Workshops

Learning low-dimensional models: Nonlinear model reduction via lifting transformations and proper orthogonal decomposition

Low-rank tensor methods for simulation, optimization and uncertainty quantification of parametric PDEs

Streaming Algorithms for Halo Finders in Cosmology

Alternatives to IEEE: NextGen Number Formats for Scientific Computing

Three Frontiers of Lossy Compression for Scientific Data

No equations, no variables, no parameters, no space, no time: Data and the modeling of complex systems

Accurate and Scalable Processing of Big Data in Earth Observation

Cognitive Discovery: Pushing the Frontiers of Technical R&D with AI.

Computing Challenges at the Large Hadron Collider (LHC)

Retrieving atomic structure from transmission electron microscopy data: a family of inverse problems

Combining computing and learning for physics

Scalability and Fault Tolerance for Exascale Simulations of Hot Fusion Plasmas

Multilevel-Multifidelity Sampling and Emulation for Forward UQ

Learning Sparse High-Dimensional Governing Equations from Limited Data

Sparsity-enforced regularizations for optimal learning of high-dimensional systems from random data

Scalable kernel methods

Scalable algorithms for optimal experimental design for large-scale Bayesian inverse problems governed by complex models

Domain Knowledge in Data Analysis: A Geometrical Method for Low-Dimensional Representations of Simulations

Communication-Avoiding Algorithms for Linear Algebra, Machine Learning, and Beyond

Memory- and Data-Centric Abstractions for Management and Optimisation of Data Movement

How FAIR are data repositories in materials science?

When More Data Do Not Provide A Better Description

Efficient Adaptive Hybrid Kinetic Simulation: Computing the Signal in the Noise

Enabling Reproducibility in Computational and Data-enabled Science

Learning and Geometry for Stochastic Dynamical Systems in high dimensions