Statistics at UC Berkeley

Speaker: TBD, TBD (Speaker - Featured)
Oct 22, 2019 11:00am 1011 Evans Hall
Balint Virag, University of Toronto
Oct 23, 2019 4:00pm 1011 Evans Hall
The distribution of the top principal value of a random covariance matrix appears in seemingly unrelated models. These include particle systems originating in cell biology, longest increasing subsequences, and the shape of coffee spots. Random planar geometry lurks behind these phenomena. I will discuss the recently constructed common scaling limit, the directed landscape, and its...
Neyman Seminar
Speakers: Julia Belford, Tingyue Gan, Lisa Goldberg, UC Berkeley (Speaker - Featured)
Oct 29, 2019 11:00am 1011 Evans Hall
Speaker: CANCELED, UC Berkeley (Speaker - Featured)
Nov 5, 2019 11:00am 1011 Evans Hall
Roman Vershynin, University of California, Irvine
Nov 5, 2019 4:00pm 1011 Evans Hall
Deep learning is a rapidly developing area of machine learning, which uses artificial neural networks to perform learning tasks. Although mathematical description of neural networks is simple, theoretical explanation of spectacular performance of deep learning remains elusive. Even the most basic questions about remain open. For example, how many different functions can a neural network compute?...
Neyman Seminar

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.