【Master Forum】AI Deployment and Statistical Thinking

December 05, 2025 Upcoming

Topic: AI Deployment and Statistical Thinking

Speaker: Prof. LIU Jun

Host: Prof. WU, C. F. Jeff

Date: Friday, December 5, 2025

Time: 10:30 a.m. - 11:45 a.m.

Venue: SIN Wai Kin International Conference Centre (W201, Administration Building)

Language: Chinese

Abstract:?

Driven by big data, technology developments, and statistical ideas, artificial intelligence (AI) has witnessed remarkable development in recent years. However, how to effectively implement and deepen AI across various application domains remains a major challenge for the next decade. Statistics focuses on designing experiments, collecting data, and extracting insights from noisy data to guide decision-making and make predictions while quantifying associated uncertainties; at its core, it employs probability theory as a foundation to build generative and predictive models, enhancing our understanding of data and cognition. These methodological and philosophical perspectives of statistical thinking serve as one of the key driving forces behind the current AI revolution and represent crucial means to further root AI technologies across industries. Fundamental statistical concepts and methods—such as randomization, cross-validation, shrinkage estimation, regularization, bias-variance tradeoff, causal inference, and the Bayesian theory—remain essential guides for handling noisy data, advancing machine learning and AI, and are critical in assessing uncertainty and building interpretable models. We illustrate the power of integrating statistical thinking with AI through several examples.

Speaker Profile:

Liu Jun obtained a Bachelor's degree in Mathematics from Peking University in 1985; In 1991, he obtained a Ph.D. in Statistics from the University of Chicago in the United States. Since 2000, Liu Jun has served as a tenured professor in the Department of Statistics at Harvard University in the United States, and also served as a professor in the Department of Biostatistics at Harvard from 2003 to 2015. He served as an assistant professor in the Harvard Department of Statistics (1991-1994); Assistant Professor, Associate Professor, and tenured Professor in the Department of Statistics at Stanford University (1994-2004); Changjiang Lecture Professor at the School of Mathematics, Peking University, Visiting Professor at the Department of Mathematics, Tsinghua University, and awarded the National Outstanding Youth Fund Class B (2005). He led the establishment of the Center for Statistical Science of Tsinghua University in 2015 and served as honorary director until 2024. In July 2024, he led the establishment of the Department of Statistics and Data Science of Tsinghua University as the director of the Development Committee.

Liu Jun has been engaged in research in Bayesian statistical theory, Monte Carlo methods, statistical machine learning, state space models and time series, bioinformatics, computational biology, and other fields, and has made outstanding contributions, having a profound impact on the fields of big data processing and machine learning. He was awarded the COPSS Presidents' Award in 2002, widely recognized as the highest honor in the international statistical community; In 2010, won the highest honor in applied mathematics for Chinese people in the world, the Chenxing Applied Mathematics Gold Award (once every three years, not exceeding 45 years old); In 2014, he was recognized by ISI as a mathematician with frequent citation of papers; Received the Pao-Lu Hsu Award from the International Chinese Statistical Association in 2016 (once every three years, not exceeding 51 years old); In 2004 and 2005, he became a Fellow of the American Society for Mathematical Statistics and the American Statistical Society respectively; Elected as a Fellow of the International Society for Computational Biology in 2022; Elected as a member of the National Academy of Sciences of United States in 2025.

Professor Liu Jun has also served as co-editor of the Journal of the American Statistical Association (JASA) and associate editor of several top statistical journals. As of May 2025, he has published over 300 papers in various top international academic journals (such as Science, Nature, Cell, JASA, JMLR, etc.) and one book, with more than 90,000 citations (Google Scholar). He has supervised over 40 PhD students and 30 postdoctoral fellows.