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Two USU Faculty Are Shaping the Future of Artificial Intelligence in Utah

As artificial intelligence reshapes industry and society, two Utah State University professors are being recognized for leading decades of groundbreaking AI research.

Utah State University

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Sep 2, 2026 at 4:40 PM UTC · Updated il y a 15 heures · 1 min de lecture

Two USU Faculty Are Shaping the Future of Artificial Intelligence in Utah
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Key Signal

40+ years AI research experience

Last Updated

il y a 15 heures

As artificial intelligence reshapes industry and society, two Utah State University professors are being recognized for leading decades of groundbreaking AI research.

Heng-Da Cheng, a professor of computer science, and Kevin Moon, associate professor of math and statistics and director of USU’s Data Science and Artificial Intelligence Center (DSAI), were both selected to be featured in the AI Utah 100 2026 Yearbook.

The award highlights AI experts leading meaningful work in product development, research, and infrastructure.

For over 40 years, Cheng has researched AI and its applications and has produced more than 140 journal papers, 200 conference papers, and 15 books and book chapters.

“I am honored and grateful,” Cheng said. “I can exchange ideas with people from different fields, explore the theory and practice aspects of AI, and I can further help the students.”

As his work continues, his team is integrating classical AI and deep learning to improve system performance, develop new directions in AI, and apply it to various fields.

Moon said the award honors the contributions he and his group have made in AI and his leadership at DSAI.

“I'm grateful for the recognition this brings to the research I've been doing with USU students and the broader work in AI that's happening at USU through the DSAI Center,” Moon said.

The pair were honored Thursday at an event in Sandy celebrating AI innovators, community partners, and supporters of Utah’s AI ecosystem.

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Originally reported by Utah State University

NewsLayer coverage based on externally reported material.

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