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

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.

Texas Engineers have demonstrated a technique to trick light into behaving as if it was interacting with atomically thin metal films, setting the stage for the design and development of next-generation optoelectronic devices such as emitters, detectors and nano-sensors that could be used in health care, pollution detection, telecommunications and more.

Researchers developed an innovative new technique that allows accurate space and time reconstruction of optically scattering samples that undergo simple movements while being imaged.

Associate professor Zhangyang “Atlas” Wang and ph.D. student Peihao Wang have received a DARPA Disruptive Idea Paper Award at the International Conference on Neuro-symbolic Systems (NeuS), 2025.