Philip B. Stark
Philip B. Stark's research centers on inference (inverse) problems and uncertainty quantification, especially confidence procedures tailored for specific goals. Applications include causal inference, the U.S. Census, climate modeling, cosmology, earthquake prediction and seismic hazard analysis, election auditing, endangered species, epidemiology, evaluating and improving teaching and educational technology, food web models, health effects of sodium, the geomagnetic field, geriatric hearing loss, information retrieval, Internet content filters, litigation, resilient and sustainable food systems, risk assessment (including natural disasters and food safety), the seismic structure of Sun and Earth, spectroscopy, spectrum estimation, and uncertainty quantification for computational models of complex systems. Methods he has developed for auditing elections have been incorporated into laws in California, Colorado, and Rhode Island, Texas, Virginia, and Washington. Methods for data reduction and spectrum estimation that he has developed or co-developed are part of the Øersted geomagnetic satellite data pipeline and the Global Oscillations Network Group (GONG) helioseismic telescope network data pipeline.