Summary

Dr. Dasgupta is a data scientist and statistician who works with collaborators to help develop ways (models, algorithms, predictive modeling, visualizations) to extract intelligence from data and reveal the story that the data is expressing. He worked for several years in genetic epidemiology, computational biology and analyses of microarrays and other genomic platforms for cancer research. He is experienced in machine learning algorithms and statistical analyses of biological data, biomarkers, sensor data, outcomes research, business analytics, and Bayesian modeling. He co-founded the Statistical Programming DC meetup and continues to help build the local data community. He is also a 4th degree black belt in aikido.

Research Statement

Dr. Dasgupta focuses on developing novel statistical and data analytic methods to solve scientific problems. He has worked for several years at the interface of statistics and machine learning to understand and enhance the interpretability of machine learning methods. Over the years, he has developed an appreciation for the importance of data collection methods, study design and the need to better analyze observational studies. Recent interests have included propensity scoring for observational studies and meta-analyses. He is increasingly interested in the Bayesian perspective to statistical learning.

Scientific Publications

Risk of End-Stage Renal Disease in Patients With Lupus Nephritis, 1971-2015: A Systematic Review and Bayesian Meta-Analysis.

Tektonidou MG, Dasgupta A, Ward MM
Arthritis & rheumatology (Hoboken, N.J.).
2016 Jun;
68(6).
doi: 10.1002/art.39594
PMID: 26815601

Risk estimation using probability machines.

Dasgupta A, Szymczak S, Moore JH, Bailey-Wilson JE, Malley JD
BioData mining.
2014 Mar 1;
7(1).
doi: 10.1186/1756-0381-7-2
PMID: 24581306

The limits of p-values for biological data mining.

Malley JD, Dasgupta A, Moore JH
BioData mining.
2013 May 11;
6(1).
doi: 10.1186/1756-0381-6-10
PMID: 23663551

Probability machines: consistent probability estimation using nonparametric learning machines.

Malley JD, Kruppa J, Dasgupta A, Malley KG, Ziegler A
Methods of information in medicine.
2012;
51(1).
doi: 10.3414/ME00-01-0052
PMID: 21915433

Brief review of regression-based and machine learning methods in genetic epidemiology: the Genetic Analysis Workshop 17 experience.

Dasgupta A, Sun YV, König IR, Bailey-Wilson JE, Malley JD
Genetic epidemiology.
2011;
35 Suppl 1().
doi: 10.1002/gepi.20642
PMID: 22128059

Education

Dr. Dasgupta received his B.Stat. (Hons.) in Statistics (1993) from the Indian Statistical Institute, Kolkata, India.

An M.S. in Statistics (1994) and Ph.D. in Biostatistics (2001) from the University of Washington, Seattle.

He then completed a postdoctoral fellowship at the Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute.

Experience

2006-2009    Assistant Professor, Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University

Last Updated: June 2020