Ruitao Lin

Broadly speaking, my research work is driven by the interdisciplinary nature of statistics and the interplay between theory and practice. My primary research interest lies in developing adaptive statistical methods for clinical trials. Collaborative in nature, clinical studies need input from experts in a variety of domains, to test the efficacy and safety of potential treatments in human volunteers. I am very interested in early-phase clinical trial designs and adaptive methods in precision medicine and immunotherapy. In particular, I researched innovative and robust designs for single- or multiple-agent phase I dose-finding trials, enabling a more efficient escalation to the therapeutic dose levels to cope with the changing landscape of cancer research. To address new challenges brought by the precision medicine, I am interested in developing efficient and flexible designs for adaptively optimizing subgroup-specific dose-schedule regimes, conducting safety monitoring, and addressing problems of the late-onset responses in early-phase clinical trials. A clinical trial is inherently a complex procedure that involves sequential decision making and multiple relevant sources. Moving forward, I am also interested in application of sophisticated machine learning techniques to clinical trials in order to enhance trial efficiency and reduce variability. Besides clinical trial designs, I study other interesting statistical problems, such as causal inference from a robust Bayesian perspective, and high dimensional inference using random matrix theory.