Researchers in the Chandra Family Department of Electrical and Computer Engineering at The University of Texas at Austin have developed a chip-based motion sensor that offers both high sensitivity and a wide measurement range.

Dance carries knowledge about how people live — information about the dancer’s landscapes, communities and histories. However, AI systems that analyze human movement are trained almost entirely on everyday actions like walking, sports and gestures, recorded in Western labs with no information about the geographic or cultural origins of the movement

Prof. David Z. Pan was named the recipient of the UCLA Samueli Professional Achievement in Academia Award this month.

Geo Tu earned his B.S. in electrical engineering from Texas ECE in 2008. He went on to earn an M.S. in electrical engineering from the University of Southern California in 2010. Now an architect at Lightmatter, Tu is helping develop technology designed to address one of computing’s most pressing challenges: moving enormous volumes of data quickly and efficiently.

Texas ECE assistant professor Neeraja Yadwadkar has been selected to receive a 2026 Google ML and Systems Junior Faculty Award in recognition of the significance and promise of her work in Cloud ML Systems.

Texas ECE is welcoming two new faculty to the department this Fall. Kun Woo Cho and Guandao Yang are joining the department as assistant professors.

Researchers advanced a promising yet challenging light-based method for imaging large, complex tissue samples.

Texas ECE alumnus Sanmi Koyejo has been named one of the TIME Magazine Top 100 People in AI. Sanmi received his Ph.D. under Prof. Joydeep Ghosh in 2013. He is the co-founder of Virtue AI.

Texas Engineers teamed up with the world’s largest semiconductor foundry to fabricate and test an emerging memory technology that could help deal with the increasing energy demand of artificial intelligence.

Together with Taiwan Semiconductor Manufacturing Company (TSMC), researchers tested SOT-MRAM, a type of memory that can retain information even

John, a professor of electrical and computer engineering at the University of Texas at Austin, has spent the past five years working on a class of models called weightless neural networks.