Gyanendra Pokharel

Dr. Gyanendra Pokharel serves as an Associate Professor in the Department of Mathematics and Statistics at the University of Winnipeg. He concurrently holds an adjunct appointment as Assistant Professor in the Department of Community Health Sciences at the University of Manitoba.

His educational qualifications include:

• Ph.D. in Applied Statistics from the University of Guelph, Canada
• M.Sc. in Applied Mathematics from Wilfrid Laurier University, Canada
• M.Sc. in Pure Mathematics from Tribhuvan University, Nepal
• B.Sc. in Mathematics from Tribhuvan University, Nepal

Dr. Pokharel's research spans Biostatistics, Cancer Epidemiology, Bayesian & Computational Statistics, and Infectious Disease Modeling. He develops advanced methodologies for spatial infectious disease surveillance, including emulation-based inference and ensemble learning techniques. His cancer epidemiology work focuses on breast cancer stage-shifting through simulation studies, while his rheumatology research employs network meta-analysis to evaluate treatment effectiveness. His expertise in Statistical Learning enables innovative classification and clustering approaches for complex health data.

Analysis of his 15 most recent publications (2014-2024) reveals a dominant focus on infectious disease modeling (60% of articles), particularly spatial-temporal methods and machine learning applications. Biostatistics contributions (40%) center on cancer epidemiology and rheumatology, featuring network meta-analyses and discrete-choice experiments for patient preference studies. His work consistently integrates computational statistics with real-world health challenges.

No specific scientific awards are documented in the provided materials, though his research is competitively funded.

Dr. Pokharel actively mentors students through his NSERC Discovery Grant-funded projects. He teaches advanced statistical computing courses (STAT 2903/3904) and core statistics courses (STAT 1301/1302), with past instruction in statistical learning (STAT 4103). Prospective graduate students in biostatistics, spatial statistics, or machine learning applications are encouraged to contact him for research opportunities.