News
Read the latest news from the College of Natural Sciences at The University of Texas at Austin
McDonald Observatory
Galaxies Actively Forming in Early Universe Caught Feeding on Cold Gas
James Webb Space Telescope images reveal three galaxies that may be actively forming when the universe was only 400 to 600 million years old.

Transitioning Gender Identities Is Not Linked With Depression
A landmark longitudinal study of LGBTQ+ youths has found that transitioning gender identities is not associated with depression.

Whole Communities — Whole Health
Faculty Collaborations Awarded 'Fast Track to Impact' Support
Human Development and Family Sciences faculty are leading research in a campus-wide Bridging Barriers initiative.

Department of Computer Science
Computer Scientist Honored for Teaching Excellence
Professor Calvin Lin has won the prestigious Minnie Stevens Piper Teaching Award.

NSF Awards Graduate Research Fellowships and Honors to Natural Sciences Students
Dozens of College of Natural Science students received recognition through the National Science Foundation program.

Artificial Intelligence Trained to Draw Inspiration From Images, Not Copy Them
Researchers are using corrupted data to help generative AI models avoid the misuse of images under copyright.

Otters, Especially Females, Use Tools To Survive a Changing World
A new study has found that individual sea otters that use tools — most of whom are female — are able to eat larger prey...

To Optimize Guide-Dog Robots, First Listen to the Visually Impaired
Guide-dog users and trainers can provide insight into features that make robotic helpers useful in the real world.

Texas Undergraduate College
Computer Science and Mathematics Dual Major Receives Goldwater Scholarship
Luisa Mao of The University of Texas at Austin is among a select group of undergraduates to be awarded the prestigious Goldwater Scholarship.

Defense Research Advancement
Computer Science Professor Trains AI Through Game Theory
Computer scientists Ryan Farrel and Chandrajit Bajaj will test a novel reinforcement learning approach.
