Priberam Machine Learning Lunch Seminar: Dynamic Network Analysis: Model, Algorithm, Theory, and Application
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Speaker: Eric P. Xing (http://www.cs.cmu.edu/~epxing/) Venue: IST Alameda, Sala PA2 (Edifício de Pós-Graduação) Date: Thursday, April 29th, 2010 Time: 13:00 Lunch will be provided |
Abstract:
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 two recent developments in analyzing what we refer to as the dynamic tomography of evolving networks. I will first present new sparse-coding algorithms for estimating the topological structures of latent evolving networks underlying nonstationary time-series or tree-series of nodal attributes, along with theoretical results on the asymptotic sparsistency of the proposed methods; then, I will present a new Bayesian model 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 and the Enron corporation, and the evolving gene network of fruit fly while aging, at a time resolution only limited by sample frequency. In all cases, our methods reveal interesting dynamic patterns in the networks.
Bio:
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. Sloan Research Fellowship in Computer Science, and the United States Air Force Young Investigator Award.
