Join us for an in-person Distinguished Speaker event featuring Xiao-Li Meng, an internationally renowned statistician, data scientist, and former Dean of the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences. Widely recognized for his pioneering contributions to statistical science, data analysis, and interdisciplinary research, Meng has helped shape statistics and data science through groundbreaking scholarship, academic leadership, and innovative approaches to understanding uncertainty in an increasingly data-driven world.
Title: A Trinity Platform for Scholarly Publications: A Harvard Experiment
Designed as a Trinity platform and launched in 2019 with a commitment to diamond open access, Harvard Data Science Review (HDSR) unites the strengths of a premier research journal, a cutting-edge educational publication, and a popular magazine to provide a crossroads at which fundamental data science research and education intersect and interact directly with societally important applications. This eight-year experimentation offers initial evidence that such a model can be intellectually viable, societally impactful, and financially sustainable, because it better captures the intellectual, pedagogical, and economic values created by the three Cs that define scholarly publication. That is, statistically speaking, a scholarly publication is where the content is timely and of the highest quality, created by an ever-expanding community of the best minds — authors, reviewers, and editors — committed to a shared culture of rigorous pursuit and critical thinking. Extending beyond the page into podcasts, conferences, and online programs, it offers a versatile and agile model for scholarly publications in the age of AI, especially as our global society undergoes an “AGI realignment” (Meng, HDSR Spring Editorial, 2026): of the roles, functions, and interactions of Academia, Government, and Industry. With respect to the more commonly known AGI (Artificial General Intelligence), the talk ends with a provocation: the scholarly publishing community sits on a hidden treasure for building AGI. Deciding whether to offer this treasure likely takes more than scholarly debate, since its consequences could be lauded (e.g., supporting open access) or dreaded (e.g., given existing fears about AI).
Xiao-Li Meng is the Whipple V. N. Jones Professor of Statistics at Harvard and Founding Editor-in-Chief of Harvard Data Science Review. He received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. After serving on the University of Chicago faculty (1991–2001), he returned to Harvard, where he chaired the Department of Statistics (2004–2012) and served as Dean of the Graduate School of Arts and Sciences (2012–2017). His research spans foundations of statistical inference—including interactions among Bayesian, fiducial, and frequentist perspectives; standard, imprecise, and quantum probability; data utility and privacy; and multi-source, multi-phase, and multi-resolution inference—statistical methodology and computation, including posterior predictive p-values, EM, MCMC, bridge and path sampling, and heavy-tail approximations; and applications across natural, social, and medical sciences and engineering, including complex modeling in astronomy and astrophysics, disparities in mental health services, and statistical information in genetic studies. In 2001, Meng received the COPSS President’s Award, recognizing the outstanding statistician under age 40, and in 2020 was elected to the American Academy of Arts and Sciences.