Washington University in St. Louis
My name is Peinan Zhao. I am an Instructor in the Department of OBGYN at Washington University in St. Louis with expertise in statistical data analysis and modeling development. With a broad background in biophysics and statistics, I am interested in developing multi-level modeling and Machine Learning/Deep Learning methodologies for clinical studies and medical scientific research.
My research focuses on applying different modeling techniques in clinical studies and developing novel computational models related to maternal and fetal health, including adverse pregnancy outcomes, preterm birth prediction, cervical remodeling, and uterine maturation. I contributed to many projects in the Prematurity Research Center, mainly focused on the March of Dimes 1000 women cohort study to predict preterm birth. I developed a novel computational method to analyze actigraphy data and published an online application as an automatic analytical tool. I established an innovative methodology for quantitatively measuring cervix tissue stiffness from transvaginal ultrasound images. I also developed the statistical analysis methodology for Electromyometrial Imaging, a system with which uterine electrical activity can be noninvasively measured and mapped onto the entire uterine surface in three dimensions.