Alan HubbardMachine Learning
Head of Biostatistics, University of California, Berkeley, School of Public Health. Professor Hubbard is the Principal Investigator of a study of statistical methods related to patient-centered outcomes research among acute trauma patients (PCORI), head of the computational biology Core D of the SuperFund Center at UC Berkeley (NIH/EPA), and a consulting statistician on several federally funded and foundation projects, including a study to measure the impacts of sanitation, water quality, handwashing, and nutrition on child growth and development.
Mr. Hubbard has published over 200 articles and worked on projects ranging from molecular biology of aging, wildlife biology, epidemiology, and infectious disease modeling, but most of his work has focused on semi-parametric estimation in high-dimensional data, with particular applications in precision medicine and other big data applications.
Mr. Hubbard’s study and analysis of T6’s high resolution clinical, physiological and interventional data using his methods-research focused on statistical inference for data-adaptive parameters, is at the core of T6’s next generation of clinical decision support tools, including predictive medicine algorithms.