Understanding What Treatments Work and How to Tailor Health Care to Individuals Using Real-World Data
Jared Huling’s research focuses on the development and implementation of high-quality, innovative statistical methods for rigorous analysis of complex observational health data. His methods integrate statistics, machine learning, and advanced computational techniques to solve challenging real-world problems using the increasingly complex data sources available today. In particular, his work focuses on the development of statistical approaches to better understand causal relationships from observational data, statistical methodology to optimally match individual patients with the right treatments, and statistical machine learning approaches for accurate and interpretable risk prediction using high-dimensional, heterogeneous data with the aim of improving patient health outcomes.