The Monte Carlo method was initially developed for numerical integrations in statistical physics problems during the early days of electronic computing (1945-55). The past two decades have witnessed a strong surge of interest from the scientific community in developing novel Monte Carlo-based integration and optimization tools. As a result, the Monte Carlo method, especially Markov chain Monte Carlo, has played a critical role in diverse fields such as physics, statistics, computer science, bioinformatics, and structural biology. A few years ago, engineering researchers and statisticians began to realize the strong adaptivity and power of Monte Carlo methods for signal filtering and prediction. Besides using the popular Markov chain Monte Carlo strategies, they have also experimented with various sequential Monte Carlo strategies, resulting in an array of novel and effective nonlinear filtering algorithms and optimization tools.

This workshop is intended to provide a forum for the presentation of recent developments in the efficient design, theoretical analysis, and novel application of the Monte Carlo method, with an emphasis on their relevance to bioinformatics, engineering, and statistics. We hope to bring together probabilists, statisticians, engineers, computational biologists, and, most importantly, interested graduate students, to share the exciting developments, to foster new ideas, and to stimulate the exchange of information between specialists in various areas.

The topics of the Workshop include theoretical analyses of MCMC, methods for estimating normalizing constants (e.g., Bayes factors), particle filters and mixture Kalman filters, sequential Monte Carlo optimization methods, applications in bioinformatics, target tracking problem, and financial applications.

Poster presentation from participants are welcomed. Each accepted poster has a space for fifteen 8.5' by 11' pages to display. Women, minorities, and graduate students are strongly encouraged to participate. There are limited funding (up to $300) available for each participant with an accepted poster abstract submission.

Organizing Committee Chair:
Jun Liu, Professor of Statistics, Harvard University

Organizing Committee:
Rong Chen, Professor of Information and Decision Sciences, University of Illinois at Chicago,
Xiaodong Wang, Assistant Professor of Electrical Engineering, Columbia University,
Steve Qin, Postdoc fellow, Department of Statistics, Harvard University,
Junni Zhang, Doctor Candidate, Department of Statistics, Harvard University.

Sponsorship:

Partially supported by