- Distinguished Professor of Statistics, UC Berkeley
- Co-I for Moore/Sloan Data Science Environments
Philip B. Stark's research centers on inference (inverse) problems and uncertainty quantification, especially confidence procedures tailored for specific goals. Applications include causal inference, the U.S. Census, climate modeling, cosmology, earthquake prediction and seismic hazard analysis, election auditing, endangered species, epidemiology, evaluating and improving teaching and educational technology, food web models, health effects of sodium, the geomagnetic field, geriatric hearing loss, information retrieval, Internet content filters, litigation, resilient and sustainable food systems, risk assessment (including natural disasters and food safety), the seismic structure of Sun and Earth, spectroscopy, spectrum estimation, and uncertainty quantification for computational models of complex systems.
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