Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

The process of testing new solar cell technologies has traditionally been slow and costly, requiring multiple steps. Led by a fifth-year PhD student, a Johns Hopkins team has developed a machine ...

The partners in Lantern, a machine learning software firm focused on product distribution, pitched the technology to HVACR ...

Robust, deployable and collaborative machine learning (ML) methods are needed for AI to become truly useful. This ERC-funded research aims to solve a major ML bottleneck and will form a cornerstone of ...

Opinions expressed by Digital Journal contributors are their own. “In a world driven by data, my mission is to create innovative AI solutions that not only solve complex problems but also push the ...

Join us to learn about how to use cutting edge GPU infrastructure to solve real world material discovery problems with AI and unsupervised machine learning. Our lab in the Department of Materials ...

In recent years, JupyterLab has rapidly become the tool of choice for data scientists, machine learning (ML) practitioners, and analysts worldwide. This powerful, web-based integrated development ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...

What if the skills you choose to learn today could determine your career trajectory in 2025? The field of machine learning is evolving at a breakneck pace, and with it comes a growing demand for ...

Neel Somani has built a career that sits at the intersection of theory and practice. His work spans formal methods, mac ...