Intelligent Systems

In Seattle, “the big one” — a massive earthquake that could devastate the region — represents an ominous threat. So widespread are the concerns that city leaders there created standards to fortify new skyscrapers using data from studies forecasting the impact of a big earthquake in the region.

Researchers have developed a system of computer programs called ADCIRC that use mathematical models of physical forces to simulate and predict the storm surge an incoming storm will produce based on the official hurricane forecast tracks. It has successfully forecast storm surge for the past 25 years. "We've added more and more physics, better numerical algorithms, better software, better use of high-performance computing resources, and it just continues to improve to the present day," said Clint Dawson, professor of aerospace engineering and engineering mechanics at The University of Texas at Austin and one of ADCIRC's lead developers.

The National Science Foundation has selected The University of Texas at Austin to lead the NSF AI Institute for Foundations of Machine Learning, bolstering the university’s existing strengths in this emerging field. Machine learning is the technology that drives AI systems, enabling them to acquire knowledge and make predictions in complex environments. This technology has the potential to transform everything from transportation to entertainment to health care.

People living in some of the largest U.S. cities and their surrounding areas face the highest risk of contracting COVID-19 in the near future, according to a new set of online dashboards created by researchers in the Cockrell School of Engineering at The University of Texas at Austin.

Founded by Mohit Tiwari, associate professor in the Cockrell School of Engineering's Department of Electrical and Computer Engineering, Symmetry Systems launched this month one year after raising $3 million in seed funding. The company's flagship solution, DataGuard, helps small teams of security engineers to protect data across a large organization.

When UT operations research and industrial engineering graduate student Kyoung Kim approached his professors in 2017 with an idea to use logistics to help with disaster planning, he had no idea the biggest disaster in a century would, in a few months, ravage the Texas coast.

The contagious nature of COVID-19 puts medical personnel at risk of contracting the virus from the patients they treat. A startup co-founded by University of Texas at Austin engineering professor Andrea Thomaz just landed $10 million in investment to ramp up production of medical robots that could prove a valuable tool in helping doctors and nurses treat patients amid the novel coronavirus outbreak.

After graduating from UT Austin in 2010 with a master’s degree in electrical and computer engineering, Sowmiya Narayanan went on to co-found Lily AI — an AI-based software designed to enhance users’ online shopping experiences — and she now serves as its chief technology officer.

Malware is evasive, intelligent and sneaky. No sooner than anti-virus software is updated to combat the latest attacks, a computer virus will have already evolved into something harder to detect and potentially more damaging to a computer system. But malware isn’t without vulnerabilities. Engineers at The University of Texas at Austin have found an additional line of defense to detect threats posed by malware that doesn’t rely on the detection and protection provided by existing anti-virus software programs.

Bringing together experts in areas ranging from robotics to space, the Cockrell School of Engineering will host the U.S. Army’s annual Mad Scientist Conference on April 24-25 in the school’s Engineering Education and Research Center. The two-day event explored the individual and convergent impacts of technological innovations on the future of military operations, from present day through 2050.

In a new interdisciplinary study, a team of engineers led by The University of Texas at Austin have used machine learning technology in tandem with next-generation RNA sequencing to reveal the inner workings of cells and how they respond to environmental stress, particularly by focusing on RNAs, to provide new insights into biology at the molecular level.

When walking in a crowded place, humans typically aren’t thinking about how we avoid bumping into one another. We are built to use a gamut of complex skill sets required to execute these types of seemingly simple motions.

Behind every successful spacecraft mission is a team of experts running thousands of calculations to accurately predict what could go wrong and avoid costly mistakes. For this level of mathematical prediction, NASA is turning to Brandon Jones, an uncertainty quantification researcher and aerospace engineering professor in the Cockrell School of Engineering.

Dana Sellers (B.S. ChE 1975) is passionate about using data to improve patient care. In the early 1980s, she noticed that major hospital departments couldn’t afford the expensive computer systems for health care data collection. So, she partnered with a fellow Longhorn and embarked on her first entrepreneurial venture to develop health care applications for personal computers, setting the course for her successful career.

Earlier this year, mechanical engineering professor Delbert Tesar, director of the Robotics Research Group and the Carol Cockrell Curran Chair in Engineering at The University of Texas at Austin, hosted key members of industry and multiple U.S. Army engineers and lab personnel for a two-day workshop aimed at enhancing Army battlefield technology and keeping our servicemen and servicewomen safer.

Where Longhorns once played basketball, robots now play soccer.

Wang, a pioneer in the integration of data, has begun analyzing driving behavior by interconnecting three different data sources — a driving simulator; a standalone engine, or powertrain; and a fully equipped autonomous vehicle prototype designed by UT researchers.

More so than any other generation, Gen Z students are eager to get out of the classroom and learn in the real world.

Uber is betting that one day — however soon or far away that is — we all will be ride-sharing in the skies, and the company has enlisted UT engineers to help.

Tom Truskett and his team believe there is a better way to bring products to market sooner, and it starts with machine learning.