Date: 2013.5.8(Wed.) 16:00
Venue: Wooribyul Seminar room.
Dept. of EE(E3-2)
Speaker: Professor Yeonseung Chung/Department of Mathematical Sciences/KAIST
Title: Introduction to Nonparameteric Bayesian Statistical Modeling
Abstract
:
In
this talk, a brief introduction to Nonparameteric (NP) Bayesian statistical
modeling will be given. Following the description of some key components of
Bayesian statistical modeling, the lecture starts with some complicated
statistical modeling problems for which parametric modeling may have
limitations and moves to NP Bayes methodology for the complex modeling. Focuses
will be on NP Bayes approaches involving Gaussian process (GP), Dirichlet
process (DP), extensions of DP, Beta process, and Indian Buffet process. If
time permits, computation-based inference procedure focusing on Markov Chain
Monte Carlo (MCMC) will also be discussed. The lecture will be concluded with a
summary and some discussions of future research directions.
Bio:
ACADEMIC TRAINING
B.S. in Statistics, Korea University, Korea, 2000
M.S. in Biostatistics,
University of North Carolina, Chapel Hill, USA, 2005
Ph.D. in
Biostatistics, University of North Carolina, Chapel Hill, USA, 2009
Dissertation
: Nonparamertric Bayes inference for conditional distribution modeling
Advisor : Prof. David Dunson
ACADEMIC APPOINTMENTS
Assistant Professor, Dept. of Mathematical Sciences, KAIST, Korea,
January, 2011 - present
Research
Associate, Dept. of Biostatistics, Harvard School of Public Health, USA, 2010 -
2011
Post-doctoral
Research Fellow, Dept. of Biostatistics, Harvard School of Public Health, USA,
2009 - 2010
Pre-doctoral
Research Fellow, Division of Biostatistics, NIEHS, NIH, USA, 2007-2009
Graduate
Industry Trainee, GlaxoSmithKline, Durham, USA, 2005-2007
Graduate Research Assistant, Lineberger Comprehensive Cancer
Center, UNC-CH, USA. 2003-2005