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Psychometrics Workshop - September 22-23, 2023
Psychometrics Workshop – September 22-23, 2023

This psychometrics workshop presents research talks on statistical methods (such as latent variable models and graphical models) for complex and heterogeneous data in psychological and educational measurement.  ...

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In Memoriam – Tze Leung Lai
In Memoriam – Tze Leung Lai

With deep regret and sadness, we share the passing of Tze Leung Lai on Sunday, May 21st. Tze came to our department in 1968 to pursue his doctoral studies under David Siegmund and graduated in 1971....

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In Memoriam – David H. Krantz
In Memoriam – David H. Krantz

With deep regret and sadness, we share the passing of David H. Krantz on Saturday, May 20th. During the most challenging years of our department, when the department was unstable and small, Dave was our most potent protector and advocate on campus....

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Congratulations to our Phd Alum, Adji Bousso Dieng on being awarded the Graduate School of Arts and Sciences (GSAS) 2023 Dean’s Award for the Outstanding Recent Alumni Award.
Congratulations to our Phd Alum, Adji Bousso Dieng on being awarded the Graduate School of Arts and Sciences (GSAS) 2023 Dean’s Award for the Outstanding Recent Alumni Award.

We proudly announce that Columbia University Department of Statistics PhD Graduate Class of 2020, Adji Bousso Dieng, Assistant Professor of Computer Science, Associated Faculty in Chemical and Biological Engineering, Associated Faculty in the Materials Institute, and Associated Faculty in High-Meadows Environmental Institute at Princeton University, has been awarded the prestigious Graduate School of Arts and Sciences (GSAS) 2023 Dean’s Award for the Outstanding Recent Alumni Award. ...

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Congratulations to Anne van Delft and Andrew Blumberg on being awarded an NSF Grant
Congratulations to Anne van Delft and Andrew Blumberg on being awarded an NSF Grant

Congratulations to Anne van Delft, Assistant Professor of Statistics, and IICD Faculty Andrew Blumberg, Herbert, and Florence Irving, Professor of Cancer Data Research and Professor of Mathematics and Computer Science, who received an NSF award for their project about a statistical framework for the analysis of the evolution in shape and topological structure of random objects....

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Statistics Department Summer Courses Cut Across Disciplines
Statistics Department Summer Courses Cut Across Disciplines

The Columbia University Department of Statistics is offering two sections of STAT GR8201 for Summer 2023, weekdays beginning July 17, 2023 and running through July 28, 2023, which will be held in the School of Social Work, Room 903, with guest faculty Professor Fan Li (Duke University) and Professor Arthur Gretton (University College London)....

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Congratulations to Marcel Nutz on being named Fellow of the Institute of Mathematical Statistics (IMS).
Congratulations to Marcel Nutz on being named Fellow of the Institute of Mathematical Statistics (IMS).

Marcel F. Nutz, Professor, Columbia University, has been named Fellow of the Institute of Mathematical Statistics (IMS).  Dr. Nutz received the award for outstanding contributions to probability, in particular to optimal transport, stochastic analysis, and mathematical finance; and for dedicated service to the profession....

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Congratulations to Maria-Cristiana (Kitty) Girjau, who was recently awarded the Center for Teaching and Learning's (CTL) Lead Teaching Fellow (LTF) Award for the 2023-2024 academic year.
Congratulations to Maria-Cristiana (Kitty) Girjau, who was recently awarded the Center for Teaching and Learning’s (CTL) Lead Teaching Fellow (LTF) Award for the 2023-2024 academic year.

Congratulations to Maria-Cristiana (Kitty) Girjau, who was recently awarded the Center for Teaching and Learning’s (CTL) Lead Teaching Fellow (LTF) Award for the 2023-2024 academic year!...

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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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