News
Department of Statistics is to participate in a new NIH DS-I Africa award to Columbia University.
Department of Statistics is to participate in a new NIH DS-I Africa award to Columbia University.

The Department of Statistics is proud to participate in a new NIH DS-I Africa award to Columbia University.

The National Institutes of Health is investing about $74.5 million over five years to advance data science, catalyze innovation, and spur health discoveries across Africa....

Read more

Congratulations to Yixin Wang on being selected as one of the winners of the 2021 Blackwell-Rosenbluth Award.
Congratulations to Yixin Wang on being selected as one of the winners of the 2021 Blackwell-Rosenbluth Award.

Congratulations to Yixin Wang on being selected as one of the winners of the 2021 Blackwell-Rosenbluth Award.

The winners will present their work at the JB^3 webinar series on November 10 (Trevor, Daniel and Yixin) and 12 (Marta, Samuel and Dootika) at 1pm UTC hosted by Bocconi and Universidad Nacional Autónoma de México (UNAM), respectively....

Read more

Introducing Diahmin Hawkins, Our 1st  Bridge to Ph.D. in STEM Scholar
Introducing Diahmin Hawkins, Our 1st  Bridge to Ph.D. in STEM Scholar

Introducing Diahmin Hawkins, Our 1st  Bridge to Ph.D. in STEM Scholar

A native of Birmingham, Alabama, Diahmin graduated Magna Cum Laude with a B.S....

Read more

Congratulations to Mark Hansen and Wayne Lee on their Modern Statistics and the Modern Newsroom grant.
Congratulations to Mark Hansen and Wayne Lee on their Modern Statistics and the Modern Newsroom grant.

Congratulations to Mark Hansen and Wayne Lee on their Modern Statistics and the Modern Newsroom grant.

Over the next year, they will create a course joint with Columbia Journalism School and the Department of Statistics....

Read more

Introducing our new Faculty member Yuqi Gu
Introducing our new Faculty member Yuqi Gu

Yuqi Gu joined the Department of Statistics at Columbia University as a tenure-track Assistant Professor in July 2021. Prior to this, she spent a year from 2020 to 2021 as a postdoctoral researcher at Duke University, under the supervision of David B....

Read more

Join our upcoming virtual PhD and MA Admission Information Sessions.
Join our upcoming virtual PhD and MA Admission Information Sessions.

Join our upcoming virtual PhD and MA Admission Information Sessions. Faculty, students, and staff will discuss the curriculum, application process, research, and funding opportunities available to aspiring graduate students. ...

Read more

Statistics Open House - Friday, Oct. 29
Statistics Open House – Friday, Oct. 29
Join our upcoming virtual Statistics Open House Friday, Oct. 29. from 1-3 pm.  BA/MA, MA, and Ph.D. faculty, students, and staff will discuss the curriculum, application process, research, and funding opportunities available to undergraduates and aspiring graduate students.    RSVP here....

Read more

Summer Readings - Selected Faculty Publications 2020 - 2021
Summer Readings – Selected Faculty Publications 2020 – 2021
​Click here for a list of Summer Readings – Selected Faculty Publications 2020 – 202...

Read more

« Previous Page
Next Page »

Quick Links

Upcoming Events

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
Columbia University
In the City of New York
116th Street and Broadway, New York, NY 10027
© 2014 Columbia University

Website by Sunray Computer
DEPARTMENT OF STATISTICS
Columbia University
Room 1005 SSW, MC 4690
1255 Amsterdam Avenue
New York, NY 10027

Phone: 212.851.2132
Fax: 212.851.2164

Department of Statistics on LinkedIn