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. Under its new Harnessing Data Science for Health Discovery and Innovation in Africa (DS-I Africa)program, the NIH announced 19 awards to research partners in the U.S. and globally, including one to support a project led by scientists at Columbia University Mailman School of Public Health.

The initiative at Columbia, led by Professor Kiros Berhane, Cynthia and Robert Citron-Roslyn and Leslie Goldstein Professor of Biostatistics and the chair of the Department of Biostatistics at the Mailman School of Public Health, will be carried out in collaboration with the Data Science Institute and Department of Statistics at Columbia University, Schools of Public Health and Information Science at Addis Ababa University in Ethiopia, and the Schools of Public Health and School of Mathematics at University of Nairobi in Kenya. The Columbia Global Center in Nairobi will serve as a regional hub for key grant-related activities.

Samory K. Kpotufe and Tian Zheng  are representing the department in this effort. 

To read more please read the official press release: https://www.publichealth.columbia.edu/public-health-now/news/columbia-mailman-joins-nih-effort-bolster-data-science-africa

 

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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.
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Privacy-preserving parametric inference: a case for robust statistics. Journal of the American Statistical Association, 116(534), 969-983.
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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
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A note on the Screaming Toes game. arXiv preprint arXiv:2006.04805.
Tavaré, S. (2020).
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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).
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Multivariate rank-based distribution-free nonparametric testing using measure transportation. Journal of the American Statistical Association, (just-accepted), 1-45.
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Credit Risk, Liquidity, and Bubbles. International Review of Finance, 20(3), 737-746.
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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.
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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.
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On the bias and variance of odds ratio, relative risk and false discovery proportion. Communications in Statistics-Theory and Methods, 1-31.
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The continuous categorical: a novel simplex-valued exponential family. In International Conference on Machine Learning (pp. 3637-3647). PMLR.
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High-frequency analysis of parabolic stochastic PDEs. The Annals of Statistics, 48(2), 1143-1167.
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