Sunil Mathur, Ph.D., MBA
Associate Professor of Epidemiology and Biostatistics
Robison Hall 222
- Ph.D. (Statistics), M.Phil (Statistics), M.Sc. (Statistics), B.Sc. (Math) University
of Delhi, India
- MBA, Indira Gandhi National University
- Certified training in SAS, SPSS, and SAP, USA
- Certified training in Computer Programming and System Design
- Editor, PLOS ONE
- Editorial Board Member, International Journal of Medicine and Public Health
- Associate Editor, International Journal of Statistics and Systems
- Associate Editor, Global Journal of Pure and Applied Mathematics
- Cancer Communication, Perception, and Prevention
- Public Health
Book: Statistical Bioinformatics with R, Academic Press, USA, January 2010.
Mathur, S. K. and Sepehrifar, M. (2012). A New Signed Rank Test Based on Slopes of Vectors for
Bivariate Location Problems. Statistical Methodology, Accepted for Publication, DOI: 10.1016.
Mathur, S.K., and Sadana, A. (2011). Finding Differentially Expressed Genes in High Dimensional
Data: Rank Based Test Statistic via a Distance Measure. Statistical Methods in Medical Research. Accepted for publication, DOI: 10.1177/0962280211434428.
Mathur, S. K. (2009). A Run based Procedure to Identify Time-lagged Gene Clusters in Microarray
Experiments. Statistics in Medicine, 28 (2), 326-337.
Mathur, S.K. (2009). A New Nonparametric Bivariate Test for Two-Sample Location Problem. Statistical Methods and Applications, 18 (3), 375-388.
Murthy, S.S., Kiran, V.S.R, Mathur, S.K., and Murthy, S. (2008). Noninvasive Transcutaneous Sampling of Glucose by Electroporation.
Journal of Diabetes Science and Technology, 2(2), 250-254.
Mathur, S.K., and Smith, P.F. (2008). An Efficient Nonparametric Test for Bivariate Two-Sample
Location Problem, Statistical Methodology, 5 (2), 142-159.
Mathur, S.K. and Dolo, S. (2008). A new Efficient Statistical Test for Detecting Variability in
the Gene Expression Data, Statistical Methods in Medical Research, 17, 405-419.
Recent Accomplishments and Honors:
Faculty Research Fellow Award, University of Mississippi, 2006, and 2007
First Prize, Sigma Xi Research Poster Competition, University of Mississippi, 2007
Dr. Mathur’s research interests focus on both biostatistical applications and methodological development
of biostatistics. Dr. Mathur wrote a book Statistical Bioinformatics with R, Academic Press, USA in January 2010. His research interests include public health, nonparametric
statistics, likelihood procedures, genomics, epidemiology, and biostatistics. In some
of the papers (Mathur and Smith, 2007; Mathur and Dolo, 2007; Bokka and Mathur, 2006;
Mathur, 2005; Sen and Mathur, 1997, 2000), his group has proposed new methodologies
for analyzing complex multivariate data. Currently he is working in the area of cancer
research. Cigarette smoking is the leading preventable cause of death and disease
in the world and new effective efforts to reach smokers must be explored. Dr. Mathur
is working with his collaborators to assess the role of cancer information seeking
behavior in developing and disseminating effective smoking cessation strategies. In
another study, Dr. Mathur’s group found that the cancer history of first-degree relatives
(parents, siblings, and offspring) of all-age cancer patients, play an important role
suggesting a familial contribution to cancer. The associations of family health history
(FHH) with specific cancer sites including brain, breast, colon/rectum, genital, lip/oral,
kidney cancer, and testicular teratomas have been shown in many studies. Most of the
childhood cancers have been associated with FHH but a precise etiologic link yet to
be firmly established. Since parental non-malignant diseases and cancer may be transmitted
to children through inheritable DNA, Dr. Mathur is working with his collaborators
to determine whether FHH may play a major role in increased childhood cancer risk
among urban children. He is also working in the area of proteomics. The aim of Dr.
Mathur’s research is to determine the specific proteins and their abundance and compare
them across different experiments (patients), outcomes, and treatment groups, and
then find protein markers for the disease. His work will enable researchers in medical
field to identify these protein signatures and hence help the researchers to develop
targeted drugs and cure the disease in more effective ways. Dr. Mathur’s work in the
area of nonparametric inference has focused on bivariate and multivariate location
problems. In some of the papers, he has proposed new methodologies for analyzing data
using nonparametric procedures. Three students have completed their Ph.D. under Dr.
Mathur’s supervision. He is Associate Editor of two international journals, editorial
board member of a journal, and a reviewer for several international journals.