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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. According to IBM, the Fellowship Awards Program is “intensely competitive.” Awardees are exceptional Ph.D. students conducting research in promising and disruptive technologies, particularly: Hybrid Cloud; Quantum Computing / Quantum Systems; Artificial Intelligence; Cloud / Open Source Technologies; Security / Cyber Security; Data Science; and Systems.

This news feature is part of Dell Med's Voices, a series of profiles that highlight the people of Dell Med as they work to improve health with a unique focus on our community. How can a computer help someone regain motor function? José del R. Millán, Ph.D., a professor in Texas ECE and the Dell Medical School Department of Neurology, tackles this question as he designs brain-computer interfaces that empower people to surpass their limits. Q&A WHAT’S THE PROBLEM YOU’RE TRYING TO SOLVE, AND HOW DID YOU COME TO RECOGNIZE IT?

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.” Every year, Adobe funds a university faculty research program to “promote the understanding and use of data science in the area of marketing with the goal “to encourage both theoretical and empirical development of solutions to problems in marketing.”

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.

WNCG continues to grow; this spring, Assistant Professor Jonathan Tamir and Professor José del R. Millán were welcomed to the group. [[{"type":"media","view_mode":"media_preview","fid":"1761","attributes":{"alt":"","class":"media-image","height":"180","width":"180"}}]] Dr. Jonathan Tamir holds a joint appointment with the Department of Electrical and Computer Engineering and Dell Medical School’s Department of Diagnostic Medicine. He is also affiliated with the Oden Institute for Computational Engineering and Sciences.

Advances in machine learning are announced every day, but efforts to fundamentally rethink the core algorithms of AI are rare.