Scientific Dissemination Committee

Scientific Dissemination Committee Co-Chair

Laura B. Balzer is an Associate Professor of Biostatistics at the University of California, Berkeley, School of Public Health. Her expertise is in causal inference, machine learning, and messy real-world data. Dr. Balzer’s work addresses challenges in the design and analysis of both randomized trials and observational studies and has largely been motivated by ongoing collaborations in Uganda and Kenya. She is the Primary Statistician for several studies aiming to eliminate HIV and improve community health in rural East Africa (e.g., www.searchendaids.com). Overall, Dr. Balzer’s work is informed by cross-disciplinary, real-world problems and aims to ensure methodological advances in academia translate into real-world impact.

I employ Bayesian methods to achieve my research goals. Practically speaking, the machinery of Bayesian methods (Markov chain Monte Carlo) allows a great deal of flexibility in model specification and allows us to examine exposures as mixtures in ways that traditional statistical tools are simply incapable of handling. This comes with a computational cost (some models can take days to run), but one that can be well worth it.