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CONGRATULATIONS TO PROFESSOR VICTOR DE LA PENA'S FOUNDATIONAL WORK AS MOST-READ IN THE ANNALS OF PROBABILITY
CONGRATULATIONS TO PROFESSOR VICTOR DE LA PENA’S FOUNDATIONAL WORK AS MOST-READ IN THE ANNALS OF PROBABILITY

September 2025

 

The Department of Statistics is pleased to announce that Professor Victor H. de la Pena’s 2004 paper “Self-normalized processes: exponential inequalities, moment bounds and iterated logarithm laws” (co-authored with Michael J....

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Congratulations to Professor Ming Yuan 2024 Sharpe Award Winner
Congratulations to Professor Ming Yuan 2024 Sharpe Award Winner

Congratulations to Professor Ming Yuan, Department of Statistics, on his 2024 Sharpe Award for Scholarship in Financial Research.

William F. Sharpe Award...

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Congratulations to Jonas Magdy Mikhaeil, Department of Statistics rising 4th year PhD student, on his 2025 Tom Ten Have Award. The Tom Ten Have Award is awarded at each ACIC to two early career researcher poster presenters for posters that exhibit exceptionally creative or skillful research on causal inference. Tom Ten Have, a professor of biostatistics at the University of Pennsylvania School of Medicine, passed away in 2011 at the age of 53 from complications of multiple myeloma. To honor Tom’s passionate encouragement of early career causal inference researchers, the Tom Ten Have poster award was established in 2012. Tom made important contributions to causal inference in the areas of estimating the effect of receiving treatment in randomized trials with nonadherence and mediation analysis.
Congratulations to 2025 ACIC Tom Ten Have Awardee, Jonas Magdy Mikhaeil, Rising 4th Year Statistics Doctoral Student

Congratulations to Jonas Magdy Mikhaeil, Department of Statistics rising 4th year PhD student, on his 2025 Tom Ten Have Award. The Tom Ten Have Award is awarded at each ACIC to two early career researcher poster presenters for posters that exhibit exceptionally creative or skillful research on causal inference. ...

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Founder’s Postdoctoral Fellowship in Statistics Starting Fall 2025
Founder’s Postdoctoral Fellowship in Statistics Starting Fall 2025

Columbia University Department of Statistics

Founder’s Postdoctoral Fellowship in Statistics Starting Fall 2025

Position Description: The Department of Statistics invites applications for the 2025 Founder’s Postdoctoral Fellowship in Statistics at Columbia University....

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Congratulations to Founder’s Postdoc Belinda Tzen and her Co-author’s on the Roberto Tempo Best CDC Paper Award
Congratulations to Founder’s Postdoc Belinda Tzen and her Co-author’s on the Roberto Tempo Best CDC Paper Award

The Roberto Tempo Best CDC Paper Award recognizes the best paper presented at the annual IEEE Conference on Decision and Control. ...

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Congratulations to 2018 PhD Alum Lisha Qiu
Congratulations to 2018 PhD Alum Lisha Qiu

 

 

Lisha Qiu, Quantitative Strategist at Virtu Financial, Awarded Rising Star at Markets Media Group’s 2024 Women in Finance (U.S.) Awards....

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Congratulations to Two Columbia University PhD Candidates in Statistics Awarded 2025 Student Paper Competition Winners!
Congratulations to Two Columbia University PhD Candidates in Statistics Awarded 2025 Student Paper Competition Winners!

 

 

 

 

 

 

 

The Department of Statistics is very proud to announce that two of our Doctoral Students, Seunghyun (Sky) Lee and Ye Tian, have been named winners of the 2025 Student Paper Competition in the Statistical Learning and Data Science (SLDS) Section of the American Statistical Association (ASA)!...

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Congratulations to Professor Genevera Allen, Professor Blei, Professor Marcel Nutz, Professor Bodhisattva Sen & Professor Ian Mckeague!
Congratulations to Professor Genevera Allen, Professor Blei, Professor Marcel Nutz, Professor Bodhisattva Sen & Professor Ian Mckeague!
Five Columbia Faculty Receive Top Honor in Statistics-https://news.columbia.edu/news/five-columbia-faculty-receive-top-honor-statistics

