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Yifan Jiang, a PhD student in the Chandra Family Department of Electrical and Computer Engineering at The University of Texas at Austin, has been named a recipient of a 2023 Apple Scholars in AI/ML PhD fellowship. The Apple Scholars in AI/ML PhD fellowship recognizes the contributions of researchers in computer science and engineering at the graduate and postgraduate level. Apple Scholars in AI/ML are selected based on their innovative research, record as thought leaders and collaborators, and commitment to advancing their respective fields.

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.

Significant advances in artificial intelligence over the past decade have relied upon extensive training of algorithms using massive, open-source databases. But when such datasets are used “off label” and applied in unintended ways, the results are subject to machine learning bias that compromises the integrity of the AI algorithm, according to a new study by researchers at The University of Texas at Austin and the University of California, Berkeley.

WNCG professor Atlas Wang has received several grants for his work on artificial intelligence. 

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.