Experience

Before coming to USC, I spent a year at a clinical research organinzation where I worked on dynamic monitoring in clinical trials. I became an adept programmer in R and SAS. I worked on model development and implementation. I was fortunate enough to collaborate with several researchers, including Peng Zhang, Dr. Tai Xie, Dr. Wei-Chung Shih, Dr. K.K Gordon Lan, and Dr. Ping Gao. Through my interactions with them I became more interested in methodology and trial design. With their encouragement and support, I took the next step forward to pursue a Ph.D in Biostatistics.

At USC, I am working mainly on my research goal of bringing adaptive randomization into clinical trial design. I am fortunate to be mentored by Dr. Lindsay Renfro. In my Ph.D training, I've also dived deeper into Bayesian statistics, statistical inference, and computer programming. Finally, I also have been able to learn about different areas within Biostatistics and increased my general understanding of the subject.

Teaching Experience

• P6104 - Introduction to Biostatistics Department of Biostatistics, Mailman School of Public Health, Columbia University

Working Experience

• University of Southern California Research Assistant
• Working on thesis topic on Bayesian biomarker-adaptive clinical trial design algorithms for personalized medicine in oncology
• Conducted literature review on basket trial and adaptive enrichment trial designs
• Brightech International, LLC/CIMS Global SAS and R Programmer
• Develped simulations for generalized adaptive trial design using R
• Pioneered the statistical support and implementation for a software program in dynamic monitoring of on-going clinical trials using R, which was later applied in the Chinese Clinical Trials of Remdesivir in treating patients with COVID-19
• Participated in the study of pomalidomide(Pomalyst) by visualizing clinical trial data, which is the first new FDA approved Kaposi sarcoma treatment in 20 years
• Genentech Statistical Programming Analysis - Data Analytics Intern
• Explored possibly early endpoint to predict asthma exacerbations using data from two identical,randomized, multicenter, placebo-controlled Phase III studies
• The main goal was to evaulate new options to establish Phase II proof of concept and impact on overall clinical development timelines

Relevant Courses

• Advanced Statistical Computing, Algorithms, Statistical Methods in Clinical Trial, Theory of Statistics, High-Dimensional Data Analysis, Design of Clinical Studies, Machine Learning, Design of Medical Experiments, Survival Analysis, Longitudinal Analysis, Pharmaceutical Statistics, Linear Regression, Inference, Categorical Data Analysis, Probability, Generalized Linear Models, Linear Algebra, Calculus of Several Variables, Statistical Programming with R