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Columbia Home

Department of Statistics
Columbia University in the City of New York
Toggle navigation
Department of Statistics
Columbia University
 * About
   * About Us
   * Department History
   * Diversity, Equity, and Inclusion
   * Department News
   * Howard Levene Outstanding Teaching Award
   * Minghui Yu Teaching Assistant Award
   * Department Directory
   * Department Calendar
   * Location & Directions
   * Contact Us
 * Research
   * Founder’s Postdoc Fellows
   * Focus Series
   * Recent Summer Topic Courses
   * Recent Ph.D. Dissertations
   * MA2PHD
   * Research Experiences for Undergraduates
   * Minghui Yu Memorial Conference
   * Centers
     * Applied Statistics Center
     * Center for Applied Probability
     * Grossman Center
   * Research Computing
   * Consulting Information
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 * Programs
   * Undergraduate Programs
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   * Ph.D. Program
   * Certificate Program
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   * MA in Statistiscs option for GSAS PhDs
   * Bridge to the Ph.D. Program in STEM
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 * Courses
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   * Robust Statistics and Privacy Workshop (October 5-6)
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Search

Previous
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....
Read More
Faculty Positions starting 2023 and 2024
Department of Statistics at Columbia University Positions Assistant Professor
(Limited Term): Starting Fall 2023 – Review begins on December 1, 2022 and will
continue until the position is filled Lecturer in Discipline: Starting Fall 2023
– Review begins on February 1, 2023 and will continue until the position is
filled....
Read More
Congratulations to our Phd Alum, Yang Kang, on being awarded the 2023 Applied
Probability Society Best Publication Award.
We proudly announce that Columbia University Department of Statistics PhD
Graduate Class of 2018, Yang Kang, has been awarded the prestigious 2023 Applied
Probability Society Best Publication Award....
Read More
Congratulations to Oliva Bobrownicki, a statistics student from Barnard, for
winning second place in the national USCLAP (The Undergraduate Class Project
Competition).
...
Read More
Robust Statistics and Privacy Workshop – Thursday, October 5th – Friday, October
6th
The goal of this workshop will be to present recent developments in robust
statistics and differential privacy, including algorithmic foundations,
concentration results, inference and applications to high dimensional statistics
and machine learning....
Read More
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.  ...
Read More
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....
Read More
Faculty Positions starting 2023 and 2024
Department of Statistics at Columbia University Positions Assistant Professor
(Limited Term): Starting Fall 2023 – Review begins on December 1, 2022 and will
continue until the position is filled Lecturer in Discipline: Starting Fall 2023
– Review begins on February 1, 2023 and will continue until the position is
filled....
Read More
Congratulations to our Phd Alum, Yang Kang, on being awarded the 2023 Applied
Probability Society Best Publication Award.
We proudly announce that Columbia University Department of Statistics PhD
Graduate Class of 2018, Yang Kang, has been awarded the prestigious 2023 Applied
Probability Society Best Publication Award....
Read More
Congratulations to Oliva Bobrownicki, a statistics student from Barnard, for
winning second place in the national USCLAP (The Undergraduate Class Project
Competition).
...
Read More
Robust Statistics and Privacy Workshop – Thursday, October 5th – Friday, October
6th
The goal of this workshop will be to present recent developments in robust
statistics and differential privacy, including algorithmic foundations,
concentration results, inference and applications to high dimensional statistics
and machine learning....
Read More
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.  ...
Read More
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....
Read More
Next
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NEWS

   
 * Faculty Positions starting 2023 and 2024
 * Congratulations to our Phd Alum, Yang Kang, on being awarded the 2023 Applied
   Probability Society Best Publication Award.
 * Congratulations to Oliva Bobrownicki, a statistics student from Barnard, for
   winning second place in the national USCLAP (The Undergraduate Class...
 * Robust Statistics and Privacy Workshop – Thursday, October 5th – Friday,
   October 6th

More news


M.A. PROGRAMS

The Statistics Department offers a flexible on-campus M.A. program designed for
students preparing for professional positions or for doctoral programs in
statistics and other quantitative fields.



Stat GR5399: Statistical Fieldwork

Learn more


PH.D. PROGRAM

The PhD program prepares students for research careers in probability and
statistics in both academia and industry. The first year of the program is
devoted to training in theoretical statistics, applied statistics, and
probability. In the following years, students take advanced topics courses and
seminars.



Ph.D. Alumni

Howard Levene Outstanding Teaching Award

Minghui Yu Teaching Assistant Award

Learn more


UNDERGRADUATE PROGRAMS

The Statistics major builds on a foundation in probability and statistical
theory to provide practical training in statistical methods, study design, and
data analysis.

BA/MA Program

Research Experiences for Undergraduates

Learn more


QUICK LINKS

 * Academic Programs
   * Undergraduate Programs
   * M.A. Statistics Programs
   * M.A. in Mathematical Finance
   * M.S. in Actuarial Science
   * M.A. in Quantitative Methods in the Social Sciences
   * M.S. in Data Science
   * Ph.D. Program
   * BA/MA Program
 * Department Directory
 * Hiring
   * Faculty Positions
   * Founder’s Postdoctoral Fellowship Positions
   * Staff Hiring
   * Joint Postdoc with Data Science Institute
 * Seminars
 * Department News
 * Department Calendar
 * Research Computing
 * Help Room
 * Contact Us


UPCOMING EVENTS




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
Columbia University
In the City of New York
116th Street and Broadway, New York, NY 10027
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DEPARTMENT OF STATISTICS
Columbia University
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New York, NY 10027

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Fax: 212.851.2164

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