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.

Kun Woo Cho is an assistant professor and Fellow of the Jack Kilby/Texas Instruments Endowed Faculty Fellowship in Computer Engineering in the Chandra Family Department of Electrical and Computer Engineering at The University of Texas at Austin.
She earned her Ph.D. from Princeton University in 2024, her M.A. from Princeton University in 2021, and her B.S. from the University at Buffalo, SUNY in 2018. After graduating, she worked as a postdoctoral associate at Rice University for one year.
Dr. Cho's research spans wireless communication and radar systems, metamaterial-based analog computing, and AI-integrated systems, with an emphasis on mmWave hardware design, fabrication, and prototyping, embedded systems, signal processing, and full-stack system integration.

She is a recipient of the 2026 Heidelberg Laureate Forum Young Researcher, 2025 Siebel Scholarship, 2025 N2Women Rising Star, 2025 ACM MobiSys Rising Star, 2024 EECS Rising Star from MIT, 2023 Princeton SEAS Excellence Award, and the Best Paper Award from ACM MobiHoc 2023.  

Guandao Yang is an assistant professor and Fellow of the Advanced Micro Devices (AMD) Chair in Computer Engineering in the Chandra Family Department of Electrical and Computer Engineering at The University of Texas at Austin, where he directs the Spatial Physical Intelligence Lab. Prior to joining UT Austin, he was a Senior Research Scientist at Apple. Before that, he spent two years as a Postdoctoral Scholar at Stanford University, working with Leonidas Guibas and Gordon Wetzstein. He received his Ph.D. in Computer Science in 2023 from Cornell Tech, where he was advised by Serge Belongie and Bharath Hariharan. He obtained his bachelor's degree in Mathematics and Computer Science from Cornell University in 2018.

Guandao's research lies at the intersection of machine learning, computer vision, and computer graphics. He studies how to build data-efficient artificial intelligence systems that can operate in the spatial and physical world, with downstream applications in data-limited domains such as robotics, engineering design, and scientific discovery. His work has received support from various funding sources including Google, NVIDIA, Intel, Samsung, LVMH, Magic Leap, and the Army Research Laboratory.