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New Book by Professor Demissie Alemayehu, Emir Birol and Michael Gaffney "Interface between Regulation and Statistics in Drug Development" available on Amazon
New Book by Professor Demissie Alemayehu, Emir Birol and Michael Gaffney “Interface between Regulation and Statistics in Drug Development” available on Amazon

New Book by Professor Demissie Alemayehu, Emir Birol and Michael Gaffney “Interface between Regulation and Statistics in Drug Development” (Chapman & Hall/CRC Biostatistics Series) available on Amazon...

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Congratulations to our 2020 Ph.D. Graduates! Ph.D. Students Successfully Defend Their Doctoral Dissertations.
Congratulations to our 2020 Ph.D. Graduates! Ph.D. Students Successfully Defend Their Doctoral Dissertations.
Congratulations to our 2020 Ph.D. Graduates!

Click here to view the list of our 2020 Ph.D. Graduates 

 ...

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2021 Minghui Yu Memorial Conference will take place on Saturday, March 27th
2021 Minghui Yu Memorial Conference will take place on Saturday, March 27th

2021 Minghui Yu Memorial Conference will take place on Saturday, March 27th. ...

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Congratulations to Professor Michael Sobel on winning the Sells Award for Distinguished Multivariate Research
Congratulations to Professor Michael Sobel on winning the Sells Award for Distinguished Multivariate Research

Congratulations to Michael Sobel on winning the Sells Award for Distinguished Multivariate Research. The Sells award is the highest award given by the Society for Multivariate Experimental Psychology (SMEP)....

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Ph.D. Student, Miguel Ángel Garrido, is spotlighted in the annual report of Center for Teaching and Learning
Ph.D. Student, Miguel Ángel Garrido, is spotlighted in the annual report of Center for Teaching and Learning

Ph.D. Student, Miguel Ángel Garrido, is spotlighted in the annual report of Center for Teaching and Learning.

The 2019-2020 Annual Report of the Center for Teaching and Learning summarizes the programs and services provided during the year to the Columbia University teaching community....

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Please join us in congratulating Professor Marcel Nutz on being selected as the Columbia-Ecole Politechnique Alliance Visiting Professor for the 2020 - 2021 academic year.
Please join us in congratulating Professor Marcel Nutz on being selected as the Columbia-Ecole Politechnique Alliance Visiting Professor for the 2020 – 2021 academic year.

Please join us in congratulating Professor Marcel Nutz on being one of the selected Alliance Visiting Professors for the 2020 – 2021 academic year....

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Professor Victor de la Pena's paper ``From Decoupling and Self-Normalization to Machine Learning'' was invited and has been published in the ``Notices of the American Society'' 
Professor Victor de la Pena’s paper “From Decoupling and Self-Normalization to Machine Learning” was invited and has been published in the “Notices of the American Society” 
Professor Victor de la Pena’s paper “From Decoupling and Self-Normalization to Machine Learning” has been published in the “Notices of the American Society”  https://www.ams.org/journals/notices/201910/rnoti-p1641.pdf...

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Search: Lecturer in Discipline Position Starting Fall 2021
Search: Lecturer in Discipline Position Starting Fall 2021

Department of Statistics

Columbia University

Lecturer in Discipline Position Starting Fall 2021

The Department of Statistics invites applications for multiple positions at the rank of Lecturer in Discipline to begin July 1, 2021....

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Recent Faculty Publications

Before data analysis: Additional recommendations for designing experiments to learn about the world.
Andrew Gelman (2023).
Pathfinder: Parallel quasi-Newton variational inference.
Andrew Gelman (2022).
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).
Robust estimation of superhedging prices. The Annals of Statistics, 49(1), 508-530
Obłój, J., & Wiesel, J. (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).
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).
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).
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).
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.
Asset pricing with general transaction costs: Theory and numerics. Mathematical Finance, 31(2), 595-648.
Gonon, L., Muhle‐Karbe, J., & Shi, X. (2021).
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).
An asymptotic rate for the LASSO loss. In International Conference on Artificial Intelligence and Statistics (pp. 3664-3673). PMLR.
Rush, C. (2020, June).
Credit Risk, Liquidity, and Bubbles. International Review of Finance, 20(3), 737-746.
Jarrow, R., & Protter, P. (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).
Joint estimation of parameters in Ising model. The Annals of Statistics, 48(2), 785-810.
Ghosal, P., & Mukherjee, S. (2020).
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).
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).
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).
Self-Tuning Bandits over Unknown Covariate-Shifts. In Algorithmic Learning Theory (pp. 1114-1156). PMLR.
Suk, J., & Kpotufe, S. (2021, March).
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).
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).
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).
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).
High-frequency analysis of parabolic stochastic PDEs. The Annals of Statistics, 48(2), 1143-1167.
Chong, C. (2020).
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).
A Proxy Variable View of Shared Confounding. In International Conference on Machine Learning (pp. 10697-10707). PMLR.
Wang, Y., & Blei, D. (2021, July).
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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