Machine Learning

Wireless E-Tattoo for Pneumonia Aims to Transform Patient Monitoring

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.

Tianlong Chen Selected for IBM Fellowship

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.

UT Austin Selected as Home of National AI Institute Focused on Machine Learning

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.

Recap: WNCG Hosts 16th Annual Texas Wireless Summit

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.

Machine Learning to Manage Cellular Network Faults and Improve Voice-Over-LTE Service

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.

Profs. Gerstlauer and John Receive Collaborative NSF Grant on Predictive Modeling for Next-Generation Heterogeneous Computer System Design

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:

Pages

Subscribe to RSS - Machine Learning