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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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Congratulations to Gonzalo Mena on being recently acknowledged as one of Chile's Young Leaders for his work in artificial intelligence in the solving of complex phenomenon.
Congratulations to Gonzalo Mena on being recently acknowledged as one of Chile’s Young Leaders for his work in artificial intelligence in the solving of complex phenomenon.

Congratulations to Gonzalo Mena (GSAS’18) on being recently acknowledged as one of Chile’s Young Leaders for his work in artificial intelligence in the solving of complex phenomenon....

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Introducing: Summer 2022 Statistics Undergraduate Research Interns
Introducing: Summer 2022 Statistics Undergraduate Research Interns

Clemente Antuna

Majors/Minor: Mathematics – Statistics/ Economics Research Mentor: Marco Avella Medina, Ph.D. Project Title: Statistical differential privacy, optimization, and robustness

 

Guosheng Cai

Majors: Mathematics – Statistics  Research Mentor: Wayne T....

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"Making a Better World: One P-value at a Time" By Kim Martineau
“Making a Better World: One P-value at a Time” By Kim Martineau

Statistics lecturer and amateur dancer Wayne Lee talks about his experience as a Taiwanese-American and explains why he left Silicon Valley to teach statistics and how this corner of mathematics can help us all learn to be more open minded....

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Congratulations to Adji Bousso Dieng on being selected as the 2022 Annie T. Randall Innovator Award Winner!
Congratulations to Adji Bousso Dieng on being selected as the 2022 Annie T. Randall Innovator Award Winner!

Congratulations to Adji Bousso Dieng on being selected as the 2022 Annie T. Randall Innovator Award Winner!

Dr. Dieng @adjiboussodieng is recognized for her innovative statistical methods, founding @theafricaiknow_ & focus on societal impact....

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Congratulations to Bodhisattva Sen and Tian Zheng on being named 2022 IMS Fellows.
Congratulations to Bodhisattva Sen and Tian Zheng on being named 2022 IMS Fellows.

Congratulations to Professor Bodhisattva Sen and Professor Tian Zheng on being named 2022 IMS Fellows.

Bodhisattva Sen Named IMS Fellow

Bodhisattva Sen, Professor of Statistics, Columbia University, has been named Fellow of the Institute of Mathematical Statistics (IMS). ...

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Congratulations to Daiki Tagami on winning the 2022 Columbia College Leadership and Excellence Award (LEA) for Innovation & Enhancement
Congratulations to Daiki Tagami on winning the 2022 Columbia College Leadership and Excellence Award (LEA) for Innovation & Enhancement

Daiki Tagami Wins the 2022 Columbia College Leadership and Excellence Award (LEA) for Innovation & Enhancement

Daiki’s selection as an honoree is a testament to the outstanding impact he’s had on his peers, the campus, and other communities he has served....

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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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