Statistics at UC Berkeley: We are a community engaged in research and education in probability and statistics. In addition to developing fundamental theory and methodology, we are actively involved in statistical problems that arise in such diverse fields as molecular biology, geophysics, astronomy, AIDS research, neurophysiology, sociology, political science, education, demography, and the U.S. Census. We have forged strong interdisciplinary links with other departments and areas of study, particularly biostatistics, mathematics, computer science, and biology, and actively seek to recruit graduate students and faculty who can help to build and maintain such links. We also offer a statistical consulting service each semester.
Statistics at UC Berkeley
Speaker: Wachi Bandara, Pluribus Labs (Speaker - Featured)
Speakers: Chi Zhang, Kamyar Kaviani, Nikita Vemuri, and Simon Walter (Speaker - Featured)
As an alternative to traditional loans, young people could issue securities that pay dividends that depend on their future financial success in life. This type of a personal IPO is especially desirable for young people, who for example may need money for a college education, because it allows them to shift the risk of repayment to investors who bet on their future success, unlike in a traditional...
As the complexity of patients and the health care system grows, the human mind struggles to keep pace. Even with perfect incentives, medical decision making is maddeningly difficult, and it’s not surprising that doctors get many of these decisions wrong—with the end result of low-value care. Machine learning can help improve decision making in health care. I’ll present results suggesting that...
Rosemary Gillespie, UC Berkeley (Speaker - Featured)
A central challenge in understanding the origins of biodiversity is that, while we can observe and test local ecological phenomena, we must usually infer the longer-term outcomes of these ecological forces indirectly. My colleagues and I have been developing inferential models at the interface between macroecology and population-level processes, and applying them to data from geological...
Berkeley Distinguished Lectures in Data Science
James R. Lee, University of Washington
Consider deforming the path metric of a unimodular random graph by a (unimodular) reweighting of its vertices. In many instances, a well-chosen change of metric can be used to study the spectral measure, estimate the heat kernel, and bound the speed of the random walk. Even for extensively studied models like random planar maps (e.g., the uniform infinite planar triangulation) and critical...