Statistics at UC Berkeley

Hao Ge, Peking University
Jan 18, 2017 3:30pm 891 Evans Hall
Stochastic process has a glorious history in physics, chemistry and biology. Due to the advance of single-molecule techniques, stochastic modeling and computation become more and more useful and popular recently. I will talk about several different issues related to stochastic processes at single-molecule and single-cell levels, including stochastic theory of nonequilibrium statistical mechanics,...
Seth Flaxman, Department of Statistics, Oxford
Jan 18, 2017 4:00pm 1011 Evans Hall
In this talk I will highlight the statistical machine learning methods that I am developing, in response to the needs of my social science collaborators, to address public policy questions. My research focuses on flexible nonparametric modeling approaches for spatiotemporal data and scalable inference methods to be able to fit these models to large datasets. Most critically, my models and...
Geoffrey Grimmett, Cambridge University
Jan 25, 2017 3:10pm 1011 Evans Hall
The problem of self-avoiding walks (SAWs) arose in statistical mechanics in the 1940s, and has connections to probability, combinatorics, and the geometry of groups. The basic question is to count SAWs. The so-called 'connective constant' is the exponential growth rate of the number of n-step SAWs. We summarise joint work with Zhongyang Li concerned with the question of how the connective...
Daniel Kowal, Cornell University
Jan 25, 2017 4:00pm 1011 Evans Hall
I will present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data--functional, time dependent, and multivariate components--we extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We also develop Bayesian spline theory in a more general constrained...
Yang Chen, Department of Statistics, Harvard University
Jan 30, 2017 4:00pm 1011 Evans Hall
Single-molecule experiments investigate the kinetics of individual molecules and thus can substantially enhance our understandings of various organisms. Analyzing data from single-molecule experiments poses a number of challenges: (a) the inherent stochasticity of molecules is usually buried in random experimental noise; (b) single-molecule behavior can be highly volatile. For both of these...

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.