On April 26, the Department of Statistics and Data Science at Tsinghua University held a successful series of anniversary events, featuring a keynote talk and an alumni forum that drew active participation from faculty, students, and alumni. The event was hosted by Professor Ke Deng, Deputy Head of the department. Professor Deng gave an update on the department's recent progress in research, education, and academic collaboration, and shared a news with alumni: the department is bringing back its statistical consulting platform, which will soon offer professional data analysis and advisory services to the Tsinghua community and beyond.
Invited Keynote Address
Professor Tian Zheng, an alumna of Tsinghua's Department of Applied Mathematics (Class of 1994) and currently a professor at Columbia University, delivered the invited keynote address, entitled Statistics Is What Statisticians Do. Her presentation offered a systematic reflection on the challenges confronting statistics in the age of AI and data science, the evolving professional mindset, the expansion of practical applications, and prospective future directions.
Professor Zheng pointed out that statisticians have mixed feelings about the AI hype. Because the field has always valued rigor and been cautious about mistakes, it tends to move more slowly—quite different from the fast-paced world of machine learning. Using her own research as examples, she argued that statistics needs to actively broaden its definition and keep up with the times.
During the Q&A, Professor Zheng emphasized that asking where data comes from and measuring uncertainty are fundamental to statistics—and these are exactly the strengths that make the field so valuable in the AI era.
Alumni Forum
Following the keynote, the alumni forum - Navigating the Data Seas, Charting Your Career Future, brought together five Tsinghua alumni from academia and industry as panelists: Professor Tian Zheng (Columbia University), Professor Jingyi Li (Fred Hutchinson Cancer Center), Lecturer Nayang Shan (Capital University of Economics and Business), Assistant Professor Yucong Lin (Beijing Institute of Technology), and Senior Solution Architect Moqin Zhou (IEIT SYSTEMS). The session was moderated by Haoyang Yu, a Ph.D. student from the Department of Statistics and Data Science (Class of 2022). The panelists engaged in an in-depth discussion on core topics such as the synergistic development of statistics and AI, and the balanced cultivation of mathematical foundations and AI competencies. Drawing on their own career trajectories, they shared valuable insights into career choices and professional growth.
The panelists reached a consensus on several topics. They agreed that statistics provides both the intellectual and methodological foundation for AI—a relationship of symbiosis rather than replacement—and that statisticians should leverage AI to tackle complex problems and interpret the statistical assumptions underlying models. On the balance between mathematical foundations and AI skills, the panelists advised students to cultivate programming as a core competency, and to purposefully use AI as a tool to support learning and fill gaps in knowledge. The critical factor, they noted, is the capacity to exercise sound judgment in evaluating the validity of AI outputs. The panelists also shared how statistical thinking—particularly uncertainty quantification and Bayesian reasoning—has profoundly influenced both their professional and personal lives, helping them navigate challenges in decision‑making, research, and communication. To close the forum, the panelists reflected on their own career journeys and looked back fondly on their time as students at Tsinghua, recalling the campus life and academic atmosphere that shaped them.
The event closed with panelists reflecting on their Tsinghua days and sharing their career paths—academia, research, or industry. The nearly three‑hour session was lively and interactive. In closing, they stressed the value of a strong foundation and encouraged young scholars to learn tools through practice, build professional skills, and contribute to statistics and data science.
Authors & Photographers: Yushan Hou, Minyu Wang
Reviewers: Ke Deng, Lin Hou, Lefei Li
