Robert Michael Weylandt

Asst Professor

Zicklin School of Business

Department: Paul Chook Dept InfoSys & Stat

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Michael Weylandt is an Assistant Professor in the Paul H. Chook Department of Information Systems and Statistics at the Zicklin School of Business, Baruch College, CUNY. Before joining Baruch, he held postdoctoral positions at Sandia National Laboratories and through the US Intelligence Community Postdoctoral Fellowship. His work has been recognized with best paper awards from the American Statistical Association in both Statistical Learning and Data Science and in Business & Economic Statistics. He has served as a mentor in the Google Summer of Code program for 7 years on behalf of the R Foundation for Statistical Computing and previously held an NSF Graduate Research Fellowship. He received a Bachelor’s of Science in Engineering from Princeton University in 2008 and a Ph.D. in Statistics from Rice University in 2020, where he was supervised by Katherine B. Ensor.


Ph.D., Statistics, William Marsh Rice University Houston United States

M.A., Statistics, William Marsh Rice University Houston United States

B.Sc., Operations Research and Financial Engineering, Princeton University Princeton United States

Journal Articles

Weylandt, M., & Swiler, L. P. (2024). Beyond PCA: Additional Dimension Reduction Techniques to Consider in the Development of Climate Fingerprints. Journal of Climate, 37(5). 1723--1735.

Lehoucq, R. B., Weylandt, M., & Berry, J. W. (2024). Optimal accuracy for linear sets of equations with the graph Laplacian. ArXiv Pre-Print 2405.07877,

(2022). Debiasing Projections for Fair Principal Components Analysis.

(2022). To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier. ArXiv Pre-Print 2206.00074,

(2022). A Coupled CP Decomposition for Principal Components Analysis of Symmetric Networks. ArXiv Pre-Print 2202.04719,

(2021). Ecological correlates of reproductive status in a guild of Afrotropical understory trees. BioRXiv Pre-Print,

(2021). HepatoScore14: Measures of Biological Heterogenity Significantly Improve Prediction of Hepatocellular Carcinoma Risk. Hepatology, 73(6). 2278-2292.

Weylandt, M., Nagorski, J., & Allen, G. I. (2020). Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization. Journal of Computational and Graphical Statistics, 29(1). 87-96.

(2019). Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility. ArXiv Pre-Print 1907.10152,

Conference Proceedings

Weylandt, M., Roddenberry, T. M., & Allen, G. I. (2021). Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering. DSLW 2021: Proceedings of the IEEE Data Science and Learning Workshop 2021.

Navarro, M., Allen, G. I., & Weylandt, M. (2021). Network Clustering for Latent State and Changepoint Detection. ArXiv Pre-Print 2111.01273.

Weylandt, M., Michailidis, G., & Roddenberry, T. M. (2021). Sparse Partial Least Squares for Coarse Noisy Graph Alignment. SSP 2021: Proceedings of the 2021 IEEE Statistical Signal Processing Workshop.

Weylandt, M., & Michailidis, G. (2021). Automatic Registration and Convex Clustering of Time Series. ICASSP 2021: Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing.

Weylandt, M. (2019). Multi-Rank Sparse and Functional PCA: Manifold Optimization and Iterative Deflation Techniques. CAMSAP 2019: Proceedings of the 8th IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing.

Weylandt, M. (2019). Splitting Methods For Convex Bi-Clustering And Co-Clustering. DSW 2019: Proceedings of the IEEE Data Science Workshop 2019.

Allen, G. I., & Weylandt, M. (2019). Sparse and Functional Principal Components Analysis. DSW 2019: Proceedings of the IEEE Data Science Workshop 2019.