Uncertainty Quantification and Reasoning for Reliable AI at Brigham Young University

September 22, 2025||inEvents|tagsbrigham young university, reliable ai, uncertainty quantification, uncertainty quantification and reasoning for reliable ai, xujiang zhao

Xujiang Zhao, a researcher in our Data Science & System Security department will present the “Uncertainty Quantification and Reasoning for Reliable AI” seminar on advancing trustworthy AI at the Talmage Math Sciences/Computer Building, TMCB 1170, Brigham Young University, on Thursday, Sept 25th at 11am. As AI systems play a greater role in critical decisions, understanding how to measure and reason about uncertainty is essential.

Xujiang Zhao BYU

Xujiang will share how advanced statistical modeling and reasoning frameworks can make AI more robust, transparent, and reliable in real-world applications from healthcare to autonomous systems. Don’t miss this opportunity to engage with cutting-edge research and learn how uncertainty quantification is shaping the next generation of responsible AI.

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Uncertainty Quantification and Reasoning for Reliable AI Seminar at Brigham Young University

Our researcher Xujiang Zhao will present “Uncertainty Quantification and Reasoning for Reliable AI” at Brigham Young University on Thursday, Sept. 25 at 11 a.m. in TMCB 1170. The seminar explores how statistical modeling and reasoning frameworks can strengthen trustworthy AI, making systems more

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