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Congratulations to Diahmin Hawkins on being awarded a five-year NSF Graduate Research Fellowship.
Congratulations to Diahmin Hawkins on being awarded a five-year NSF Graduate Research Fellowship.

Congratulations to Diahmin Hawkins on being awarded a five-year NSF Graduate Research Fellowship.

The NSF Graduate Research Fellowship Program Award ensures the quality, vitality, and diversity of scientific and engineering careers in the US, and is a catalyst to broadening participation in science and engineering of historically underrepresented groups, including women, minorities, persons with disabilities, and veterans....

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Public-Interest Technology Data Science Corps Students and Mentors Publish Study on NYC Parking Tickets
Public-Interest Technology Data Science Corps Students and Mentors Publish Study on NYC Parking Tickets

Led and mentored by PhD Alum, Dr. Jonathan Auerbach and Dr. Owen Ward, a team of PIT-DSC students carried a study on parking tickets in Manhattan that sought to answer the question: ”Does it pay to park in front of a fire hydrant?”...

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Congratulations to Bianca Dumitrascu on being named one of the recipients of the 2023 DSI Seed Funds Program.
Congratulations to Bianca Dumitrascu on being named one of the recipients of the 2023 DSI Seed Funds Program.

The Data Science Institute’s Seed Funds Program supports new collaborations with the goal of developing longer-term and deeper interdisciplinary relationships among faculty at Columbia....

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Professor Richard Davis
Extremes and Time Series: A Workshop on the Occasion of Richard Davis’ 70th Birthday

The conference will be held at Columbia University on January 20 and January 21, 2023. The event will feature presentations by leading experts in the statistical analysis of extremes and time series....

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Congratulations to Ye Tian on receiving the ICSDS student travel award.
Congratulations to Ye Tian on receiving the ICSDS student travel award.

The International Conference on Statistics and Data Science (ICSDS) award committee is pleased to announce that the following 10 PhD students are awarded 2022 ICSDS Student Travel Awards....

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Founder's Postdoctoral Fellowship in Statistics: Starting Fall 2023
Founder’s Postdoctoral Fellowship in Statistics: Starting Fall 2023

This fellowship aims to develop the next generation of leaders in statistics and related disciplines by offering a unique opportunity for the best young scholars to develop their research expertise and collaborate with Columbia faculty both within the department and in the broader Columbia community....

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Congratulations to Amal Moussa on being named one of the Top 10 Female Quants on Wall Street 2022 by Rebellion Research.
Congratulations to Amal Moussa on being named one of the Top 10 Female Quants on Wall Street 2022 by Rebellion Research.

, Adjunct Professor in , Columbia Ph.D. graduate, and Goldman Sachs derivatives trader, has been named one of the Top 10 Female Quants on Wall Street 2022 by Rebellion Research....

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Meet Our New Faculty
Meet Our New Faculty
We are excited to welcome our new faculty members: Alexander Clark, Ph.D. Adjunct Assistant Professor, Gokce Dayanikli, Ph.D., Assistant Professor, Bianca Dumitrascu, Ph.D., Assistant Professor, Musa Elbulok, Ph.D., Adjunct Assistant Professor, Yongchan Kwon, Ph.D., Assistant Professor, Dobrin Marchev, Ph.D., Lecturer in Discipline, Alex Pijyan, Ph.D., Lecturer in Discipline, Franz Herbert Rembart, Ph.D., Adjunct Assistant Professor, Chenyang Zhong, Ph.D., Assistant Professor 

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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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