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

Amal Moussa, Adjunct Professor in Columbia University’s Mathematics of Finance MA (MAFN) program, 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. Congratulations Amal!

Recent Faculty Publications

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).

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)

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).

Revisiting colocalization via optimal transport. Nature Computational Science, 1(3), 177-178.

Wang, S., & Yuan, M. (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

Asset pricing with general transaction costs: Theory and numerics. Mathematical Finance, 31(2), 595-648.

Gonon, L., Muhle‐Karbe, J., & Shi, X. (2021).

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).

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).

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).

Self-Tuning Bandits over Unknown Covariate-Shifts. In Algorithmic Learning Theory (pp. 1114-1156). PMLR.

Suk, J., & Kpotufe, S. (2021, March).

Credit Risk, Liquidity, and Bubbles. International Review of Finance, 20(3), 737-746.

Jarrow, R., & Protter, P. (2020).

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).

Boosted Sparse Non-linear Distance Metric Learning

Yuting Ma and Tian Zheng

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