News

Johns Hopkins researchers unlock hidden patterns to help AI recognize images with far less training data.

Johns Hopkins team uses computer simulations to find the optimal level of disorder for light-manipulating devices.

In this Q&A, a new assistant professor discusses the mathematical foundations of AI and his plans for collaboration at Johns Hopkins.

In this Q&A, incoming assistant professor shares how she became interested in PDEs and what excites her about joining Johns Hopkins.

New research ditches trial-and-error for faster, sharper, more trustworthy models.

In this Q&A, Nikhil Rammohan ’09 shares how a foundation in statistics led him to impactful cancer research.

An engineering student’s project explores the math behind FM synthesis to uncover what makes its tones unique. 

An engineering student’s project shows how localized energy networks can deliver cheaper, cleaner power to remote communities.

Johns Hopkins mathematicians introduce ‘multiplayer federated learning,” a new framework that allows independent entities to optimize their own goals while reaching good outcomes.

In this Q&A, Professor Edward Scheinerman discusses his newly published book, which focuses on the foundational nature of real numbers and their complexities.

Adrian Severino and the Miraheart Team leverage light and simulation to revolutionize infant heart monitoring.

Researchers use advanced models to uncover critical thresholds in global ocean circulation, highlighting potential climate impacts.