Organizing Committee
Abstract

Monte Carlo methods are one of the main tools used to study the properties of complex physical, chemical and biological systems. Since their introduction in the late 1940s, these methods have undergone a remarkable expansion and are now used in many other fields, including statistical inference, engineering, and computer science. However, the design and theoretical understanding of Monte Carlo methods is still a challenging topic, especially for those problems where rare events play the key role in determining algorithm performance. The aim of the workshop is to bring together specialists in the application areas who understand the specific challenges posed by realistic problems and have developed sophisticated tools to tackle these problems, and mathematicians developing methods for algorithm analysis, abstraction, and optimization.

Image courtesy of Eric Vanden-Eijnden

Confirmed Speakers & Participants

  • Speaker
  • Poster Presenter
  • Attendee

Workshop Schedule

Wednesday, October 31, 2012
TimeEventLocationMaterials
8:30 - 8:55Registration  
8:55 - 9:00Welcome - Jill Pipher, Director, ICERM  
9:00 - 10:20Accelerated Molecular Dynamics Methods - Arthur F. Voter, Los Alamos National Laboratory11th Floor Lecture Hall
10:20 - 10:40Coffee/Tea Break11th Floor Collaborative Space 
10:40 - 11:30Extension of the string method for saddle points search - Weiqing Ren, National University of Singapore and IHPC11th Floor Lecture Hall
11:30 - 12:20Computational Approaches to Investigate Ligand Migration Pathways in Proteins - Markus Meuwly, Universitat Basel11th Floor Lecture Hall
12:15 - 2:00Break for Lunch and Free Time  
2:00 - 2:50Adaptive Kinetic Monte Carlo for Long Time Simulations and Global Optimization - Hannes Jonsson, University of Iceland (Haskoli Islands)11th Floor Lecture Hall
2:50 - 3:20Coffee/Tea Break11th Floor Collaborative Space 
3:20 - 4:30Challenges in Path Integral Monte Carlo - David Ceperley, University of Illinois Urbana-Champaign11th Floor Lecture Hall
7:00 - 8:30Poster Session and Dessert Reception 11th Floor Lecture Hall and Collaborative Space 
Thursday, November 1, 2012
TimeEventLocationMaterials
9:00 - 9:50Ferromagnets and the mean-field classical Heisenberg model - Kay Kirkpatrick, University of Illinois at Urbana-Champaign11th Floor Lecture Hall
10:00 - 10:30Coffee/Tea Break11th Floor Collaborative Space 
10:30 - 11:20Concentration inequalities for Feynman-Kac particle models and their applications - Pierre Del Moral, INRIA11th Floor Lecture Hall
11:20 - 12:10Sign and Phase Problems in Markov Chain Monte Carlo- Sampling the Unsampleable - James E. Gubernatis, Los Alamos National Laboratory11th Floor Lecture Hall
12:10 - 2:00Break for Lunch and Free Time  
2:00 - 2:50Metastability and Monte Carlo Methods for Multiscale Problems - Konstantinos Spiliopoulos, Boston University11th Floor Lecture Hall
2:50 - 2:55Group Photo11th Floor Lecture Hall 
2:55 - 3:35Coffee/Tea Break11th Floor Collaborative Space 
3:30 - 4:20Overcoming the rare-event sampling problem in biological systems with infinite swapping - Nuria Plattner, Universitat Basel11th Floor Lecture Hall
4:30 - 6:00Workshop Reception  
Friday, November 2, 2012
TimeEventLocationMaterials
9:00 - 9:50Antithetic Thermostat for Fast Computing Machines - Cristian Predescu, D. E. Shaw Research11th Floor Lecture Hall
10:00 - 10:30Coffee/Tea Break11th Floor Collaborative Space 
10:30 - 11:20Metastability and coarse-graining of stochastic systems - Jianfeng Lu, Duke University11th Floor Lecture Hall
11:20 - 12:10Bayesian inference of three-dimensional chromosomal organization from Hi-C data - Jun Liu, Harvard University11th Floor Lecture Hall
12:20 - 5:00Afternoon for Collaborations11th Floor Lecture Hall 
3:00 - 3:30Coffee/Tea Break11th Floor Collaborative Space  

Associated Semester Workshops

Computational Challenges in Probability
Computational Challenges in Probability Image
Bayesian Nonparametrics
Bayesian Nonparametrics Image
Uncertainty Quantification
Uncertainty Quantification Image
Performance Analysis of Monte Carlo Methods
Performance Analysis of Monte Carlo Methods Image