Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has ...

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

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

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 ...

Phishing websites remain a persistent cybersecurity threat, exploiting users by imitating trusted online services. New ...

The question of whether prehospital emergency anaesthesia and intubation improves survival in patients with major trauma has ...

New forms of fentanyl are created every day. For law enforcement, that poses a challenge: How do you identify a chemical you've never seen before? Researchers at Lawrence Livermore National Laboratory ...

High-entropy alloys are promising advanced materials for demanding applications, but discovering useful compositions is difficult and expensive due to the vast number of possible element combinations.

RIT researchers publish a paper in Nature Scientific Reports on a new tree-based machine learning algorithm used to predict chaos.