Niloofar Ramezani

Background

About Niloofar Ramezani

I hold a PhD and a master's degree in applied statistics and research methods, along with a bachelor's degree in statistics with a minor in mathematics. My expertise spans statistical and interdisciplinary research, as well as teaching courses in statistics, biostatistics, research methods, and data analytics. With over a decade of experience as a senior statistician, consultant, and biostatistician, I have worked extensively with multiple state agencies. Currently, I serve as a co-PI and co-investigator on multiple projects funded by NIH and other agencies, developing and applying statistical methodologies and machine learning techniques to improve quality of life, particularly among vulnerable populations. My recent applied studies focus on mental health, justice and policy, and various areas of health and biomedicine. My methodological interests include power analysis, longitudinal and correlated studies, joint models, survival and recurrent event analysis, and their applications in health, social, and educational research. I am passionate about handling missing data, efficiently modeling correlated and multilevel data, and extending generalized estimating equations to capture higher response variation—methods highly applicable across biomedical, social, health, engineering, business, education, and econometric fields. I am a two-time recipient of the SAS Ambassador Award for innovative statistical modeling, as well as the Junior Faculty SAS Award, and have earned over two dozen competitive academic awards through SAS, APHA, ASA, and other organizations. My research contributions include developing two new power estimation techniques for longitudinal data, and developing models for handling time-dependent covariates. Beyond research, I am committed to community engagement, having volunteered for multiple years with nonprofit organizations in grant writing, program evaluation, and study design. I also have over 15 years of experience teaching undergraduate and graduate courses in statistics and data analytics, designing curricula for in-person, hybrid, and online learning. My teaching philosophy is to inspire students to apply statistical methods to solve real-world problems. I welcome opportunities to collaborate with fellow researchers and educators to advance statistical research and its applications.