Research

12/17/2025 - Digital modeling is one of the most widely used tools for bringing bodies to life in 3D. Created from thousands of everyday images and videos, 3D generative models employ artificial intelligence to help us understand the structure of animals and humans. These models are essential for a wide range of real-world applications, including biological research and surgical planning. Existing generative models, however, have limitations as they rely on training data that consists of fixed, typical skeletal structures—and nature is anything but typical. 

07/29/2025 - Big news from UTCS: With renewed funding from the National Science Foundation, IFML researchers are expanding their work to make AI more accurate, reliable, and ready for the real world. This critical research will strengthen how AI systems function — and accelerate progress in science, technology, and workforce innovation.

07/23/2025 - Daehyeok Kim, an assistant professor in the Department of Computer Science at The University of Texas at Austin, has received a 2025 National Science Foundation (NSF) CAREER Award for his project “Toward Programmable NICs as Multi‑Tenant Cloud Resources.” The award provides $622,738 over five years to su

05/20/2025 - Researchers at The University of Texas at Austin and Cognizant AI Lab have developed an AI-driven system that leverages 175 years of global land use and carbon storage data to generate optimal environmental policy strategies. The AI model, backed by Project Resilience and AI for Good, balances complex trade-offs to maximize carbon storage, minimize economic disruption, and improve everyday life.

03/28/2025 - UT Austin’s Scott Aaronson and his former postdoctoral researcher, Shih-Han Hung, played a key role in demonstrating the first practical use of quantum computing: generating and verifying truly random numbers.

03/18/2025 - UT Austin and Tec de Monterrey have established the OriGen Health Research Center to leverage Latin America’s largest biobank and AI-driven analysis for advancing precision medicine. The collaboration includes faculty from UT’s College of Natural Sciences, Dell Medical School, and College of Pharmacy, with researchers applying machine learning and data science to genetic and clinical data. This work will enhance understanding of chronic diseases affecting Latino populations and develop targeted, data-driven healthcare solutions.

02/19/2025 - UT Austin is celebrating 100 years of quantum science, highlighting its impact on computing, clean energy, and medicine. Faculty member Scott Aaronson is advancing quantum computing by developing methods to certify quantum-generated randomness for cryptography and data privacy. With the Texas Quantum Institute and leading research initiatives, UT continues to shape the future of quantum technology.

02/12/2025 - In a recent interview with TechGuide.org, UT Austin Computer Science Professor Peter Stone shares insights on how undergraduates can secure research opportunities in AI. As the founder of the Learning Agents Research Group and Director of Texas Robotics, he emphasizes the value of interdisciplinary skills, hands-on research, and continuous learning. Listen to the full conversation to explore AI career paths, research strategies, and actionable steps for breaking into the field.

02/07/2025 - UT Austin researchers have improved their AI-powered brain decoder, allowing it to translate thoughts into continuous text with just one hour of training—far less than the original 16-hour process. This advancement makes the technology more accessible, particularly for individuals with aphasia, by enabling communication without requiring spoken language comprehension. The team is now collaborating with experts in aphasia research to explore its potential for real-world applications.

01/31/2025 - Dr. Adam Klivans, UT Computer Science professor and director of the Institute for Foundations of Machine Learning, joined KXAN Austin to discuss the impact of DeepSeek’s latest AI model. He explained how the Chinese company’s breakthrough in training efficiency—achieving high-performance results with significantly less computational power—has surprised the AI industry and affected major tech stocks. Klivans also highlighted ongoing questions about DeepSeek’s methods and the broader implications for AI development.