Philip B. Stark | Professor of Statistics | University of California
Berkeley, CA 94720-3860 | stark [at] stat.berkeley.edu | 510-394-5077 | @philipbstark
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, 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, nonparametrics (constrained confidence sets for functions and probability densities), 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 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 the Berkeley Open Source Food Project.
Consulting and expert witness topics have included truth in advertising, election contests, equal protection under the law, intellectual property and patents, jury selection, trade secrets, employment discrimination, import restrictions, construction defects, insurance litigation, mortgage-backed securities, 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 a series of introductory statistics MOOCs in spring 2013. Nearly 53,000 students enrolled in the first course, of whom more than 10,600 finished and nearly 8,200 received a certificate of completion.
Last modified 1 July 2015. P.B. Stark. statistics.berkeley.edu/~stark/index.html