Statistical theory and principles for data science
Philosophical and foundational issues in statistics
Statistical computing and computational statistics
Signal extractions and uncertainty assessments
- Statistical Theory and Principles toward the foundation of Data Science;
- Multi-resolution Inferences, such as accumulating statistical evidence for individualized treatments (high resolution prediction) and dealing with partial prior knowledge (low resolution information);
- Multi-phase Inferences, such as handling uncongeniality between data pre-processors (e.g., imputers) and data analysts and preserving information in a distributed pre-processing system;
- Multi-source Inferences, such as comparing large observational datasets with small probabilistic samples and designing methods to gain combined information guided by bias-variance trade-off;
- Philosophical and Foundational Issues in Statistics, such as connecting and the interplay between Bayesian, Fiducial, and frequentist (BFF) perspectives, and their extensions, including belief function;
- Statistical Computing and Computational Statistics, such as Markov chain Monte Carlo, EM-type algorithms and their self-consistent generalizations, and user-friendly combining rules for multiple-imputation inference;
- Signal extractions and Uncertainty Assessments in natural, social, and medical sciences, such as in astronomy/astrophysics and in psychology/psychiatry;
- Elegant Mathematical Statistics, especially distribution theory and stochastic algebra
- 1990: Ph.D. in Statistics - Harvard University
- 1987: M.A. in Statistics - Harvard University
- 1986: Diploma in Graduate Study of Mathematical Statistics - Research Institute of Mathematics, Fudan University, Shanghai, P.R. China
- 1982: B.S. in Mathematics - Fudan University, Shanghai, P.R. China
- 2018-present: Founding Editor-in-Chief, Harvard Data Science Review
- 2012 - 2018; Dean, Graduate School of Arts and Sciences, Harvard University (on leave 2017-2018)
- 2004 - 2012: Chair, Department of Statistics, Harvard University (on leave 2010-2011)
- 2001 - present: Professor, Department of Statistics, Harvard University
- 2001 - 2005: Research Associate (Professor), Department of Statistics and the College, The University of Chicago
- 1991 - 2001: Assistant/Associate/Full Professor, Department of Statistics and the College, The University of Chicago
- 1993 - present: Faculty Research Associate, Population Research Center, National Opinion Research Center (NORC), The University of Chicago
- 1982 - 1984: Instructor of Mathematics, Department of Basic Science, China Textile University, Shanghai, P.R. China
- Meng, X.-L. (2018) Statistical Paradises and Paradoxes in Big Data (I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election. Annals of Applied Statistics, Vol. 12, No. 2, 685–726.
- Meng, X.-L. (2018) Conducting Highly Principled Data Science: A statistician's job and joy. Statistics and Probability Letters,136, 51-57.
- X. Xie and Meng, X.-L. (2017) Dissecting Multiple Imputation from a Multiphase Inference Perspective: What Happens When There Are Three Uncongenial Models Involved? (With discussions.) Statistics Sinica 27, 1485-1594.
- Liu, K. and Meng, X.L. (2016). There is Individualized Treatment. Why Not Individualized Inference? Annual Review of Statistics and Its Application 3, 79-111. Main paper. DASH entry.
- Meng, X.L. (2014). A Trio of Inference Problems that Could Win You a Nobel Prize in Statistics (If You Help Fund It). In Past, Present, and Future of Statistical Science (Eds: X. Lin, et. al), CRC Press, pp. 537-562. Final draft.
- Blocker, A.W. and Meng, X.L. (2013). The Potential and Perils of Preprocessing: Building New Foundations. Bernoulli 19, 1176-1211. Final draft.
- Yu, Y. and Meng, X.L. (2011). To Center or Not to Center: That Is Not the Question -- An Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Efficiency (with discussion). Journal of Computational and Graphical Statistics 20, 531-615. Main paper (531-570), Supplement, Discussion (571-602) and Rejoinder (603-615).
- Nicolae, D., Meng, X.L. and Kong, A. (2008). Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies (with discussion). Statistical Science 23, 287-331. Main paper (287-312) and Rejoinder (325-331).
- Kong, A., McCullagh, P., Meng, X.L., Nicolae, D. and Tan, Z. (2003). A Theory of Statistical Models for Monte Carlo Integration (with discussion). Journal of the Royal Statistical Society B 65, 585-618; JSTOR.
- van Dyk, D.A. and Meng, X.L. (2001). The Art of Data Augmentation (with discussion). Journal of Computational and Graphical Statistics 10, 1-111. Main paper (1-50) and Rejoinder (98-111).
- Meng, X.L. and van Dyk, D.A. (1997). The EM Algorithm - An Old Folk Song Sung to a Fast New Tune (with discussion). Journal of the Royal Statistical Society B 59, 511 - 567; JSTOR.
- Gelman, A.E., Meng, X.L. and Stern, H. (1996). Posterior Predictive Assessment of Model Fitness via Realized Discrepancies (with discussion). Statistica Sinica 6, 733-807.
- Meng, X.L. (1994). Multiple-Imputation Inference with Uncongenial Sources of Input (with discussion). Statistical Science 9, 538-573. Main paper (538-558) and Rejoinder (566-573).
Curriculum Vitae (contains links to most lecture videos and articles)
Biography (Harvard Data science Initiative)
The XL-Files (a collection of IMS Bulletin columns: comments after each are welcome!)
On Rejection (thoughts about an inevitable but necessary experience)