原标题：时隔14年，再次花落中国！ 北京时间2月6日，国际统计学会会长委员会官网宣布：2026年考普斯会长奖（COPSS Presidents' Award）授予中国学者苏炜杰，以表彰其在人工智能大模型统计理论、隐私数据保护分析、改进机器学 习同行评审、凸优化加速算法理论以及深度学习数学理论与高维统计推断等方面的工作。 这是中国学者时隔14年再次斩获这一殊荣！考普斯会长奖由国际数理统计学会、美国统计学会、国际生 物统计学会以及加拿大统计学会联合颁发，每年在全球范围内评选一位40岁及以下、在统计与数据科学 领域作出原创性、奠基性贡献并产生深远影响的学者，其学术地位通常被类比为统计学界的"诺贝尔 奖"、基础数学领域的"菲尔兹"奖。 苏炜杰在接受科技日报记者采访时表示，人工智能时代，统计学正从传统的数据分析工具，演变为支撑 人工智能系统可信性、可解释性与安全性的核心理论基础。无论是对大模型不确定性的定量刻画、算法 偏差与公平性评估，还是数据隐私与安全保护，统计学都发挥着重要作用。"此次获奖，是对统计与数 据科学在人工智能时代基础性地位的进一步肯定。"苏炜杰说。 来源：科技日报 编辑：王 丹 校对：秦璐敏 审核：张晓 ...

科技日报记者 姜靖 北京时间2月6日，国际统计学会会长委员会官网宣布：2026年考普斯会长奖（COPSS Presidents'Award）授予中国学者苏炜杰，以表彰其在人工智能大模型统计理论、隐私数据保护分析、改 进机器学习同行评审、凸优化加速算法理论以及深度学习数学理论与高维统计推断等方面的工作。 来源：科技日报 在此之前，苏炜杰曾获斯隆研究奖、工业与应用数学学会数据科学青年奖等国际奖项。2025年，他当选 国际数理统计学会会士，受邀作国际数理统计学会Medallion讲座，其学术贡献已在统计、数学与机器 学习领域获得广泛而持续的国际认可。 苏炜杰2011年毕业于北京大学数学科学学院数学专业，2016年从斯坦福大学获得统计学博士学位，随后 进入宾夕法尼亚大学任教。近年来，北京大学数学科学学院2007级被广泛称为"黄金二代"，在基础数学 领域涌现出王虹、邓煜、唐云清等一批在国际学界产生重要影响的学者。苏炜杰此次荣获考普斯会长 奖，被视为北大数院2007级"黄金二代"在应用数学与统计方向补上的重要一块拼图。 （受访者供图） 这是中国学者时隔14年再次斩获这一殊荣！考普斯会长奖由国际数理统计学会、美国统计学会、 ...

Group 1 - Michael I. Jordan is recognized as a pioneer in machine learning and has recently been elected as a foreign academician of the Chinese Academy of Sciences [1][17] - He has extensive experience in both academia and industry, having collaborated with companies like Ant Group and Amazon, and has been involved in founding multiple companies [1][24] - Jordan emphasizes the importance of curiosity and applying mathematical thinking to solve real-world problems [1][2][27] Group 2 - Jordan's work spans various fields, including decision-making, knowledge exchange, and data prediction in real-world scenarios, which he believes does not require extraordinary talent but rather dedication and hard work [3][18] - He highlights the close relationship between machine learning and statistics, stating that learning involves making predictions based on statistical methods [7][22] - Jordan has engaged in significant collaborations in China, particularly in meteorology, where he worked on predicting severe weather events using machine learning [8][23] Group 3 - He has been involved in the development of tools that are currently used by Chinese meteorological departments, showcasing the practical applications of his research [8][23] - Jordan believes there is no disconnect between industry development and academic research, as many advancements in technology stem from academic findings [10][25] - He notes that while China has made strides in open-source initiatives, there is still a need to focus on creativity and problem-solving skills rather than just academic metrics [11][27]

