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Congratulations to teaching awards recipients named by the Department of Statistics at Columbia University, Dr. Jiajin Sun & Dr. Gan Yuan!
Congratulations to teaching awards recipients named by the Department of Statistics at Columbia University, Dr. Jiajin Sun & Dr. Gan Yuan!

 

 

 

Teaching Awards Recipients Named by the Department of Statistics at Columbia University

The Columbia University Department of Statistics is thrilled to announce the 2024 PhD student departmental awards recipients....

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Congratulations to Professor Samory Kpotufe on his promotion to full Professor!
Congratulations to Professor Samory Kpotufe on his promotion to full Professor!

Congratulations to Professor Samory Kpotufe on his well-deserved promotion to full Professor! The university’s trustees recently approved this significant milestone. We are thrilled to celebrate Professor Kpotufe’s achievement as a valued department member....

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Congratulations to Howard Levene Assistant Professor Marco Avella Medina for being named a Spring 2024 Junior Faculty Grants Awardee.
Congratulations to Howard Levene Assistant Professor Marco Avella Medina for being named a Spring 2024 Junior Faculty Grants Awardee.
...

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Congratulations to Dr. Genevera Allen for being named an Institute of Mathematical Statistics Fellow.
Congratulations to Dr. Genevera Allen for being named an Institute of Mathematical Statistics Fellow.

Dr. Genevera Allen has been named an IMS fellow.

Congratulations to Dr. Allen for being named an Institute of Mathematical Statistics Fellow....

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Probability Theory, Sequential Analysis and Adaptive Methods Conference in Memory Of Professors Y-S Chow and TL Lai - Friday, May 3, 2024 through Saturday, May 4, 2024
Probability Theory, Sequential Analysis and Adaptive Methods Conference in Memory Of Professors Y-S Chow and TL Lai – Friday, May 3, 2024 through Saturday, May 4, 2024

Click here for conference webpage

Probability Theory, Sequential Analysis and Adaptive Methods Conference in Memory Of Professors Y-S Chow and TL Lai

Friday, May 3, 2024 through Saturday, May 4, 2024

Conference Venue

1255 Amsterdam Avenue, Room C03 (underground level in the School of Social Work building), New York, NY 10027 (Directions here)...

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Congratulations to David Blei on being awarded the Lenfest Distinguished Faculty Award
Congratulations to David Blei on being awarded the Lenfest Distinguished Faculty Award

David Blei, Professor of Statistics and Computer Science, Departments of Statistics and Computer Science

For his groundbreaking scholarship in machine learning and artificial intelligence, around which he has built a flourishing research group at Columbia and a thriving intellectual community across the city, and for his tireless mentorship and teaching of students in the Departments of Statistics and Computer Science. ...

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Department of Statistics Doctoral Student Highlight Series: William Knauth
Department of Statistics Doctoral Student Highlight Series: William Knauth

Department of Statistics Doctoral Student Highlight Series: William Knauth

What are the odds that Schubert’s sonatas, sport airplane piloting, linear neural networks, and the 2023 AT&T Byron Nelson Golf Tournament Award would all have something in common?...

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Kevin Jiang, Best Use of Statistical Innovations Award - Fall 2023
Kevin Jiang, Best Use of Statistical Innovations Award – Fall 2023

Congratulations to Kevin Fu Jiang, a mathematics-statistics major in Columbia College, on winning the “Best Use of Statistical Innovations” Award at the 2023 Undergraduate Computer and Data Science Research Fair....

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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
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DEPARTMENT OF STATISTICS
Columbia University
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