News

WNCG professor Alex Dimakis of has been invited to serve as a member of the AI Commission of the US Chamber of Commerce. The Commission on Artificial Intelligence Competitiveness, Inclusion, and Innovation (AI Commission) will research and recommend artificial intelligence policies as it relates to regulation, international research and development competitiveness, and future jobs. The AI Commission will convene field hearings in key locations around the U.S. and internationally to explore these critical issues.

A new approach could lower computing costs and increase accessibility to state-of-the-art natural language processing. WNCG student Tianlong Chen is the lead author of a study in artificial intelligence that posits that hidden within massive neural networks, leaner subnetworks exist that can complete the same task more efficiently. The study is co-authored by WNCG assistant professor Zhangyang "Atlas" Wang, along with Jonathan Frankle of MIT CSAIL, and Shiyu Chang, Sijia Liu, and Yang Zhang, all of the MIT-IBM Watson AI Lab.

Text was considered relatively safe from adversarial attacks, because, whereas a malicious agent can make minute adjustments to an image or waveform of sound, it can’t alter a word by, say, 1%. But Prof. Alex Dimakis of Texas ECE and his collaborators have investigated a potential threat to text-comprehension AIs.  The research was led by UT student Qi Lei and collaborators at IBM Research and Amazon. The study was published in SysML 2019 and covered by Nature News. 

WNCG student Debarati Kundu and her advisor, Prof. Brian Evans, have been selected for a top 10% paper award for the IEEE International Conference on Image Processing 2015. The conference will take place in Quebec City, Canada in September. The paper "Full-Reference Visual Quality Assessment for Synthetic Images: A Subjective Study" deals with conducting a series of subjective experiments to aid in better understanding how humans perceive synthetic images encountered in computer graphics.

Modern communication systems rely on multiple antennas that enhance the performance of network links using a series of techniques known as Multiple Input Multiple Output (MIMO). However, new technology is needed to meet the demands of a rapidly increasing number of wireless devices and enable the next generation of cellular systems. Known as Massive MIMO, this adaptation of traditional MIMO techniques presents challenges to research and development teams worldwide.