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.

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.”

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.

A paper published by students and faculty from the Chandra Family Department of Electrical Engineering in collaboration with Meta has received an Outstanding Paper Honorable Mention at MLSys 2025, the premier conference on machine learning and systems.

Prof. Nina Telang of Texas ECE has been selected to join the prestigious Provost's Distinguished Leadership Service Academy (DLSA) at The University of Texas at Austin.

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.