Modern explorations in science, technology and medicine increasingly demand complex stochastic models. Computational and theoretical advances are needed in order to formulate, analyze, apply and interpret these models. Recent years have witnessed a remarkable interplay between computation and probability. On the one hand, probabilistic techniques have led to powerful computational methods such as Markov chain Monte Carlo algorithms, while on the other hand the calculation of probabilistic quantities such as modes and marginals of high-dimensional distributions and the analysis of data from random samples has posed several computational challenges.
The Fall 2012 Semester on "Computational Challenges in Probability" aims to bring together leading experts and young researchers who are advancing the use of probabilistic and computational methods to study complex models in a variety of fields. The goal is to identify common challenges, exchange existing tools, reveal new application areas and forge new collaborative efforts. The semester includes three intensive weeklong workshops - Bayesian Nonparametrics, Uncertainty Quantification, and Monte Carlo Methods in the Physical and Biological Sciences. In addition, synergistic activities will be planned throughout the duration of the semester. In particular, there will be several short courses and plenary invited talks by experts on related topics such as graphical models, randomized algorithms and stochastic networks, regular weekly seminars and relevant film screenings.