Plenary Keynote Speakers
Iain Johnstone, Stanford University
Presentation Topic: Largest Eigenvalues and Eigenvectors in Multivariate Analysis
The eigenvalues of Wishart matrices play a central role in classical multivariate analysis. A new impetus to approximate distribution results has come from methods that imagine the number of variables as large. We focus on the largest eigenvalue in particular, and review null distribution approximations to Gaussian, single and double Wishart problems in terms of the Tracy-Widom laws. If time permits, we will also briefly mention estimation of the eigenvectors associated to the top eigenvalues.
Iain Johnstone is Marjorie Mhoon Fair Professor of Quantitative Science in the Department of Statistics at Stanford University. He holds a joint appointment in biostatistics in Stanford's School of Medicine. He received his Ph.D. in Statistics from Cornell in 1981.
His work in theoretical statistics aims to provide insight into methods of data analysis in diverse areas of science and medicine. He has used ideas from harmonic analysis, such as wavelets, to understand noise-reduction methods in signal and image processing. More recently, he has applied random matrix theory to the study of high-dimensional multivariate statistical methods, such as principal components and canonical correlation analysis. In biostatistics, he has collaborated extensively with investigators in cardiology and prostate cancer.
A native of Australia, Johnstone is a member of the U.S. National Academy of Sciences and the American Academy of Arts and Sciences and a former president of the Institute of Mathematical Statistics.
Jennifer Tour Chayes, Microsoft Research New England
Presentation Topic: Interdisciplinarity in the Age of Networks
Everywhere we turn these days, we find that dynamical random networks have become increasing appropriate descriptions of relevant interactions. In the high tech world, we see mobile networks, the Internet, the World Wide Web, and a variety of online social networks. In economics, we are increasingly experiencing both the positive and negative effects of a global networked economy. In epidemiology, we find disease spreading over our ever growing social networks, complicated by mutation of the disease agents. In problems of world health, distribution of limited resources, such as water resources, quickly becomes a problem of finding the optimal network for resource allocation. In biomedical research, we are beginning to understand the structure of gene regulatory networks, with the prospect of using this understanding to manage the many diseases caused by gene mis-regulation. In this talk, I look quite generally at some of the models we are using to describe these networks, and at some of the methods we are developing to indirectly infer network structure from measured data. In particular, I will discuss models and techniques which cut across many disciplinary boundaries.
Jennifer Chayes is Managing Director of Microsoft Research New England. Her research areas include phase transitions in discrete mathematics and computer science, structural and dynamical properties of self-engineered networks, and algorithmic game theory. She is the coauthor of over 100 scientific papers and the co-inventor of over 20 patents.
Chayes serves on numerous institute boards, advisory committees and editorial boards, including the Turing Award Committee, the US National Committee on Mathematics, and the Board of Trustees of the Mathematical Sciences Research Institute. Chayes received her Ph.D. at Princeton, and held postdoctoral fellowships at Harvard and Cornell. She is the recipient of the NSF Postdoctoral Fellowship, the Sloan Fellowship, and the UCLA Distinguished Teaching Award. Chayes is a Fellow of the AAAS and the Fields Institute, and a National Associate of the National Academies.