On May 21, Chen Songxi, an academician of the Chinese Academy of Sciences and a professor from Guanghua School of Management, Peking University, was invited to the Beijing Digital Economy Forum in CUEB. He delivered a fascinating report titled Data, Data Analysis, and Data Empowerment. Among the notable attendees were Wang Wenju, Secretary of CUEB Party Committee; President Wu Weixing, Deputy Party Secretary; Yin Zhichao and Li Kunpeng, Party Committee members and Vice Presidents. The report was hosted by Wu Weixing.

Chen Songxi, a distinguished alumnus of CUEB, began by reflecting on his unforgettable learning and working experiences at Beijing College of Economics, the predecessor of CUEB. He encouraged all to be pioneers in the new era, regardless of their research field. Focusing on the theme of the report, Chen highlighted that the key to data empowerment lies in data analysis. He stressed the importance of utilizing statistics and skills to evaluate data quality, extract useful information, and unleash potential productivity, thereby boosting data’s value. He described data analysis as algorithm optimization, and noted that integrating statistical analysis and deep learning can facilitate AI’s efficient and sustainable development, creating a higher quality “Greener AI”. Chen emphasized the importance of fostering a strong data culture for developing a digital China. He advocated for the orderly opening and sharing of public data based on data risk categories, and the concerted efforts to develop high-quality reanalyzed datasets. Only by doing so can China take the lead in mastering technological resources and get across its data science narrative. Additionally, he proposed that the current shortage of data analysis personnel could be addressed by strengthening the construction of first-class disciplines in statistics and improving the foundational capabilities for training data analysts in China, thus laying a solid foundation for the personnel needed to build a digital China.

In his closing remarks, Wu Weixing expressed sincere gratitude to Chen Songxi for delivering such an insightful report to the faculty and students. Wu noted that Chen’s academic achievements and unique insights enabled him to vividly illustrate the crucial role of statistical analysis in driving the green and sustainable development of AI in an accessible manner. As a leading scholar in his field and a member of the National Committee of the Chinese People’s Political Consultative Conference, Chen integrated his expertise with China’s development, showing great concern for the long-term development of a digital China and particularly for the cultivation of data analysts. This sense of responsibility and dedication fully embodies his patriotic feelings.

During the Q&A session, Chen humorously and patiently answered questions from the faculty and students about data mining and the opening of public data. His thorough and detailed responses greatly benefited everyone in the audience.

The Beijing Digital Economy Forum, established by CUEB, is a new initiative to implement the guiding principle of Digital China set forth by the 20th National Congress of the CPC, and to promote the development of interdisciplinary platforms for the digital economy. Two major academic reports have been held in Zhuoyu Auditorium, which accommodates over 700 people. Featuring renowned domestic academicians as speakers, both reports have been well-attended and highly admired by faculty and students.

Chen Songxi is an academician of the Chinese Academy of Sciences and an internationally renowned statistician. He holds the position of chair professor at Peking University and is a member of the American Association for the Advancement of Science, a fellow of the Institute of Mathematical Statistics, a fellow of the American Statistical Association, and an elected member of the International Statistical Institute. Chen has served as an editor for several top international journals, including the Journal of the American Statistical Association, Annals of Statistics, Journal of Business & Economic Statistics, and Environmetrics. Chen has made foundational contributions to the fields of ultra-high dimensional hypothesis testing and nonparametric empirical likelihood methods. His proposed modified Hotelling’s T-squared statistic, published in the Annals of Statistics in 2010, remains a classic statistic for sample testing on high-dimensional means and has garnered great attention.