Core Viewpoint - The article highlights the achievement of Su Weijie, a Chinese statistician, who won the COPSS Presidents' Award in 2026, marking the first time in 14 years that a Chinese scholar has received this prestigious award [1][6]. Group 1: Award Significance - The COPSS Presidents' Award is considered the highest honor in international statistics and data science, equivalent to the Fields Medal in mathematics, awarded annually to a statistician under 40 years old [4]. - The award is jointly evaluated by five leading statistical societies, recognizing outstanding contributions to statistical theory, methods, or applications [4]. Group 2: Su Weijie's Contributions - Su Weijie established a rigorous statistical foundation for various applications of large language models and made significant advancements in privacy-preserving data analysis, notably applied in the 2020 U.S. Census [2]. - He designed the peer review mechanism for top AI conferences, which was officially implemented at ICML 2026, and conducted foundational research in convex optimization and deep learning's mathematical theory [2]. Group 3: Academic Background and Achievements - Su Weijie graduated from Stanford University in 2016 without a postdoctoral phase and joined the Wharton School at the University of Pennsylvania as a faculty member [9][14]. - He has received numerous prestigious awards, including the first Stanford Anderson Doctoral Dissertation Award and the NSF CAREER Award, among others [10]. Group 4: Research Focus - His research is characterized by rigorous mathematical derivations, focusing on deep learning generalization, trustworthy alignment of large models, privacy-preserving computation, and the design of academic review mechanisms [16]. - Su Weijie is recognized as a leading figure in mathematics within the artificial intelligence field, having chaired the Scientific Integrity Committee for ICML, overseeing the review process for over 24,000 papers [17]. Group 5: Key Research Frameworks - He developed the Gaussian Differential Privacy (GDP) framework, which optimally balances privacy protection and model accuracy, significantly enhancing the application value of census data in economic and sociological research [21]. - Su Weijie's isotonic mechanism for academic review transforms the relationship between authors and reviewers into a cooperative one, addressing the challenges of increased submission rates and declining review quality [23].