Research Areas

His research interests are centered on the development of novel Bayesian methods and computational tools for complex innovative clinical trial designs and high-throughput genomic studies. Specifically, he developed several novel Bayesian methods and designs for clinical trials. These include Bayesian dynamic information borrowing from historical data in clinical trials, adaptive designs for randomized dose optimization trials, a Bayesian adaptive design that utilizes both animal and human data for precision medicine, and decentralized clinical trials using machine learning methods. Additionally, he proposed a Bayesian methodology and computational tool for evaluating the intratumor heterogeneity using multi-region gene expression data.