Philip B. Stark

photo of P.B. Stark
Primary Research Area: 
Applied & Theoretical Statistics
Applied Statistics, Statistics in Physical Sciences, Statistics in Social Sciences
Research Interests: 
uncertainty quantification and inference, inverse problems, nonparametrics, risk assessment, earthquake prediction, election auditing, geomagnetism, cosmology, litigation, food/nutrition
stark [at] stat [dot] berkeley [dot] edu
Office / Location: 
403 Evans Hall

Stark's 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, election auditing, 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. Numerical optimization is important to his work; he has published some optimization software. He is also interested in nutrition, food equity, and sustainability and is studying whether foraging wild foods could contribute meaningfully to nutrition, especially in "food deserts." To that end, he is investigating the occupancy, nutritional value, and possible toxicity of wild foods in the East Bay. Stark's consulting and expert witness experience include 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. Stark created SticiGui, 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. SticiGui was the basis of the first online course (in any subject) taught at UC Berkeley. With Ani Adhikari, he co-taught an introductory statistics MOOC in 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.