News
UT Austin Selected for EcoCAR EV Challenge to Make Next-Gen Vehicle
The University of Texas at Austin is one of 13 university teams in North America set to participate in the EcoCAR EV Challenge, a prestigious collegiate competition to re-engineer state-of-the-art vehicles and prepare students for the future.
Texas ECE Researchers Awarded Top Pick in Hardware and Embedded Security 2021
A team of researchers from Texas ECE were awarded a Top Pick in Hardware and Embedded Security 2021 by the IEEE Hardware Security and Trust Technical Committee (HSTTC) for their work on "Horizontal Side-Channel Vulnerabilities of Post-Quantum Key Exchange Protocols."
Xiuling Li Awarded 2022 IEEE Pioneer Award in Nanotechnology
Xiuling Li of Texas ECE has been named the recipient of the 2022 IEEE Nanotechnology Council Pioneer Award “for innovative contributions to nanoscale device growth, fabrication, and demonstration especially nanowire epitaxy, metal-assisted chemical etching, and self-rolled-up nanomembrane technology."
PhD Student Alexander Ware Awarded NDSEG Fellowship
Texas ECE PhD student Alexander Ware has been awarded an National Defense Science and Engineering Graduate (NDSEG) Fellowship from the Department of Defense for 2022.
Researchers are Developing Analytics to Combat Dementia Using Mobile Sensor Data
Edison Thomaz of Texas ECE and fellow researchers from The University of Texas at Austin have received a 4-year R01 grant award from the National Institute of Health (NIH) to study "Digital Biomarkers and Analytics for Cognitive Impairment with Mobile and Wearable Sensing."
Researchers Develop New Method to Predict and Optimize Performance of Deep Learning Models
Researchers at the University of Texas Austin have developed a new method to design NAS models and predict their success and accuracy that eliminates the high costs and delays associated with their training.
Alumna Wei Ye Receives 2022 EDAA Outstanding Dissertation Award
Texas ECE alumna Wei Ye has been selected to receive the 2022 EDAA Outstanding Dissertation Award by the European Design and Automation Association (EDAA) for her dissertation "Design for Manufacturability and Reliability through Learning and Optimization."
‘Off Label’ Use of Imaging Databases Could Lead to Bias in AI Algorithms
The findings, published this week in the Proceedings of the National Academy of Sciences, highlight the problems that arise when data published for one task are used to train algorithms for a different one. Jonathan Tamir, assistant professor of electrical and computer engineering in the Cockrell School of Engineering and a member of the UT-led National Science Foundation AI Institute for the Foundations of Machine Learning, is one of the study’s co-authors.
