Philip B. Stark

photo of P.B. Stark

Philip B. Stark

Professor
Office / Location
403 Evans Hall
Phone
510-394-5077
Email
stark@stat.berkeley.edu
Research Expertise and Interests

uncertainty quantification and inference, inverse problems, nonparametrics, risk assessment, earthquake prediction, election auditing, geomagnetism, cosmology, litigation, food/nutrition

My research centers on inference problems, primarily in physical and social sciences. I am especially interested in confidence procedures tailored for specific goals and in quantifying the uncertainty in inferences that rely on numerical models of complex systems. I've done research on the internal structure of Sun and Earth, climate modeling, clinical trials, earthquake prediction, the Big Bang, the geomagnetic field, election integrity, gender bias in academia, geriatric hearing loss, the U.S. census, the effectiveness of Internet content filters, endangered species, spectrum estimation, urban foraging, and information retrieval. I am also interested in nutrition, food equity, and sustainability and am studying whether foraging wild foods in urban environments could contribute meaningfully to nutrition, especially in "food deserts." I am interested in numerical optimization, and have published some software.

I've consulted in product liability litigation, truth in advertising, equal protection under the law, jury selection, election security and contested elections, trade secret litigation, employment discrimination litigation, import restrictions, insurance litigation, natural resource legislation, environmental litigation, patent 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, targeted marketing, water treatment, sampling the web, risk assessment, and whistleblower cases. 

I also 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, I also 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.