Core Insights - The article emphasizes the importance of focusing on the critical few rather than the trivial many to achieve extraordinary results, suggesting that success often stems from strategic adjustments rather than sheer effort [2][10][22] Group 1: Statistical Distributions - Normal distribution is commonly used to summarize data, but it often leads to misconceptions about the nature of success and outcomes [3][5][6] - Pareto distribution, or the 80/20 rule, illustrates that a small percentage of actions often leads to the majority of results, highlighting the need to concentrate resources on the most impactful areas [7][10][12] Group 2: Practical Applications - Personalized marketing efforts yield better results than mass marketing, as targeted communications resonate more with potential customers [11][12] - Investing in relationships with key individuals can provide greater returns than broad advertising strategies, emphasizing the value of quality over quantity in networking [13][18] Group 3: Customer Focus - Companies should prioritize their most valuable customers, as they contribute significantly to growth and success, rather than spreading resources thinly across all clients [14][15][16] - The concept of having a few deep, meaningful relationships is more beneficial than having numerous superficial connections [18][21] Group 4: Value of Unconventional Thinking - The article argues that true value lies in areas often overlooked by others, where small, intelligent actions can yield disproportionately large returns [22][23][25] - The perception of "luck" is framed as the result of being prepared and strategically positioned to seize opportunities when they arise [25][26]

Core Viewpoint - The book "Ladies' Tea: How Statistics Transforms Science and Life" by David Salsburg explores the evolution of statistics from its methodological origins to a systematic discipline, using the "Ladies' Tea" experiment as a narrative anchor [7][8]. Group 1: Historical Context of Statistics - The "Ladies' Tea" story originates from Ronald Fisher's 1935 work "Experimental Design," where a group of Cambridge scientists tested a lady's claim about the taste difference in tea preparation methods [8]. - The 19th century saw a dominant philosophical view of a "mechanical universe," where scientists believed that reality could be precisely described by a few mathematical formulas [8][9]. Group 2: Key Figures and Theories - Karl Pearson, in the late 19th century, proposed that experimental results should be viewed as distributions of numbers rather than precise measurements, leading to the development of statistical models [9]. - William Sealy Gosset, under the pseudonym "Student," focused on small sample sizes and introduced the widely used "t-test" [9][11]. - Fisher's work at the Agricultural Experiment Station led to the development of original data analysis tools, including "analysis of variance" and "randomized control" methods [11]. Group 3: Evolution of Statistical Methods - The 20th century saw the rise of hypothesis testing, with Fisher introducing the concept of the p-value as a measure of significance, although its interpretation remains debated [14]. - Non-parametric methods emerged as alternatives to traditional parametric methods, allowing for analysis without assuming a specific distribution [13]. Group 4: Philosophical Implications - The book raises philosophical questions about the application of statistical models in decision-making and the understanding of probability in real life [15]. - Despite the challenges and limitations of statistical methods, the discipline has become integral to various fields, including medicine and public policy [16][17].

Core Viewpoint - The article emphasizes the growing importance of statistics in the era of artificial intelligence, highlighting its applications across various fields such as business, medicine, engineering, and social sciences, while also addressing the challenges faced by AI technologies [1][2]. Group 1: Importance of Statistics - Statistics is not only used for government purposes but is also crucial in commercial, medical, engineering, and social science applications, focusing on data collection, analysis, and inference [1]. - The third National Conference on Statistics and Data Science featured over 600 academic reports, with nearly 25% related to machine learning and artificial intelligence [1]. Group 2: Challenges in AI - The "2024 Artificial Intelligence Development Report" by the China Academy of Information and Communications Technology identifies challenges such as insufficient interpretability of algorithms, security vulnerabilities, and irregular data labeling in AI [1]. - Current AI applications tend to prioritize algorithm functionality over understanding underlying mechanisms, particularly in high-stakes fields like medicine and construction, where stability and reliability are critical [1]. Group 3: Talent Development in Statistics - There is a growing emphasis on the cultivation of statistical talent in academia, as the demand for professionals in statistics and data science exceeds supply [2]. - The need for educational institutions to enhance their training capabilities is highlighted, aiming to produce more statisticians and data analysts while encouraging some to remain in academia [2]. Group 4: International Collaboration and Growth - Since the establishment of statistics as a primary discipline in 2011, there has been rapid development in statistical research in China, with Chinese authors now accounting for the second-largest share of publications in top international statistical journals [2]. - The conference attracted over 1,800 scholars, with 15% from abroad, indicating a growing international collaboration in the field [2].