Button to scroll to the top of the page.

News

From the College of Natural Sciences

Aaron Dubrow is the science and technology writer for the Texas Advanced Computing Center. He previously was a public affairs specialist at the U.S. National Science Foundation, where he was responsible for communications and media relations for NSF's Computing and Information Science and Engineering (CISE) Directorate, as well as creating and implementing strategic communications plans in support of the agency's $1 billion computing research portfolio. Aaron has extensive experience writing and developing print, video, multimedia and social media content for scientific organizations and publications. He contributes regularly to the Huffington Post and Futurity and is a leading voice in explaining the value of high performance computing and modern data platforms for science, society and industry to lay audiences.

Rethinking Brain-Inspired Computing from the Atom Up

Rethinking Brain-Inspired Computing from the Atom Up

If you wanted to deliver a package across the street and avoid being hit by a car, you could program a powerful computer to do it, equipped with sensors and hardware capable of running multiple differential equations to track the movement and speed of each car. But a young child would be capable of doing the same task with little effort, says Alex Demkov, professor of physics at The University of Texas at Austin.

UT Austin Launches Institute to Harness the Data Revolution

UT Austin Launches Institute to Harness the Data Revolution

Research from UT Austin professors and TRIPODS members Alex Dimakis and Eric Price shows that it is possible to learn a deep generative model that dreams images of human faces (right panel), trained by observing only occluded images (left panel). The middle panel shows a previous approach for solving this problem, that fails. [Figure from: AmbientGAN: Generative models from lossy measurements, by A. Bora, E. Price and A.G. Dimakis, ICLR 2018.]

Advances in machine learning are announced every day, but efforts to fundamentally rethink the core algorithms of AI are rare.

Twisted Physics: Magic Angle Graphene Produces Switchable Superconductivity

Twisted Physics: Magic Angle Graphene Produces Switchable Superconductivity

When the two layers of bilayer graphene are twisted relative to each other by 1.1 degrees -- dubbed the "magic angle" -- electrons behave in a strange and extraordinary way. The effect was first theorized by UT Austin physics professor Allan MacDonald and postdoctoral researcher Rafi Bistritzer. Illustration credit: David Steadman/University of Texas at Austin.

Last year, scientists demonstrated that twisted bilayer graphene — a material made of two atom-thin sheets of carbon with a slight twist — can exhibit alternating superconducting and insulating regions. Now, a new study in the journal Nature by scientists from Spain, the U.S., China and Japan shows that superconductivity can be turned on or off with a small voltage change, increasing its usefulness for electronic devices.