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Texas Science Stories that Wowed Us in 2021

Texas Science Stories that Wowed Us in 2021

While for many 2021 may have felt like it lasted a few years, it was in fact just 12 months—and University of Texas at Austin scientists and researchers managed to pack a ton of new discoveries into that time. From the furthest reaches of the cosmos to the depths of the ocean and from the tiniest microbes to the most massive black holes, research in Texas Science covered a lot of ground, as researchers pushed boundaries, answered big questions and offered solutions to the world's problems. Here are 16 examples of how UT Austin scientists, mathematicians and technologists used 2021 to usher in new knowledge and innovations to help change the world.

An Algorithm for EMS Response

An Algorithm for EMS Response

David Kulpanowski has an important job. As an IT business systems analyst with Austin-Travis County EMS, he's responsible for tracking ambulance response times in the City of Austin and then conducting simulation models to see how they can be improved.

Getting ambulances where they need to be and fast enough is a life-or-death matter.

As AI Becomes Ubiquitous, There are Risks, Says New AI100 Report

As AI Becomes Ubiquitous, There are Risks, Says New AI100 Report

Artificial intelligence has reached a critical turning point in its evolution, according to a new report by an international panel of experts assessing the state of the field for the second time in five years.

UT Austin Climbs in Latest National Undergraduate Rankings

UT Austin Climbs in Latest National Undergraduate Rankings

The University of Texas at Austin rose to No. 38 among national universities in U.S. News & World Report's latest undergraduate rankings, climbing four spots since last year.

Ethical Artificial Intelligence is Focus of New Robotics Program

Ethical Artificial Intelligence is Focus of New Robotics Program

Ethics will be at the forefront of robotics education thanks to a new University of Texas at Austin program that will train tomorrow's technologists to understand the positive — and potentially negative — implications of their creations.

Three Natural Sciences Faculty Receive NSF CAREER Awards

Three Natural Sciences Faculty Receive NSF CAREER Awards

Three faculty members from the College of Natural Sciences have received distinguished Faculty Early Career Development (CAREER) Awards from the National Science Foundation.

Kristen Grauman Named Finalist in 2021 Blavatnik National Awards for Young Scientists

Kristen Grauman Named Finalist in 2021 Blavatnik National Awards for Young Scientists

University of Texas at Austin computer science researcher Kristen Grauman was selected as a finalist for the 2021 Blavatnik National Awards for Young Scientists.

Xue-Xin Wei Asks Basic Questions about the Nature of Intelligence

Xue-Xin Wei Asks Basic Questions about the Nature of Intelligence

Image by Vivian Abagiu.

Xue-Xin Wei, a computational and theoretical neuroscientist, recently joined the Department of Neuroscience as an assistant professor. Wei grew up in Qingdao, China, before obtaining his undergraduate degree in mathematics at Peking University. His lab works at the intersection of computational/theoretical neuroscience, statistics, artificial intelligence and deep learning. He and his team work closely with experimental scientists to test predictions of computational models to form theory-experiment loops.

New Study Shows How Deep-learning Technology Can Improve Brain Imaging

New Study Shows How Deep-learning Technology Can Improve Brain Imaging

Compare these two images of a slice of brain tissue from a rat. The PSSR method applies deep learning to a low resolution image from a scanning electron microscope (left) to yield a higher resolution image (right).

Neuroscience researchers often face challenges when using high-powered microscopes to capture clear images of brain tissue. Microscopes suffer from what researchers call the "eternal triangle of compromise" — image resolution, the intensity of the illumination the sample is subjected to, and speed compete with each other. For example, taking an image of the sample very quickly can result in a dark image, but subjecting a biological sample to more intense light can damage it.

UT Austin Professors Named ACM Fellows by the Association for Computing Machinery

UT Austin Professors Named ACM Fellows by the Association for Computing Machinery

The Association for Computing Machinery, the primary professional organization in the field of computer science, has named two University of Texas at Austin professors — Peter Stone and Lizy Kurian John — as ACM Fellows. The award goes only to highly distinguished computer scientists representing the top 1% of ACM members.