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Recent Faculty Publications

Pathfinder: Parallel quasi-Newton variational inference.
Andrew Gelman (2022).
Before data analysis: Additional recommendations for designing experiments to learn about the world.
Andrew Gelman (2023).
Toward a taxonomy of trust for probabilistic machine learning. Science Advances, 9(7), eabn3999.
Broderick, T., Gelman, A., Meager, R., Smith, A. L., & Zheng, T. (2023).
Taylor's law of fluctuation scaling for semivariances and higher moments of heavy tailed data, (PNAS November 16,2021)
Brown, M., Cohen, J.E., Tang, C-F., & Yam, S.C.P. (2021).
Privacy-preserving parametric inference: a case for robust statistics. Journal of the American Statistical Association, 116(534), 969-983.
Avella-Medina, M. (2021).
Heavy-tailed distributions, correlations, kurtosis, and Taylor’s law of fluctuation scaling. Proceedings of the Royal Society A 476:20200610.
J. E. Cohen, Richard A. Davis, Gennady Samorodnitsky (2020).
A Proxy Variable View of Shared Confounding. In International Conference on Machine Learning (pp. 10697-10707). PMLR.
Wang, Y., & Blei, D. (2021, July).
Count time series: A methodological review. Journal of the American Statistical Association, 1-15.
Davis, R. A., Fokianos, K., Holan, S. H., Joe, H., Livsey, J., Lund, R., ... & Ravishanker, N. (2021).
High-frequency analysis of parabolic stochastic PDEs. The Annals of Statistics, 48(2), 1143-1167.
Chong, C. (2020).
The continuous categorical: a novel simplex-valued exponential family. In International Conference on Machine Learning (pp. 3637-3647). PMLR.
Gordon-Rodriguez, E., Loaiza-Ganem, G., & Cunningham, J. (2020, November).
On the bias and variance of odds ratio, relative risk and false discovery proportion. Communications in Statistics-Theory and Methods, 1-31.
Pang, G., Alemayehu, D., de la Peña, V., & Klass, M. J. (2020).
Bayesian statistics and modelling. Nature Reviews Methods Primers, 1(1), 1-26.
van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., ... & Yau, C. (2021).
A Joint MLE Approach to Large-Scale Structured Latent Attribute Analysis. Journal of the American Statistical Association, (just-accepted), 1-39.
Gu, Y., & Xu, G. (2021).
Self-Tuning Bandits over Unknown Covariate-Shifts. In Algorithmic Learning Theory (pp. 1114-1156). PMLR.
Suk, J., & Kpotufe, S. (2021, March).
An adaptable generalization of Hotelling’s $ T^{2} $ test in high dimension. The Annals of Statistics, 48(3), 1815-1847.
Li, H., Aue, A., Paul, D., Peng, J., & Wang, P. (2020).
An Interaction-based Convolutional Neural Network (ICNN) Towards Better Understanding of COVID-19 X-ray Images. arXiv preprint arXiv:2106.06911.
Lo, S. H., & Yin, Y. (2021).
Latent feature extraction for process data via multidimensional scaling. psychometrika, 85(2), 378-397.
Tang, X., Wang, Z., He, Q., Liu, J., & Ying, Z. (2020).
Joint estimation of parameters in Ising model. The Annals of Statistics, 48(2), 785-810.
Ghosal, P., & Mukherjee, S. (2020).
Convergence to the mean-field game limit: a case study. The Annals of Applied Probability, 30(1), 259-286.
Nutz, M., San Martin, J., & Tan, X. (2020).
Neural clustering processes. In International Conference on Machine Learning (pp. 7455-7465). PMLR.
Pakman, A., Wang, Y., Mitelut, C., Lee, J., & Paninski, L. (2020, November).
Credit Risk, Liquidity, and Bubbles. International Review of Finance, 20(3), 737-746.
Jarrow, R., & Protter, P. (2020).
An asymptotic rate for the LASSO loss. In International Conference on Artificial Intelligence and Statistics (pp. 3664-3673). PMLR.
Rush, C. (2020, June).
Multivariate rank-based distribution-free nonparametric testing using measure transportation. Journal of the American Statistical Association, (just-accepted), 1-45.
Deb, N., & Sen, B. (2021).
Asset pricing with general transaction costs: Theory and numerics. Mathematical Finance, 31(2), 595-648.
Gonon, L., Muhle‐Karbe, J., & Shi, X. (2021).
Estimating causal effects in studies of human brain function: New models, methods and estimands. The annals of applied statistics, 14(1), 452.
Sobel, M. E., & Lindquist, M. A. (2020).
A note on the Screaming Toes game. arXiv preprint arXiv:2006.04805.
Tavaré, S. (2020).
Testing for stationarity of functional time series in the frequency domain. The Annals of Statistics, 48(5), 2505-2547.
Aue, A., & Van Delft, A. (2020).
Distinguishing cause from effect using quantiles: Bivariate quantile causal discovery. In International Conference on Machine Learning (pp. 9311-9323). PMLR.
Tagasovska, N., Chavez-Demoulin, V., & Vatter, T. (2020, November).
Robust estimation of superhedging prices. The Annals of Statistics, 49(1), 508-530
Obłój, J., & Wiesel, J. (2021).
Optimal stopping and worker selection in crowdsourcing: An adaptive sequential probability ratio test framework. Statistica Sinica, 31(1), 519-546.
Li, X., Chen, Y., Chen, X., Liu, J., & Ying, Z. (2021)
Revisiting colocalization via optimal transport. Nature Computational Science, 1(3), 177-178.
Wang, S., & Yuan, M. (2021).
Large‐scale, image‐based tree species mapping in a tropical forest using artificial perceptual learning. Methods in Ecology and Evolution, 12(4), 608-618.
Tang, C., Uriarte, M., Jin, H., C Morton, D., & Zheng, T. (2021).
From Decoupling and Self-Normalization to Machine Learning
Victor H. de la Pena
Capacity-Achieving Sparse Superposition Codes via Approximate Message Passing Decoding
Cynthia Rush, Adam Greig, Ramji Venkataramanan
Slice Sampling on Hamiltonian Trajectories
John P. Cunningham, Benjamin Bloem-Reddy
Feature Augmentation via Nonparametrics and Selection (FANS) in High Dimensional Classification
Fan, J., Feng, Y., Jiang, J. and Tong, X.
Topic-adjusted visibility metric for scientific articles. Ann. Appl. Stat. 10 (2016), no. 1, 1--31.
Linda S. L. Tan, Aik Hui Chan, and Tian Zheng
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