School of Computer Science, Carnegie Mellon, USA
Friday 12 March 2010
Seminar Room B10 (Basement)
Alexandra House, 17 Queen Square, London, WC1N 3AR
Dynamic Network Tomography: Model, Algorithm, Theory, and Application
Across the sciences, a fundamental setting for representing and interpreting information about entities, the structure and organization of communities, and changes in these over time, is a stochastic network that is topologically rewiring and semantically evolving over time, or over a genealogy. While there is a rich literature in modeling invariant networks, until recently, little has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks when they are not observable.
In this talk, I will present a number of recent developments on analyzing what we refer to as the dynamic tomography of evolving networks. I will first present new formalisms for modeling network evolution over time; and then, new algorithms for estimating the structure of evolving networks underlying nonstationary time-series or tree-series of nodal attributes, along with theoretical results on the asymptotic sparsistency of the proposed methods; and finally, Bayesian methods for estimating and visualizing the trajectories of latent multi-functionality of nodal states in the evolving networks.
I will show some promising empirical results on recovering and analyzing the latent evolving social networks in the US Senate based voting history, the evolving gene network of fruit fly while aging, and the gene network evolving along cell lineage during breast cancer progression and reversal, at a time resolution only limited by sample frequency. In all cases, our methods reveal interesting dynamic patterns in the networks.
Dr. Eric Xing is an associate professor in the School of Computer Science at Carnegie Mellon University. His principal research interests lie in the development of machine learning and statistical methodology; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional and dynamic possible worlds; and for building quantitative models and predictive understandings of biological systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. His current work involves, 1) foundations of statistical learning, including theory and algorithms for estimating time/space varying-coefficient models, sparse structured input/output models, and nonparametric Bayesian models; 2) computational and statistical analysis of gene regulation, genetic variation, and disease associations; and 3) application of statistical learning in social networks, data mining, vision. Professor Xing has published over 100 peer-reviewed papers, and is an associate editor of the Annals of Applied Statistics, the PLoS Journal of Computational Biology, and a member of the editorial board of the Machine Learning journal. He is a recipient of the NSF Career Award, the Alfred P.