Datta, Susmita


  • Office:  CTRB 5239
  • Phone:  352-294-5923
  • Fax:  352-294-5931
  • Email:  susmita.datta@ufl.edu
  • Website: susmitadatta.org

Department of Biostatistics
College of Public Health & Health Professions College of Medicine
University of Florida
2004 Mowry Rd
P.O. Box 117450
Gainesville, FL 32611


  • 1995 PhD University of Georgia (Statistics)
  • 1990 MS University of Georgia (Applied Statistics)
  • 1986 BS University of Calcutta, India (Physics)

Curriculum Vitae

Professional Biography

Susmita Datta has received her PhD degree in Statistics from the University of Georgia, Athens, Georgia, USA followed by a postdoctoral training in Biostatistics from the Emory University. She has joined the Department of Biostatistics at the University of Florida in 2015 with a Preeminent hire as a tenured Full Professor. Prior to that, she was a Distinguished Scholar and a Tenured Full Professor at the University of Louisville and at the Georgia State University as a tenured Associate Professor. She is a fellow of the American Statistical Association (ASA), an elected member of the International Statistical Institute (ISI), and fellow of the American Association for the Advancement of Science (AAAS). Her research area includes Biostatistics and Bioinformatics/Computational Biology. Her research contributions spans all ‘omics’ related high dimensional data such as RNA-sequencing, Single Cell RNA sequencing and mass spectrometry data for proteiomics, lipidomics, metabolomics and good old microarray data. In addition to that, her computing laboratory is involved in methodological and software development in clustering and classification techniques, statistical issues in population biology, systems biology, survival analysis, multi-state models and big data analytics. She has published a book on “Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry” by Springer.

Dr. Datta is widely (>100) published in peer reviewed journals. The National Science Foundation and the National Institutes of Health have continuously funded her work. Her constant involvement with Big and fat data made her interested in Data science. She has guided more than 30 students through their theses and dissertations. She promotes women in STEM fields.


  • Biostatistics
  • Bioinformatics/Computational Biology
  • Genomics
  • Proteomics
  • Metabolomics/Lipidomics
  • Clustering and Classification
  • Population Biology
  • Survival Analysis
  • Nonparametrics
  • Personalized Medicine
  • Complex disease modeling and Biomarker identification in Cancer, Alzheimer, Pain and infectious diseases.