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:
Texas ECE alumna Jette Henderson, who completed her PhD in August, received the Best Student Paper award for the paper "PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization" at the KDD MLMH Workshop on Machine Learning for Healthcare in London in August. Jette worked under the supervision of Texas ECE professor Joydeep Ghosh. The paper uses a special kind of tensor factorization that is guided by supporting evidence from PubMed, a huge repository of medical literature, to extract meaningful insights from electronic health records.
Picture your typical hospital scene: Patients being admitted at the front desk, doctors performing consultations, nurses administering medicine … and robots wandering the hallways toward the supply closet?
Robots in the storeroom may not be the norm quite yet, but it’s happening in Austin thanks to WNCG professor Andrea Thomaz and her company, Diligent Robotics.