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
Oct 8, 2014
Apr 14, 2015
Alex Shkolnik, UC Berkeley (Speaker - Featured)
Berkeley-Davis Joint Statistics Colloquium: Spectral analysis of linear time series in high dimensions
Debashis Paul, Department of Statistics, UC Davis (Speaker)
We study the spectral behavior of a class of $p$-dimensional stationary linear processes in two different high-dimensional regimes, (A) $p/n \to c \in (0,\infty)$; and (B) $p/n \to 0$, as $p,n \to \infty$. The key structural assumption is that the linear process is driven by a sequence of $p$-dimensional real or complex random vectors with i.i.d. entries possessing zero mean, unit variance and...
Anil Aswani, IEOR, UC Berkeley (Speaker)
Motivated by combinatorial regression problems (which we interpret as low-rank tensor completion), we study noisy completion for positive tensors. Existing approaches convert this into matrix completion, but this is unable to achieve the best statistical rates possible. Here, we show that a specific class of low-rank tensors (namely those parametrized as continuous extensions of hierarchical...
Georg Menz, Stanford University
The log-Sobolev inequality (LSI) is a very useful tool for analyzing high-dimensional situations. For example, the LSI can be used for deriving hydrodynamic limits, for estimating the error in stochastic homogenization, for deducing upper bounds on the mixing times of Markov chains, and even in the proof of the Poincare conjecture by Perelman. For most applications, it is crucial that the...
Dan Boneh, Stanford University (Speaker - Featured), Richard Karp, Simons Institute for the Theory of Computing, UC Berkeley (Moderator), Ron Fagin, IBM Almaden (Panelist/Discussant), Russell Impagliazzo, UC San Diego (Panelist/Discussant), Sandy Irani, UC Irvine (Panelist/Discussant), Christos Papadimitriou, Simons Institute for the Theory of Computing, UC Berkeley (Panelist/Discussant), Omer Reingold, Microsoft Research (Panelist/Discussant), Ryan Williams, Stanford University (Panelist/Discussant)
Theoretically Speaking Series: A new lecture series highlighting exciting advances in theoretical computer science. These events are intended for a general audience; no special background is assumed. Theoretically Speaking is produced by the Simons Institute for the Theory of Computing, with sponsorship from the Mathematical Sciences Research Institute (MSRI) and Berkeley City College.