Events Calendar

CS Colloq: Dr. Shuheng Zhou

Talk Title: High dimensional statistical estimation and modelling
Speaker: Dr. Shuheng Zhou
Host: Prof. Craig KnoblockAbstract:
A line of recent work has demonstrated that sparsity is a powerful technique in signal reconstruction and in statistical estimation.
Given n noisy samples with p dimensions, where n Undirected graphs are often used to describe high dimensional distributions.
Under sparsity conditions, the graph can be estimated using $L_1$ penalization methods. However, most methods prior to our work have assumed that the data are independent and identically distributed.
If the distribution---and hence the graph--- evolves over time, the data are not longer identically distributed. In the second part of the talk, I show how to estimate the sequence of graphs for non-identically distributed data and establish some theoretical results.In the last part of this talk, I will make a brief connection between my research on high dimensional statistical estimation and on statistical privacy, where the general goal is to construct a data release mechanism that protects individual privacy while preserving information content.
Parts of this talk are based on joint work with Professors John Lafferty and Larry Wasserman at Carnegie Mellon University.Bio:
Shuheng Zhou received her Ph.D. from Carnegie Mellon University in August 2006, co-advised by Professors Greg Ganger and Bruce Maggs; Her dissertation work focused on combinatorial optimization problems in network routing. She then continued as a postdoc fellow at CMU, working with Professors John Lafferty and Larry Wasserman on statistical and machine learning algorithms and theory. She has been a postdoc fellow with Seminar for statistics in Department of Mathematics at ETH Z\"urich, since August 2008. She is currently visiting Department of Statistics at UC Berkeley.

Location: Seaver Science Library (SSL) - 150

Audiences: Everyone Is Invited

Contact: CS Front Desk