I am an assistant professor in biostatistics and epidemiology at Cincinnati Children’s. I strive to build advanced prediction models incorporating clinical and demographic characteristics, omics and imaging as predictors of rapid disease progression. Over the years, I have gained experience and expertise in utilizing biostatistical and machine learning tools, developing new methods for problems of interest, and implementing computational algorithms for analyzing high-dimensional datasets that arise from different areas of applied science such as proteomics, genomics and medicine.

My research focuses on developing and validating biostatistical models incorporating biomarker data for outcomes prediction. Specifically, my interest lies in a variety of both Bayesian and traditional approaches for prediction and estimation models, including multiplicity adjustments for genomic data, as well as covariate modeling of proteomics, environmental exposures, and imaging as predictors of rapid human lung disease (such as in cystic fibrosis, lymphangioleiomyomatosis and bronchiolitis obliterans syndrome) progression. My research interest also includes building Bayesian regularized regression models for identifying potential predictive bio -/geo-markers for high dimensional regression problems.

I started working at Cincinnati Children’s as a postdoctoral fellow in 2018 and became an assistant professor in 2022. During my postdoctoral training, I was honored to be a two-year awardee of the Postdoctoral Fellowship from the US Cystic Fibrosis Foundation. My research is published in various journals, including Statistics in Medicine, Journal of Cystic Fibrosis, Chest, Science of the Total Environment, Journal of Clinical and Translational Science, Annals of the American Thoracic Society, Pediatric Pulmonology and Journal of Clinical Investigation Insight.