Machine Learning

Over the past decade, the world has seen tremendous increases in the deployment of artificial intelligence (AI) technology. The main horsepower behind the success of AI systems is provided by deep learning models and machine learning (ML) algorithms. Recently, a new AI paradigm has emerged: Automated Machine Learning (AutoML) including its subfield Neural Architecture Search (NAS). State-of-the-art ML models consist of complex workflows with numerous design choices and variables that must be tuned for optimal performance.

WNCG professor Jon Tamir aims to leverage machine learning techniques to make brain scans faster and more informative. He received an Amazon Machine Learning Research Award in 2020 from Amazon Web Services (AWS) to support the work.

A recent article on Amazon Science explored the details of Prof. Tamir's research on MRI scans.

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 Atlas Wang received a Data Science Research Award from Adobe for his work on “Towards Automated Design of Efficient Deep Multi-Modal Recommendation Models.”

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 16^th summit hosted by the group.