News

As co-organizer of a new NSF-sponsored white paper, Soledad Villar joined researchers across the U.S. to outline how AI can advance mathematics, physics, chemistry, astronomy, and more.

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

Johns Hopkins scientists develop method to render Earth-based images as clear as those taken from space.

The award recognized his innovative work on long-step gradient descent and its implications for modern optimization theory.

Johns Hopkins  mathematician develops a practical way to incorporate the well-known Samuelson effect into the pricing of commodity derivatives.

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.

Catalyst award recognizes promising early-career faculty advancing innovative research.

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.