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Faculty and Post-doc positions available in the Department of Statistics
Faculty and Post-doc positions available in the Department of Statistics

Department of Statistics

Columbia University

Faculty Position Starting Fall 2018

The Department of Statistics invites applications for a tenure-track Assistant Professor position to begin July 1, 2018....

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Professors Lo and Zheng receives NSF funding for Big Data Research
Professors Lo and Zheng receives NSF funding for Big Data Research

Congratulations to Professor Shaw-Hwa Lo (PI) and Professor Tian Zheng (co-PI) for receiving a $900,000 NSF BIGDATA grant. For this award, the team, led by Professor Lo, will study statistical foundations for measuring predictivity and collaborate with New York City’s Department of Transportation on predictive problems in traffic safety. 

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Conference on Causal Inference in Longitudinal Studies - September 21 - 23, 2017
Conference on Causal Inference in Longitudinal Studies – September 21 – 23, 2017
Conference on Causal Inference in Longitudinal Studies September 21-23, 2017 Columbia University Organizing Committee: Andrew Gelman, Michael Sobel, Jose Zubizaretta DATE: September 21 – 23 LOCATION: Union Theological Seminary Conference Program

 

Thursday, September 21, 2017: tutorial

14:15 15:00 Miguel Hernan (Harvard University)

15:00 15:45 Miguel Hernan

15:45 16:00 Coffee break

16:00 16:45 Miguel Hernan

16:45 17:30 Miguel Hernan

Friday, September 22, 2017: research talks

9:00 9:15 Richard Davis/David Madigan (Columbia University)

9:15 10:00 Jamie Robins (Harvard University)

10:00 10:20 Coffee break

10:20 11:05 Fabrizia Mealli (University of Florence)

11:05 11:50 Stijn Vansteelandt (Ghent University)

11:50 14:00 Lunch break

14:00 14:45 Daniel Almirall (University of Michigan)

14:45 15:30 Mark vander Laan (UC Berkeley)

15:30 15:50 Coffee break

15:50 16:35 Miguel Hernan (Harvard University)

16:35 17:20 Alexander Luedtke (Fred Hutchinson Cancer Research

Center)

18:00 Dinner for speakers

Saturday, September 23, 2017: research talks

9:00 9:45 Thomas Richardson (University of Washington)

9:45 10:30 Eric Laber (NC State University)

10:30 10:50 Coffee break

10:50 11:35 Mats Stensrud (University of Oslo)

11:35 12:20 Wenbin Lu (NC State University)...

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David Blei, Professor of Statistics and Computer Science at Columbia University, has been named Fellow of the Institute of Mathematical Statistics (IMS).
David Blei, Professor of Statistics and Computer Science at Columbia University, has been named Fellow of the Institute of Mathematical Statistics (IMS).

Press Release PDF...

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Special Focus Conference Series on Statistical Methods for Psychological and Educational Measurement
Special Focus Conference Series on Statistical Methods for Psychological and Educational Measurement

Conference Website

Conference Program Book

2017-2018 Special Focus Conference Series on Statistical Methods for Psychological and Educational Measurement of the Department of Statistics at Columbia University

You are invited to participate in the 2017-2018 Special Focus Conference Series on Statistical Methods for Psychological and Educational Measurement of the Department of Statistics at Columbia University on Saturday, August 5, 2017, in the Department of Statistics at Columbia University....

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Columbia Statistics Club (CSC) and ASA – Spring Datafest 2017 – including Professors Zheng, Robbins; representation from Google, Ladders

Spring Datafest 2017...

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Congratulations! Professor Tian Zheng receives The Columbia University Presidential Teaching Award
Congratulations! Professor Tian Zheng receives The Columbia University Presidential Teaching Award

Professor Tian Zheng receives The Presidential Teaching Award 

Professor Zheng wins the highest teaching honor at Columbia University:  The Presidential Teaching Award....

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Causal Inference Conference - Saturday, May 6, 2017
Causal Inference Conference – Saturday, May 6, 2017

https://www.eventbrite.com/e/columbia-university-causal-inference-conference-varying-treatment-effects-tickets-33553158400

This is the second of the three causal inference conferences sponsored by the Columbia University Statistics Department this 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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