Mojgan Golzy, PhD


I focus my research career on a mix of collaborative investigation and methodological development motivated by interesting scientific problems I am involved with. My research interests include Machine Learning techniques such as clustering and Bayesian network model, analysis of the mixed effect models, stochastic models, survival analysis, competing risk models, and marginal structural modeling. I believe that interdisciplinary collaboration is the key to making scientific progress, and have collaborated with researchers from other departments, both as a coauthor, and primary author. I would like to apply statistical procedures and develop new approaches for answering critical scientific questions. During my postdoctoral research, I developed a clustering method based on the mixtures of Poisson kernels for clustering high dimensional data on sphere. My Ph.D. dissertation in biostatistics focused on recurrent events data. I proposed a nonlinear hierarchical mixed effect model as an approach to event intensity rate modeling. In my master’s project, I used multivariate linear path model (MLPM) analysis to assess the effect of blood chemistry levels on time to hospitalization of Chronic Kidney Disease (CKD) patients. My research in pure mathematics was in the areas of category theory and point-free topology.

Academic Information

Assistant Research Professor


183 Galena
Columbia, MO 65211
United States

P. 573-884-9813

Research Interests

  • Machine Learning techniques such as clustering and Bayesian network model
  • Analysis of the mixed effect models
  • Analysis of the recurrent event data
  • Survival analysis
  • Competing risk models

Education & Training

Post-Graduate School

2016 Biostatistics PhD, University at Buffalo
2011 Biostatistics MA, University at Buffalo
2002 Mathematics Phd, Shahid Beheshti University, Iran


Golzy M, Carter RL: Generalized Frailty Models for Analysis of Recurrent Events. Journal of Statistical Planning and inferences 200 (2019) 213-222.

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