Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, ...

Overview: Algorithm selection is an engineering decision: the wrong choice can freeze a system at scale, regardless of ...

This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been ...

The massive datasets that power machine learning algorithms and systems are complex, noisy, and vulnerable to various kinds of errors, contamination, and adversarial corruptions. As data science and ...

Overview: An algorithm is a step-by-step set of instructions that takes an input and produces a clear output, just like a ...

This course offers an introduction to mathematical nonlinear optimization with applications in data science. The theoretical foundation and the fundamental algorithms for nonlinear optimization are ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

Can we ever really trust algorithms to make decisions for us? Previous research has proved these programs can reinforce society’s harmful biases, but the problems go beyond that. A new study shows how ...

Accelerating catalyst discovery and development is of paramount importance in addressing the global energy, sustainability and healthcare demands. The past decade has witnessed significant momentum in ...

The algorithms that underlie modern artificial-intelligence (AI) systems need lots of data on which to train. Much of that data comes from the open web which, unfortunately, makes the AIs susceptible ...