Philip B. Stark | Professor and Chair of Statistics | University of California
Berkeley, CA 94720-3860 | stark [at] stat.berkeley.edu | 510-394-5077
My research centers on inference (inverse) problems, especially confidence procedures tailored for specific goals. Applications include the Big Bang, causal inference, the U.S. census, climate modeling, earthquake prediction and seismic hazard analysis, election auditing, endangered species stressors, food web models, the geomagnetic field, geriatric hearing loss, information retrieval, Internet content filters, nonparametrics (confidence sets for function and probability density estimates with constraints), risk assessment, the seismic structure of Sun and Earth, spectroscopy, spectrum estimation, and uncertainty quantification for computational models of complex systems. Methods I developed for auditing elections have been incorporated into 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 software. I'm also interested in nutrition, food equity, and sustainability. I am studying whether foraging wild foods 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.
Consulting and expert witness topics have included truth in advertising, election contests, equal protection under the law, intellectual property and patent litigation, jury selection, trade secret litigation, employment discrimination litigation, import restrictions, insurance litigation, natural resource legislation, environmental litigation, sampling in litigation, wage and hour class actions, product liability class actions, consumer class actions, the U.S. census, clinical trials, signal processing, geochemistry, IC mask quality control, behavioral targeting, water treatment, sampling the web, First Amendment protections, risk assessment, credit risk models, and oil exploration.
SticiGui is an online introductory Statistics "text" that includes interactive data analysis and demonstrations, machine-graded online assignments and exams (a different version for every student), and a text with dynamic examples and exercises, applets illustrating key concepts, and an extensive glossary. In 2007, SticiGui became the basis of the first online course (in any subject) taught at UC Berkeley. With Ani Adhikari, I co-taught an introductory statistics MOOC in spring 2013. Over 52,600 students enrolled in the course, of whom more than 10,600 finished and nearly 8,200 received a certificate of completion.
Last modified 20 January 2014. P.B. Stark. statistics.berkeley.edu/~stark/index.html