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Professor Xiao-Li Meng: AI, Beatles, and Elections - A Nano Tour of Data Science

This event is being co-hosted by the IDSAI and Egenis, The Centre for the Study of Life Science

Professor Xiao-Li Meng is the Whipple V.N. Jones Professor of Statistics at Harvard University and the Editor in Chief of the Harvard Data Science Review. Professor Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard, where he served as the Chair of the Department of Statistics (2004-2012) and the Dean of Graduate School of Arts and Sciences (2012-2017).

Event details


AI, Beatles, and Elections - A Nano Tour of Data Science

What is data science (DS)? Some declare DS=CS (Computer Science), some consider DS=S (Statistics), and yet some even think DS=BS (but not Bayesian Statistics).  The truth is that DS is so broad that it is easier to understand it through its compliment. From that angle, DS is not just about deep learning, or prediction, or data analysis.  It is not a STEM discipline. It is not even a single discipline. This talk reports my experience as the founding Editor-in-Chief of Harvard Data Science Review (HDSR), and as a statistician, in exploring the landscape of DS.  I will first demonstrate its vastness by using articles in HDSR, which address questions ranging from “How does AI impact my life?” to “Who wrote In My Life?”. I will then demonstrate how statistical thinking helps to reveal a big data paradox, which provides an explanation of our collective failure in predicting the 2016 US Presidential Election, as well as an insight into the 2020 election.

Live stream of seminar available at:


Professor Meng was named the best statistician under the age of 40 by COPSS (Committee of Presidents of Statistical Societies) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development.  

His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, Fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multi- resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies).


LSI Seminar Room A&B