Curriculum Vitae

Pramesh Subedi, Ph.D.

Assistant Professor of Instruction
Department of Mathematics
Ohio University


Academic Profile

Statistician and biostatistician with research interests in survival analysis, longitudinal data analysis, causal inference, machine learning, and statistical methods for biomedical and public health research. My work integrates methodological statistics with collaborative health research to support disease prediction, clinical decision-making, and population health.


Current Appointment

Assistant Professor of Instruction
Department of Mathematics
Ohio University
2023–Present


Education

Ph.D. in Applied Mathematics
University of North Carolina at Charlotte
2021

M.S. in Mathematics
Ohio University
2015

M.A. in Mathematics
Tribhuvan University, Nepal
2000

B.Ed. in Mathematics
Tribhuvan University, Nepal
1997


Research Programs

NHANES Survival Analysis Research Program

Developing statistical and machine learning methods for mortality prediction, clinical risk assessment, and chronic disease epidemiology using the National Health and Nutrition Examination Survey (NHANES).

Longitudinal Aging Research Program

Investigating risk factors for incident diabetes and chronic disease progression among older U.S. adults using the Health and Retirement Study (HRS).

Methodological Biostatistics Research

Developing semiparametric statistical methods for survival analysis, missing data, complex sampling designs, and efficient estimation motivated by biomedical research.


Selected Publications

Peer-Reviewed Journal Article

Subedi, P., Dahal, K. R., Pokhrel, N. R., Bhandari, R., Gaire, S., Dahal, M., & Giwa, M. (2024).
Predicting Coronary Artery Disease Using Machine Learning.
International Journal of Statistics and Probability.

Book Chapter

Subedi, P., Sun, Y., & Gilbert, P. B. (In Press).
Semiparametric Additive Hazards Models with Missing Covariates, with Application to the Antibody Mediated Prevention HIV Trials.
In Next-Gen Lifetime Data Analysis: Emerging Innovations and Applications. Springer.


Teaching

I teach undergraduate and graduate courses in statistics, probability, and data science with an emphasis on statistical reasoning, computational methods, and real-world applications. My teaching philosophy integrates active learning, project-based instruction, and reproducible statistical computing to prepare students for careers in mathematics, statistics, and data science.


Professional Service


Technical Expertise

Programming

R • Python • SQL

Statistical Software

SAS • Stata • MATLAB

Scientific Computing

Git • GitHub • Google Colab

Scientific Writing

LaTeX • Overleaf • R Markdown


Contact

Department of Mathematics
Ohio University

Email: subedi@ohio.edu