معرفی
Yuekai Sun is an associate professor of Statistics and Director of the Master's Program in Data Science at the University of Michigan, part of the College of Literature, Science, and the Arts. He holds a BA in Computational and Applied Mathematics from Rice University (2010) and a PhD in Computational Mathematical Engineering from Stanford University (2015). His research focuses on enhancing the safety and reliability of AI systems, particularly in AI alignment, algorithmic fairness, and transfer learning. Sun's work addresses challenges such as bias mitigation, model evaluation, and ethical AI deployment. He has contributed to advancements in LLM benchmarking, weak supervision frameworks, and spatiotemporal predictive modeling. His recent publications span topics like optimal intervention in spatial networks, fair performative policy learning, and scaling laws for LLM performance. Sun's research bridges theoretical statistics and applied machine learning, with a strong emphasis on real-world societal impact.
His academic leadership includes overseeing the Data Science Master's program, fostering interdisciplinary education. His grants and collaborations focus on foundational AI research and its ethical implications. Sun's lab at the University of Michigan explores cutting-edge solutions for trustworthy AI systems, leveraging statistical rigor and computational innovation.
