Title :

Bayesian Inference for Wavelet-based Modeling of Functional Data

Speaker :

Prof. Marina Vannucci

Department of Statistics

Rice University, USA

Venue :

Room 215, William M. W. Mong Engineering Building, CUHK

Date :

Dec 23, 2010, Thursday
2:30 PM - 3:30 PM

Abstract :

In this talk I will describe methodologies for Bayesian modelling of functional data that incorporate feature extraction. Practical applications will be classification problems that involve functional predictors. Wavelet methods will be used for dimension reduction. Probit models and Bayesian methods will allow the simultaneous classification of the samples as well as the selection of the discriminating features of the data. Applications will involve spectral data. In mass spectrometry, for example, the identification of peaks related to a specific outcome, i.e. peaks that discriminate samples or that predict a clinical response, is of interest. Other practical contexts will come from chemometrics studies that explore the possibility of using NIR spectra to classify samples. In all examples we will find that very small sets of features lead to good classification results.

Biography :

Dr. Vannucci is currently a Professor in the Department of Statistics at Rice University, Houston, TX. She received the Laurea (B.S.) degree in Mathematics in 1992 and the Ph.D. degree in Statistics in 1996, both from the University of Florence, Italy. Prior to joining Rice in 2007, Dr. Vannucci was Research Fellow at the University of Kent at Canterbury, UK, during 1996-1998. In 1998 she joined the Department of Statistics at Texas A&M University, TX, as Assistant Professor, became Associate Professor in 2003 and Full Professor in 2005. Dr. Vannucci was visiting scholar at Stanford University, CA, during Summer and Fall of 2001, and at Columbia University, NY, during Fall of 2004. While at Texas A&M she served as program coordinator and mentor for a Training Program in Bioinformatics and as co-director for the Biostatistics and Bioinformatics Facility Core of the NIEHS Center for Environmental and Rural Health. She is currently an adjunct faculty member of the UT M.D. Anderson Cancer Center, TX, and the Rice Director of the Interinstitutional Graduate Program in Biostatistics.

Dr. Vannucci`s research focuses on the theory and practice of Bayesian variable selection techniques and on the development of wavelet-based statistical models and their application. Her work is often motivated by real problems that need to be addressed with suitable statistical methods. Methodologies developed by Dr. Vannucci for variable selection have found applications in chemometrics and, more recently, in bioinformatics. Her work on the development of Bayesian wavelet-based models for functional data was the first contribution to the use of wavelet methods with multiple curves. Recent work of Dr. Vannucci has focused on structural bioinformatics and, in particular, on the important problem of protein structure prediction. Dr. Vannucci has published over 70 research papers, she has co-edited the book "Bayesian Inference for Gene Expression and Proteomics" and has delivered more than 90 invited talks. Dr. Vannucci was the recipient of an NSF CAREER award in 2001 and won the Mitchell prize from the International Society for Bayesian Analysis in 2003. She is an elected Fellow of the American Statistical Association (ASA), since 2006, and of the Institute of Mathematical Statistics (IMS), since 2007, and an elected Member of the International Statistical Institute (ISI), since 2009.

    **************************************** ALL ARE WELCOME ****************************************

Enquiries: Ms. Winnie Wong or Prof. Wen J. Li, Department of Mechanical and Automation Engineering, CUHK at 2609 8337. *MAE Series (2010-11) is contained in the World-Wide Web home page at http://www3.mae.cuhk.edu.hk/maeseminars.php#mae.