Philip B. Stark

photo of P.B. Stark
Professor
Primary Research Area: 
Applied & Theoretical Statistics
Sub-Focus: 
Applied Statistics, Statistics in Physical Sciences, Statistics in Social Sciences
Phone: 
510-394-5077
Email: 
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.

Research Interests: 

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

My research centers on inference (inverse) problems, primarily in physical science. I am especially interested in confidence procedures tailored for specific goals and in quantifying the uncertainty in inferences that rely on simulations of complex physical systems. I've done research on the internal structure of Sun and Earth, climate modeling, earthquake prediction, the Big Bang, the geomagnetic field, election auditing, geriatric hearing loss, the U.S. census, the effectiveness of Internet content filters, endangered species, spectrum estimation, urban foraging, and information retrieval. 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, 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 oil exploration.