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 forage.berkeley.edu).
Real name:Philip Stark
September 6, 2018