Philip B. Stark | Distinguished Professor of Statistics | University of California

Berkeley, CA 94720-3860 | stark [at] stat.berkeley.edu | 510-394-5077 | @philipbstark

My research centers on inference problems and uncertainty quantification, especially confidence procedures tailored for specific goals. I've published on causal inference, the U.S. Census, climate, clinical trials, cosmology, earthquake prediction and seismic hazard analysis, election integrity, endangered species, epidemiology, evaluating teaching, gender bias, educational technology, food web models, the geomagnetic field, geriatric hearing loss, information retrieval, Internet content filters, litigation, optimization, reproducibility and replicability, resilient and sustainable food systems, risk assessment (including natural disasters and food safety), the seismic structure of Sun and Earth, soil carbon, spectroscopy, and spectrum estimation. Methods I developed for auditing elections have been incorporated into laws in about 15 U.S. states. 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. I currently serve on the Board of Advisors of the U.S. Election Assistance Commission and the Strategic Board of Advisors of the Open Source Election Technology (OSET) Institute. Formerly, I served on the governance committee of the Association of Foragers, the Board of Directors of Verified Voting Foundation, and the Board of Directors of the Election Integrity Foundation.

picture of P.B. Stark

I consult on topics including antitrust, truth in advertising, behavioral targeting, the U.S. Census, clinical trials, construction defects, consumer class actions, credit risk models, election contests, environmental litigation, equal protection, First Amendment protections, geochemistry, intellectual property and patents, jury selection, trade secrets, employment discrimination, food safety, import restrictions, insurance and reinsurance litigation, insurance fraud, Internet content filters, lottery fraud, mortgage-backed securities, natural resource legislation, oil exploration, pharmaceuticals and nutraceuticals, product liability class actions, public utilities, quality control, Qui Tam (whistleblower / false claims) cases, risk assessment, sampling in litigation, signal processing, torts and toxic torts, wage and hour class actions, warranties, water treatment, and white-collar crime.

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 22 October 2023. P.B. Stark. statistics.berkeley.edu/~stark/index.html