Whipple V. N. Jones Professor of Statistics
- Statistical inference with partially observed data, pre-processed data, and simulated data.
- Quantifying statistical information and efficiency in scientific studies, particularly for genetic and environmental problems.
- Statistical principles and foundational issues, such as multi-party inferences, the theory of ignorance, and the interplay between Bayesian and frequentist perspectives.
- Effective deterministic and stochastic algorithms for Bayesian and likelihood computation; Markov chain Monte Carlo, especially perfect sampling.
- Bayesian inference, ranking and mapping.
- Multi-resolution modelling for signal and image data.
- Statistical issues in astronomy and astrophysics.
- Modelling and imputation in health and medical studies.
- Elegant mathematical statistics.
- 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
- 2012 - present; Dean, Graduate School of Arts and Sciences, Harvard University
- 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. (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)
Short Biography (Dean's Office, Harvard Graduate School of Arts and Sciences)
The XL-Files (a collection of IMS Bulletin columns: comments after each are welcome!)
On Rejection (thoughts about an inevitable but necessary experience)