Texas ECE assistant professors Shwetadwip Chowdhury and Hyeji Kim have been selected to receive a Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF).

NSF IFML is part of the first cohort of AI Institutes announced in 2020. Led by The University of Texas at Austin, the new award will build on the trajectory of the past five years and develop new foundational tools to advance generative AI.

The University of Texas at Austin chapter of the IEEE Robotics and Automation Society (IEEE RAS) recently won the Vex AI World Championship in Houston, Texas.

Ph.D. student Wenyan Cong and others won the Best Paper Award at at the AI for Content Creation (AI4CC) Workshop, held at the Computer Vision and Pattern Recognition Conference 2025 (CVPR), one of the top conferences in computer vision. The award was for her work on “VideoLifter: Lifting Videos to 3D with Fast Hierarchical Stereo Alignment.”

Four Texas ECE undergraduate students have won Third Place in the Student Design Competition at IEEE International Microwave Symposium 2025 (IMS), the premier symposium in the microwave field. Mihir Chauduri, Carlos Rodriguez, Edgar Rodriguez, and John Yi won for their work on the Switched Acoustic Filter Module.

Did you know it’s possible to control a robotic arm or a wheelchair with just your thoughts, through a device called a brain-computer interface (BCI)? But for many users, learning to operate these systems is slow, difficult and, in some cases, unattainable. José del R. Millán has discovered a novel way to accelerate this learning process: a gentle electrical nudge to the spine before BCI training.

Published by Cockrell School of Engineering

Published May 29 in Device, a new study introduces a wireless forehead e-tattoo that decodes brainwaves to measure mental strain without bulky headgear. This technology may help track the mental workload of workers like air traffic controllers, surgeons, truck drivers and more.

Over the past few years, Texas ECE has seen more students gravitating toward hardware-centric courses such as computer architecture, integrated circuits, and semiconductor design, rather than the traditionally popular software engineering track.

A multi-university team with heavy involvement from industry leaders is working to change that. The team, led by researchers from The University of Texas at Austin, plans to infuse artificial intelligence into the design process for RFICs to reduce the difficulty of making these important chips.

Editor's Note: This story was originally published by Cell Press.

Molecules like DNA can store large amounts of data without requiring an energy source, but accessing this molecular data is expensive and time consuming. Researchers from The University of Texas at Austin have developed an alternative method to encode information in synthetic