Philip B. Stark is a professor of statistics at Berkeley whose 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, and Internet content filters. 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.” 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 Berkeley.