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Statistical Foundations and Applications of Machine Learning and AI
Statistical Foundations and Applications of Machine Learning and AI

In celebration of Professor Shaw-Hwa Lo’s 70th Birthday and his longtime collaboration with Professor Herman Chernoff, this symposium includes presentations on the latest advances in statistical research. ...

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Congratulations to our 2022 Ph.D. Graduates!
Congratulations to our 2022 Ph.D. Graduates!
Congratulations to our 2022 Ph.D. Graduates!

Andrew Davison

Sponsor: Tian Zheng

Nabarun Deb

Sponsor: Bodhisattva Sen / Co-Sponsor: Sumit Mukherjee

Elliot Gordon Rodriguez

Sponsor: John Cunningham

Charles C....

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Congratulations to our PhD Alum, Dr. Yunxiao Chen, on being selected as the 2022 Psychometric Society Early Career Award Winner!
Congratulations to our PhD Alum, Dr. Yunxiao Chen, on being selected as the 2022 Psychometric Society Early Career Award Winner!

The Psychometric Society Early Career Award recognizes scholars at the early stage of their career whose research has the potential to make a major contribution to psychometric research and/or practice....

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Getting a Clearer View of the Stars—With Statistics
Getting a Clearer View of the Stars—With Statistics

PhD student Luhuan Wu explains how statistics can make the stars in distant galaxies easier to see and shares her secret for taking stunning travel photos....

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Congratulations to Alejandra Quintos and Long Zhao for being awarded the 2021 Howard Levene Outstanding Teaching Award
Congratulations to Alejandra Quintos and Long Zhao for being awarded the 2021 Howard Levene Outstanding Teaching Award

Congratulations to Alejandra Quintos Lima and Long Zhao for being awarded the 2021 Howard Levene Outstanding Teaching Award...

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Congratulations to Elliott Gordon Rodriguez, Charles C. Margossian, Ashley Datta and Zhi Wang for being awarded the 2021 Minghui Yu Teaching Assistant Award
Congratulations to Elliott Gordon Rodriguez, Charles C. Margossian, Ashley Datta and Zhi Wang for being awarded the 2021 Minghui Yu Teaching Assistant Award

Congratulations to Elliott Gordon Rodriguez, Charles C. Margossian, Ashley Datta and Zhi Wang for being awarded the 2021 Minghui Yu Teaching Assistant Award....

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Introducing the Summer 2022 PIT-DSC Cohort
Introducing the Summer 2022 PIT-DSC Cohort

The Summer 2022 Public Interest Technology – Data Science Corps, PIT-DSC, program kicked off on June 6, 2022. 23 interns from Barnard College, Columbia University, and Lehman College met with 5 project mentors, and the program staff at Columbia University....

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Meet the 2022 STARs
Meet the 2022 STARs

The Department of Statistics welcomes the inaugural Summer Training for Aspiring Researchers (STAR) Program cohort! In the next five weeks, 13 undergraduate students aspiring researchers in statistics, data science, or related fields, will receive intensive training in mathematics, probability, statistics, programming, and research skills....

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Recent Faculty Publications

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).
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)
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).
Revisiting colocalization via optimal transport. Nature Computational Science, 1(3), 177-178.
Wang, S., & Yuan, M. (2021).
From Decoupling and Self-Normalization to Machine Learning
Victor H. de la Pena
Stabilized Sparse Online Learning for Sparse Data
Yuting Ma, Tian Zheng
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
Asset pricing with general transaction costs: Theory and numerics. Mathematical Finance, 31(2), 595-648.
Gonon, L., Muhle‐Karbe, J., & Shi, X. (2021).
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).
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).
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).
Self-Tuning Bandits over Unknown Covariate-Shifts. In Algorithmic Learning Theory (pp. 1114-1156). PMLR.
Suk, J., & Kpotufe, S. (2021, March).
Credit Risk, Liquidity, and Bubbles. International Review of Finance, 20(3), 737-746.
Jarrow, R., & Protter, P. (2020).
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).
Boosted Sparse Non-linear Distance Metric Learning
Yuting Ma and Tian Zheng
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