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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...

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Ph.D. candidate Casey Bradshaw solves student puzzle corner #33 in the August 2021 issue of the IMS Bulletin
Ph.D. candidate Casey Bradshaw solves student puzzle corner #33 in the August 2021 issue of the IMS Bulletin

Ph.D. candidate Casey Bradshaw solves student puzzle corner #33 in the August issue of the IMS Bulletin...

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Introducing the Summer 2021 Undergraduate Interns
Introducing the Summer 2021 Undergraduate Interns

The Department is proud to introduce our summer interns. Six Columbia University undergraduate students are working with select faculty on multiple projects. ...

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The Public Interest Technology Data Science Corps (PIT-DSC), Welcomes its Inaugural Cohort 
The Public Interest Technology Data Science Corps (PIT-DSC), Welcomes its Inaugural Cohort 

Funded by Public Interest Technology – University Network of New America, the Public Interest Technology Data Science Corps (PIT-DSC), a collaboration between the Department of Statistics, Columbia University, and Lehman College, welcomed our inaugural cohort this week....

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Professors Richard. A. Davis and Jingchen Liu complete the IMS Annals Quadfecta
Professors Richard. A. Davis and Jingchen Liu complete the IMS Annals Quadfecta

The Institute for Mathematical Statistics (IMS) produces four flagship “Annals” research journals: The Annals of Statistics, of Probability, of Applied Probability, and of Applied Statistics....

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Congratulations to our 2021 Ph.D. Graduates!
Congratulations to our 2021 Ph.D. Graduates!

Congratulations To Our 2021 Ph.D. Graduates! Ph.D. Students Successfully Defend Their Doctoral Dissertations.

Click here to view a list of our 2021 Ph.D....

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Celebrating Exceptional Women During Math and Statistics Awareness Month
Celebrating Exceptional Women During Math and Statistics Awareness Month

This year’s Math and Statistics Awareness Month (2021) is upon us. With that in mind, the team at  OnlineEngineeringPrograms.com is highlighting a great guide we published in honor of it titled Celebrating Exceptional Women During Math and Statistics Awareness Month which includes an interview with Ruth Charney, PhD, President of the American Mathematical Society (AMS). ...

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Using Big Data to Map Forests, Tree by Tree by Professor Tian Zheng
Using Big Data to Map Forests, Tree by Tree by Professor Tian Zheng

Tropical forests are made up of hundreds of tree species. Knowing what forests are made of at the individual tree level can unlock many secrets, not least how forests respond to the effects of climate change....

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Recent Faculty Publications

Privacy-preserving parametric inference: a case for robust statistics. Journal of the American Statistical Association, 116(534), 969-983.
Avella-Medina, M. (2021).
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.
avaré, 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
Stabilized Sparse Online Learning for Sparse Data
Yuting Ma, Tian Zheng
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
Boosted Sparse Non-linear Distance Metric Learning
Yuting Ma and Tian Zheng
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