[bioinfo] CS591 BIO: SPECIAL TALK ON THURSDAY (REMINDER)

[Sinha, Saurabh]

Hello,
    This is an announcement for a special bioinformatics talk on Thursday,
March 13, by Prof Eric Xing from CMU. Details of the talk appear below.

    Thanks,
    Saurabh

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Title: Nonparametric Bayesian Methods for Genetic Inference
Speaker: Prof. Eric Xing, Carnegie Mellon University.

Date: Thursday, MARCH 13
Time: 1000 AM - 1100 AM.
Place:4405 Siebel Center.

Abstract:
The problem of inferring the haplotype phases, linkage disequilibrium
patterns, chromosomal recombination events, and population structures from
genetic polymorphism data is essential for understanding the origin and
characteristics of genome variations. Statistical genetic methodologies
developed so far mostly address these problems separately using specialized
models ranging from the coalescence and mixture models for haplotype
inheritance, to HMMs and renewal processes for recombination, but many of
these approaches ignore the inherent uncertainty in the genetic complexity
(e,g., the number of genetic founders of a population) of the data, and the
close statistical and biological relationships underlying different
problems. In this talk, I discuss our recent work on a new class of
nonparametric Bayesian models motivated by the Dirichlet process, for
solving haplotype phasing, LD modeling and demographic inference in an OPEN
ancestral space based on a unified statistical framework. This approach
offers to a compact and natural representation of the population structure
and inheritance processes underlying genetic polymorphism, and leads to
competitive performance on a wide range genetic inference tasks. I will
present experimental results on both simulated and HapMap SNP data, and
compare our method with existing ones on haplotype inference, recombination
hotspot prediction, and structural map estimation.


About the speaker:
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 a Sloan Research Fellowship in Computer Science.
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