Pneumonia has emerged as a life-threatening complication of COVID-19, accounting for nearly half of all patients who have died from the novel coronavirus in the U.S. since the beginning of the pandemic. Even before the onset of the COVID-19 pandemic, pneumonia was responsible for more than 43,000 deaths in 2019.
IBM recently announced the awardees of this year’s IBM Ph.D. Fellowship Program. WNCG student Tianlong Chen was among only 16 students selected worldwide for 2021.
WNCG professor Aryan Mokhtari has received a grant from the National Science Foundation (NSF) to study Computationally Efficient Second-Order Optimization Algorithms for Large-Scale Learning. The project “lays out an agenda to develop a class of memory efficient, computationally affordable, and distributed friendly second-order methods for solving modern machine learning problems.”
The National Science Foundation has selected The University of Texas at Austin to lead the NSF AI Institute for Foundations of Machine Learning, bolstering the university’s existing strengths in this emerging field. Machine learning is the technology that drives AI systems, enabling them to acquire knowledge and make predictions in complex environments. This technology has the potential to transform everything from transportation to entertainment to health care.
Advances in machine learning are announced every day, but efforts to fundamentally rethink the core algorithms of AI are rare.
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 Engineering Education and Research Center at The University of Texas was abuzz with over 200 participants gathered for Texas Wireless Summit (TWS) on November 6. This year’s theme was “AI and the Mobile Device.”
Held annually by WNCG, TWS brings together leading figures in industry, academia, and government to discuss the latest developments in information systems technology. “AI and the Mobile Device” marked the 16th summit hosted by the group.
We present two examples of using machine learning to improve end-user quality of experience (QoE) in cellular networks operating today. In particular, we demonstrate how to automate the clearing of operational faults in outdoor networks and compensation of signal impairments in indoor networks for voice-over-LTE (VoLTE) applications. Our proposed methods are compatible with 3GPP LTE Release 8 and higher.
Texas ECE Professors Andreas Gerstlauer and Lizy K. John together with collaborators at the University of California, Riverside have been awarded a $1M grant by the National Science Foundation (NSF) to study application of machine learning techniques for performance and power prediction in early design stages of future computer systems.
The project is described below: