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.

The Multifaceted Complexity of Machine Learning

Modern machine learning methods have demonstrated an unprecedented potential to solve challenging problems in many areas. However, foundational understanding regarding how and when certain methods are adequate to use and most effective in solving tasks of interest is still emerging. A central question at the heart of this endeavor is to understand the different facets of the complexity of machine learning tasks.

Confronting Climate Change

The workshop will bring together leaders in mathematics, statistics, and atmospheric sciences to confront grand climate challenges and their impacts. A major goal of the program will be to develop next-generation suites of science-driven mathematical and statistical tools and capabilities to address decision-relevant climate hazards and impacts.

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.

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.

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.

Topological Data Analysis

In this age of rapidly increasing access to ever larger data sets, it has become clear that studying the “shape” of data using the tools of combinatorial and algebraic topology can lead to much deeper insights than other standard methods when analyzing complex data sets. Topological data analysis (TDA) is the exciting and highly active new field of research that encompasses these productive developments at the interface of algebraic topology, statistics, and data science.

Dealing with COVID-19 in Theory and Practice

The extraordinary impact of COVID-19 requires equally extraordinary measures, which is the focus of this workshop. Four major themes will be represented: Public Health Challenges, The Role of Data Science, Measuring and Managing Economic Impact.