Sudesh Srivastav    

Sudesh Srivastav, PhD
Department of Global Biostatistics and Data Science
Tulane University School of Public Health and Tropical Medicine
1440 Canal St, Room 2019
New Orleans, LA 70112
Phone: 504.504.988.2472
Fax: (504) 988-1706


Research Interests:

Microarray data, experimental designs, crossover designs, diallel cross designs, biological sequence alignment and genome analysis, statistical methods in genetics, design and analysis of clinical trials, multicenter clinical trials.

Professional Achievements:


  • 2009 Recipient, Undergraduate Public Health Studies Research and Teaching Award Grant, Tulane                             University Health Sciences Center, New Orleans, USA
  • 2005  Recipient, Award for Excellence in Intercampus Collaborative Research, Tulane University Health Sciences Center, New Orleans, USA.
  • 2005 LCRC Supported Travel Money for Courses in Practical DNA Microarray Analysis,
  • 2003 NSF Supported Travel Grant for Conference on New Directions in Experimental Design, Chicago, Illinois, USA.
  • 2002 NSF Travel Award for Conference on Designs for Generalized Linear Models, National Institute of Standard and Technology, Gaithersburg, Maryland, USA.
  • 2002 Honorary Member, Delta Omega Society in Public Health, Eta Chapter, Tulane University School of Public Health and Tropical Medicine.

Educational Background:

  • PhD, statistics, Old Dominion University 1996
  • MS, applied mathematics, New Jersey Institute of Technology
  • MSTAT, statistics, Indian Statistical Institute, India, 1988

Selected Publications:

Review Dr. Sirvastav's publications at his NCBI profile page.


Personal Statement:

My research is focused on the study of optimality and constructions of diallel cross design and block designs under different parameters setups (number of treatments or inbred lines, number of blocks, and block size etc). In particular, I am focused on the methodology of arranging experimental units (or subjects e.g. animals, human patients, agricultural units etc.) and the assignment of treatments (different drugs, doses, methods, technique etc.) or inbred crosses in such a way that comparisons among the treatments/lines are as unbiased and as powerful as possible. The aim is to maximize the overall quality of information so that the results and interpretations are optimal and reliable. My work also examines various problems in experimental issues for microarray data, biological sequence alignment, genomic and proteomics analysis, diagnostic and screening analysis, linear models, combinatorial designs, quality controls techniques, and designing and analysis of randomized controlled trials and multi-center clinical trials.


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Global Biostatistics & Data Science, 1440 Canal St, Suite 2001, New Orleans, LA 70112, 504-988-2102