Philip Stark

Professor of Statistics, University of California, Berkeley
Associate Dean, Division of Mathematical and Physical Sciences
Director, Statistical Computing Facility
Co-I for Moore/Sloan Data Science Environments
BIDS Senior Fellow

Real name: 
Philip Stark

I study inference (inverse) problems, especially nonparametric confidence procedures tailored for specific goals. Applications include the Big Bang, causal inference, the US census, climate modeling, earthquake prediction and seismic hazard analysis, educational technology, election auditing, endangered species stressors, food web models, the geomagnetic field, geriatric hearing loss, information retrieval, Internet content filters, constrained confidence sets for functions and probability densities, risk assessment, the seismic structure of the sun and Earth, spectroscopy, spectrum estimation, and uncertainty quantification for computational models of complex systems. Methods I developed for auditing elections are now in laws in California and Colorado. Methods for data reduction and spectrum estimation I 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. Numerical optimization is important to my work; I've published some optimization software. I'm also interested in nutrition, food equity, and sustainability. I am studying whether urban foraging could contribute meaningfully to nutrition, especially in "food deserts," starting by investigating the occupancy, nutritional value, and possible toxicity of wild foods in the East Bay (see


Environment and Society: Data Sciences for the 21st Century (DS421)

Status: Archived


September 4, 2018 / 8:50am to September 6, 2018 / 11:30am