Computer Science

Lek-Heng Lim uses tools from algebra, geometry and topology to answer questions in machine learning.

Large language models still struggle with basic reasoning tasks. Two new papers that apply machine learning to math provide a blueprint for how that could change.

Is it possible to fill space “cubically” with shapes that act like spheres? A proof at the intersection of geometry and theoretical computer science says yes.

As The Joy of Why podcast returns for a second season, producer Polly Stryker and host Steven Strogatz invite listeners to join them and their brilliant new guests on another voyage of discovery.

“Liquid” neural nets, based on a worm’s nervous system, can transform their underlying algorithms on the fly, giving them unprecedented speed and adaptability.

A wave of research improves reinforcement learning algorithms by pre-training them as if they were human.

In Shang-Hua Teng’s work, theoretical and practical questions have long been intertwined. Now he’s turning his focus to the impractical.

Researchers can now find the shortest route through a network nearly as fast as theoretically possible, even when some steps can cancel out others.

Random circuit sampling, a popular technique for showing the power of quantum computers, doesn’t scale up if errors go unchecked.