Creating and sharing knowledge for telecommunications


on 30-06-2008

Speaker: Eric P. Xing
Statistical Network Analysis and Inference: Methods
and Applications
Exploring the statistical properties and hidden characteristics of network entities, and the stochastic processes behind temporal evolution of network topologies, are essential for computational knowledge discovery and prediction based on
network data from biology, social sciences and various other fields. In this talk, I first discuss a hierarchical Bayesian framework that combines the mixed membership model and the stochastic blockmodel for inferring latent multi©\facet roles
of nodes in networks, and for estimating stochastic relationships (i.e., cooperativeness or antagonisms) between roles. Then I discuss a new formalism for modeling network evolution over time based on temporal exponential random graphs, and a
MCMC algorithm for posterior inference of the latent time©\specific networks. The proposed methodology makes it possible to reverse©\engineer the latent sequence of temporally rewiring networks given longitudinal measurements of node attributes, such as intensities of gene expressions or social metrics of actors, even when a single snapshot of such
measurement resulted from each (time©\specific) network is available.
Eric Xing is an assistant professor in the Machine Learning Department, the Language Technology Institute, and the Computer Science Department within 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 building quantitative models and predictive understandings of the evolutionary mechanism, regulatory circuitry, and developmental processes of biological systems; and for building computational intelligence systems involving automated learning, reasoning, and decision©\making in open, evolving possible worlds. Professor Xing received his B.S. in Physics from Tsinghua University, his first Ph.D. in Molecular
Biology and Biochemistry from Rutgers University, and then his second Ph.D. in Computer Science from UC Berkeley. He has been a member of the faculty at CMU since 2004, and his current work involves,
1) graphical models, Bayesian methodologies,
inference algorithms, and optimization techniques for analyzing and mining high©\dimensional, longitudinal, and relational data; 2) computational and comparative genomic analysis of biological sequences, systems biology investigation of gene regulation, and statistical analysis of genetic variation, demography and disease linkage; and 3) application of statistical learning in social networks, text/image mining, vision, and machine translation. He is a recipient of the NSF Career Award, and
the Sloan Research Fellowship in Computer Science.
Monday, June 30th 2008, 14:00 pm
Torre Norte, EA3, Instituto Superior T¨¦cnico

More Information.. ...