spatial big data methodology and their environmental applications. As principal investigator, Dr. Datta has been awarded an R01 grant by the National Institute of Environmental Health Sciences and an NSF grant for development of spatial methods.
Currently, Dr. Datta is working on spatial methods for hyper-local air pollution data from low-cost sensors networs, and on combining machine learning methods (random forests and neural networks) with traditional spatial methods like Gaussian processes to develop hybrid and structured spatial machine learning approaches.
Dr. Datta has been developing Bayesian methods to understand disease burden from multi-source data in many low and middle income countries (LMICS). He is currently leading a high impact global health project aimed to improve cause-specific mortality estimates for children and neonates. Dr. Datta has demonstrated that the estimates of mortality in LMICS, often derived from verbal autopsies, are highly biased and has developed Bayesian transfer learning methods for bias-correction of verbal autopsy. He has been recently awarded a grant PI grant by the Bill and Melinda Gates Foundation for broader application of the method to improve cause-specific mortality estimates globally.
