Verification, Validation, and Uncertainty Quantification Across Disciplines

With the advent of terascale, petascale and beyond computational capabilities, the reach of computational sciences is rapidly broadening well beyond its traditional ‘homes’ of physics, chemistry and computational engineering sciences to the biological and social sciences. To the extent to which such modeling and simulation are meant to be predictive in nature – and to the extent to which the systems being simulated are complex in nature – obvious questions regarding the veracity of the computational results must be inevitably confronted.

Decision Making in Health and Medical Care

One of the most challenging sets of decisions facing individuals and institutions today involves personal health and medical care. Breakthroughs in biomedical science and engineering have delivered life-saving therapies that were impossible just a few years ago, but at a cost of a million dollars per dose in some cases. The decision-making processes for balancing medical needs with economic incentives and the complexity of the healthcare system are fraught with social, ethical, and political dimensions that most stakeholders are not equipped to address.

Quantum Information for Mathematics, Economics, and Statistics

This workshop focuses on the practical and theoretical challenges in the emerging area of quantum information and computing, which seeks to make effective use of the information embedded in the state of a quantum system, and promises to solve previously intractable computational problems and revolutionize simulation.

Eliciting Structure in Genomics Data

August 30-September 3, 2021

Methods for dimension reduction play a critical role in a wide variety of genomic applications. Indeed, as technology develops, and datasets grow in both size and complexity, the need for effective dimension reduction methods that help visualize and distill the primary structures remains as essential as ever. The development and provision of effective methods for dimension reduction involves connecting a series of areas of expertise: from theory to algorithms, implementations and applications.

Opening Workshop: An introduction to the area of distributed solutions

This conference will consist of three series of lectures, the aim of which is to present the main issues at stake in the analysis of distributed solutions to complex societal problems and to describe some mathematical tools to handle these questions. Applications range from collective behavior in economy and finance to crowd analysis and the spread of diseases, and from machine learning to stochastic optimization and artificial intelligence.

Mean-field approaches in Machine Learning and Statistics

The aim of this workshop is to gather specialists from machine learning and statistics to applied probability and analysis who share a common interest in mean-field models. Potential applications range from mean-field games to stochastic algorithms and simulations, neural networks and frequentist or Bayesian statistical inference for interacting systems.