Alan Hubbard is currently Head of Biostatistics, co-director of the Center of Targeted Learning, and head of the computational biology core of the SuperFund Center at UC Berkeley (NIH), as well as a consulting statistician on several federally funded and foundation projects. He has worked as well on projects ranging from molecular biology of aging, epidemiology, and infectious disease modeling, but most of his work has focused on semi-parametric estimation in high-dimensional data. His current methods-research focuses on precision medicine, variable importance, statistical inference for data-adaptive parameters, and statistical software implementing targeted learning methods. He is currently working in several areas of applied research, including early childhood development in developing countries, environmental genomics, and comparative effectiveness research. He has most recently concentrated on using complex patient data for better prediction for acute trauma patients.
Research Expertise and Interest
causal inference, targeted learning, statistical issues in epidemiology, precision medicine and public health