Xiao-Li Meng

Xiao-Li Meng, a highly respected statistician and Whipple V. N. Jones Professor at Harvard, discusses his significant contributions to data science. He explores the differences between 'statistician' and 'data scientist' while sharing his personal journey from Shanghai to Cambridge. The conversation highlights the challenges of data collection, especially in elections, and underscores the need for educational reforms that integrate data science into K-12 curricula. Meng emphasizes the importance of critical thinking and integrity in navigating the evolving landscape of data.

Prof. Xiao-Li Meng discusses data trade-offs, paradoxical downsides of abundant data, and the differences between Bayesian, Frequentist, and Fiducial statistics in data science. Topics include data mining, data confession, and tricky trade-offs with data.

Professor Xiao-Li Meng discusses designing data science curricula, addressing the heterogeneity of the field. He explores the importance of data quality, practical experience, and balancing data privacy. The conversation covers reshaping statistics PhD programs, staying current in the field, and the Harvard Data Science Review as a valuable resource.