Apple Awards Seed Funding to Two School of Data Science Faculty

Two University of Virginia School of Data Science faculty members have received seed funding from Apple to support their research labs and continued work in machine learning.

Assistant Professor of Data Science Tom Hartvigsen received $40,000 in seed funding through Apple’s Machine Learning for Health Workshop. The unrestricted gift will support Hartvigsen’s lab and its research at the intersection of machine learning and health.

Assistant Professor of Data Science Nur Yildirim received $13,300 through the Apple Workshop on Human-Centered Machine Learning. The Apple Faculty Research Award on Human-Centered Machine Learning will provide general support for Yildirim’s lab and research exploring how artificial intelligence and machine learning systems can be designed around human needs, experiences, and values.

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Tom Hartvigsen and Nur Yildirim
Tom Hartvigsen and Nur Yildirim received a total of $53,300 in seed funding through Apple's Machine Learning for Health Workshop.

Together, the awards represent $53,300 in Apple support for machine learning research at the School of Data Science.

“Seed funding like this gives us the flexibility to explore promising ideas early, when there are often more questions than answers,” Hartvigsen said. “Apple’s support will help us continue investigating how machine learning can be used responsibly to address important challenges in health while creating opportunities for students to contribute to that work.”

Hartvigsen’s research group develops machine learning and natural language processing methods designed to enable the responsible deployment of models in changing real-world environments, particularly in healthcare. Working with clinical collaborators, the group applies these methods to areas including anesthesia and cancer care, with research published in leading venues across machine learning, natural language processing, and medicine. The Apple funding will support student research and help disseminate findings from the group’s work.

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