A research team led by Professor Radu Marculescu and PhD student Md Mostafijur Rahman in the Chandra Family Department of Electrical and Computer Engineering have made great strides in the medical imaging community with a string of influential papers that dramatically improve the efficiency and accuracy of AI-powered image segmentation.

Professor Nina Telang and students BP Rimal and Sneha Ballabh designed and implemented an AI-driven virtual tutor that creates personalized learning experiences for first-year engineering students in EE 306, an introductory computing course with nearly 200 students.

Cockrell School of Engineering faculty and alumni are playing critical roles in a startup that aims to use artificial intelligence to produce narrative short-form videos using libraries of existing, real-world footage.

The four-year, $2.4 million grant from the National Institute of Diabetes and Digestive and Kidney Diseases, supports the researchers’ plan to develop a system to detect detailed information on eating motions, potentially every bite and chew.

UT is launching the Center for Generative AI, powered by a new GPU computing cluster, among the largest in academia. The cluster will comprise 600 NVIDIA H100s GPUs — short for graphics processing units, specialized devices to enable rapid mathematical computations, making them ideal for training AI models.

The findings, published this week in the Proceedings of the National Academy of Sciences, highlight the problems that arise when data published for one task are used to train algorithms for a different one. Jonathan Tamir, assistant professor of electrical and computer engineering in the Cockrell School of Engineering and a member of the UT-led National Science Foundation AI Institute for the Foundations of Machine Learning, is one of the study’s co-authors.