Keshav Pokhrel, Ph.D.

Associate Professor of Statistics; Statistics Undergraduate Program Advisor
Keshav Pokhrel
College of Arts, Sciences, and Letters
Mathematics and Statistics
2087 College of Arts, Sciences, and Letters Building | 4901 Evergreen Road | Dearborn, MI 48128
Winter 2024 - Monday & Wednesday 1:00-2:00pm (MLC) and Tuesday 10:00-11:00am (Virtually).

Teaching Areas:

Actuarial Mathematics, Applied Statistics, Master of Science in Applied and Computational Mathematics, Statistics

Research Areas:

Epidemiology of Cancer, Statistical Methods

Biography and Education

Keshav Pokhrel received his Ph.D. and M.S. degrees in Applied Statistics from the University of South Florida in 2011 and 2013 respectively, M.S. in Mathematics from Marshall University, West Virginia in 2008, and B.S in Mathematics and Physics from Tribhuvan University, Kathmandu, Nepal in 2001. He was an Assistant professor of statistics in the Department of Mathematics and Computer Science at Mercyhurst University, Erie, Pennsylvania between 2013 and 2015. His research interests include change-point problems, high dimensional and functional data analysis, extreme value distribution, Bayesian inference, statistical computing,  and statistical analysis of RNA sequencing data. 

 

Teaching and Research

Research

Teaching Interests

Applied Statistics, Survival and reliability Analysis, Time Series Analysis, Statistical Computing

Research Interests

Change-Point Problem, Bayesian Inference, Functional Data Analysis,  RNA-Seq Data Analysis

Selected Publications

  1. Pokhrel, N., Khanal N., Pokhrel, K., and Tsokos, C.,``Cybersecurity: A Predictive Analytical Model for Software Vulnerability''.  Journal of Cyber Security Technology, 2020.
  2. Sharaf, T., Williams, T., Chehade, A., and Pokhrel, K. , ``BLNN: An R Package for Training Neural Networks Using Bayesian Inference". Vol 11, 100432, SoftwareX, 2020.
  3. Pokhrel, K. , Sharaf, T., Bhandari, P., and Ghimire, D., ``Farm Exit among Smallhoder Farmers of Nepal: A Bayesian Logistic Regression Models Approach". Agricultural Research, 2020, DOI 10.1007/s40003-020-00465-4.
  4. Aryal, G., Pokhrel, K., Khanal, N., and Tsokos, C., ``Reliability Models Using the Composite Generalizers of Weibull Distribution". Annals of Data Science, 6 (4), pp.807-829, 2019.
  5. Wong, D., Plumb, J., Talab, H., Kurdi, M., Pokhrel, K., and Oelkers, P., `` Genetically compromising phospholipid metabolism limits Candida albicans’ virulence". Mycopathologia, January 2019. https://doi.org/10.1007/s11046-019-00320-3.