# Xiao-Li Meng Whipple V. N. Jones Professor of Statistics ![Xiao-Li Meng](/sites/g/files/omnuum10116/files/styles/hwp_4_5__320x400/public/statistics-2/files/meng.jpg?itok=XUQEqti3) location\_on Maxwell Dworkin 211A smartphone [(617) 495-1603]() email **Research Interests:** - 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 **Education** - 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 **Experience** - 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 **Sample Publications** - Craiu, R.V., Gong, R., and Meng, X.L. (2023). [Six Statistical Senses](https://www.dropbox.com/s/s1qv04g4z6jj6lw/SSS.pdf?dl=0). [*Annual Review of Statistics and its Applications*](https://www.annualreviews.org/journal/statistics)*.* 10., 699-725. - Bradley, V., Kuriwaki, S., Isakov, M., Sejdinovic, D., Meng, X.L., and Flaxman, S. (2021). [Unrepresentative big surveys significantly overestimated US vaccine uptake](https://www.nature.com/articles/s41586-021-04198-4?msclkid=ede012a7cfb711ec8629d39d2093074d). *Nature,* DOI:10.1038/s41586-021-04198-4. (See related Harvard Gazette Article [at this link](https://news.harvard.edu/gazette/story/2021/12/vaccination-surveys-fell-victim-to-big-data-paradox-harvard-researchers-say/).) - Gong, R. and Meng, X.L. (2021).[ Judicious Judgment Meets Unsettling Updating: Dilation, Sure Loss and Simpson’s Paradox](/file_url/1349) (with discussions). *Statistical Science*, *36*(2),169-214. - Li, X and Meng, X.L. (2021). [A Multi-resoultion Theory for Approximateing Infinite-p-Zero-n: Transitional Inference, Individualized Predictions, and a World Without Bias-Variance Tradeoff](/file_url/1193). *Journal of the American Statistical Association,* DOI:10.1080/01621459.2020.1844210. - Meng, X.L. (2021). [Enhancing (Publications on) Data Quality: Deeper Data Minding and Fuller Data Confession](/file_url/1191). *Journal of the Royal Statistical Society: Series A (Statistics in Society)*, Vol 84, No. 4, 1161-1175. - 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](/file_url/823). *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](https://www.sciencedirect.com/science/article/pii/S0167715218300981),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?](/files/statistics/files/miforssreprint.pdf) (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](http://www.annualreviews.org/eprint/yxTDXz56Bbp8a8wwFnRi/full/10.1146/annurev-statistics-010814-020310). [DASH entry](https://dash.harvard.edu/handle/1/28493223). - 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*](http://www.crcpress.com/product/isbn/9781482204964) (Eds: X. Lin, et. al), CRC Press, pp. 537-562.[ Final draft](/file_url/1125). - Blocker, A.W. and Meng, X.L. (2013). The Potential and Perils of Preprocessing: Building New Foundations. [*Bernoulli* ](http://projecteuclid.org/euclid.bj/1377612848)19, 1176-1211. [Final draft.](/file_url/1109) - Meng, X.L. (2011). [What’s the H in H-likelihood: A Holy Grail or an Achilles’ Heel?](/file_url/1005) (with discussions). *Bayesian Statistics*, *9*, 473-500. - 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 ](/file_url/1088)(531-570),[ Supplement](https://www.tandfonline.com/doi/suppl/10.1198/jcgs.2011.203main?scroll=top), [Discussion](/file_url/1089) (571-602) and [Rejoinder](/file_url/1090) (603-615). - Meng, X.L. (2009). [Decoding the H-likelihood.](/file_url/1077) *Statistical Science*, *24*(3), 280-293. - 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*](http://projecteuclid.org/handle/euclid.ss) 23, 287-331. [Main paper](http://dash.harvard.edu/handle/1/2766348) (287-312), [Discussion](https://projecteuclid.org/journals/statistical-science/volume-23/issue-3/Comment-Quantifying-Information-Loss-in-Survival-Studies/10.1214/08-STS244B.full) (313-317), [ Discussion ](https://projecteuclid.org/journals/statistical-science/volume-23/issue-3/Comment--Quantifying-the-Fraction-of-Missing-Information-for-Hypothesis/10.1214/08-STS244C.full)(318-320), [Discussion](https://projecteuclid.org/journals/statistical-science/volume-23/issue-3/Comment--Quantifying-the-Fraction-of-Missing-Information-for-Hypothesis/10.1214/08-STS244A.full) (321-324) and [Rejoinder](http://dash.harvard.edu/handle/1/4729738) (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)](/file_url/1071). *Journal of the Royal Statistical Society B* 65, 585-618; [JSTOR](https://www.jstor.org/stable/3647541?origin=JSTOR-pdf&seq=1). - 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](/file_url/1094) (1-50), [Discussion](/file_url/1095) (51-97) and [Rejoinder](/file_url/1096) (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)](/file_url/1069). *Journal of the Royal Statistical Society B* 59, 511 - 567; [JSTOR](https://www.jstor.org/stable/2346009?msclkid=58ba9078cfc011eca8bc000c2a76b843&seq=1). - Gelman, A.E., Meng, X.L. and Stern, H. (1996). [Posterior Predictive Assessment of Model Fitness via Realized Discrepancies (with discussion)](http://www3.stat.sinica.edu.tw/statistica/password.asp?vol=6&num=4&art=1). *Statistica Sinica* 6, 733-807. - Meng, X.L. (1994). Multiple-Imputation Inference with Uncongenial Sources of Input (with discussion). [Statistical Science ](https://projecteuclid.org/journals/statistical-science/volume-9/issue-4/Multiple-Imputation-Inferences-with-Uncongenial-Sources-of-Input/10.1214/ss/1177010269.full#)9, 538-573. [Main paper](/file_url/1072) (538-558), [Discussion](https://projecteuclid.org/journals/statistical-science/volume-9/issue-4/Multiple-Imputation-Inferences-with-Uncongenial-Sources-of-Input/10.1214/ss/1177010269.full?msclkid=b7dd5597d07911ec8350b86e3ee1de7e&tab=RelatedArticles) (559-567) and [Rejoinder](https://projecteuclid.org/journals/statistical-science/volume-9/issue-4/Multiple-Imputation-Inferences-with-Uncongenial-Sources-of-Input-Rejoinder/10.1214/ss/1177010274.full) (566-573). [**Curriculum Vitae** ](/cv-xlm)**(contains links to most lecture videos and articles)** [**Biography** ](https://hdsr.mitpress.mit.edu/pub/lc5rm9ku/release/3)**(**[**Harvard Data Science Review**](https://hdsr.mitpress.mit.edu/)**)** [**The XL-Files**](https://imstat.org/category/xl-files/) **(a collection of** ***IMS Bulletin*** **columns: comments after each are welcome!)** - ## Last Initial [I - M](/last-initial/i-m) - ## People [Department Faculty](/people/department-faculty)