News

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.

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.

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

Innovation could accelerate research into how HIV affects memory and thinking.

Johns Hopkins researchers introduce new method to solve complex problems with minimal data, enhancing efficiency across industries.

Now leading research at Pink AI, student discovered the power of mathematics as an undergraduate.

New approach could help powerful AI models create realistic content using less energy.

Johns Hopkins team develops a dimension-agnostic method for machine learning.

In a Q&A session, new assistant professor Haoyang Cao shares her expertise in utilizing machine learning to tackle high-dimensional stochastic games involving large populations.

Johns Hopkins team develops a way to personalize antiretroviral therapy to reduce side effects.