[talks] Eric Xing talk 4:15pm TODAY

[Olga Troyanskaya]

Please come to an exciting colloquium talk by Eric Xing from CMU.
Eric is speaking today (Wednesday, October 10) at 4:15 P.M. in the Small
Auditorium (Room 105).

Title: "Probabilistic Graphical Models and Algorithms for Integrative 
Bioinformatics"

Abstract:
Probabilistic graphical model is a formalism that exploits the conjoined
talents of graph theory and probability theory to build complex models
out of simpler pieces. It offers a powerful language to elegantly define
expressive distributions under complex scenarios in high-dimensional
space, and provides a systematic computational framework for
probabilistic inference. These virtues have particular relevance in
bioinformatics, where many core inferential problems e.g., linkage
analysis, phylogenetic analysis, network reconstruction are already
naturally expressed in probabilistic terms, and must deal with
experimental data with complex structure and temporal and/or spatial
dynamics.

I will discuss our recent work on graphical model inferential
methodology in three areas in bioinformatics: (1) Population structure
and recombination hotspot inference, using a novel approach based on
Dirichlet process priors. I present a hidden Markov version of the
Dirichlet process which allows us to infer recombination events among
haplotypes in an "open" ancestral space. (2) Comparative genomics
prediction of imperfectly conserved transcription factor binding sites,
where multi-resolution phylogenetic inference combines with Markovian
inference to provide sensitive detection of motifs and their
evolutionary turnovers in eleven Drosophila species. (3)
Reverse-engineering of temporally rewiring networks from gene expression
time courses, where a novel hidden temporal exponential random graph
model is employed to model temporal evolution of network topologies
during a biological process, and to facilitate the inference of
transient (rather than a single universal) regulatory circuitry
underlying each time-point of the microarray time series.

Bio:
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 Carnegie Mellon University 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 text/image mining, vision, and machine translation.
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Olga Troyanskaya, Ph.D.
Assistant Professor
Department of Computer Science and
Lewis-Sigler Institute for Integrative Genomics
Princeton University, NJ 08544

(609)258-1749(ph) (609)258-1771(f)
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