About

UPDATE: As of March 2022, I have defended my PhD, and I am now happily employed at D. E. Shaw in quant equity trading. Please do not contact me with unsolicited job offers.

At Columbia, my PhD research focused on statistical machine learning, data mining, and bioinformatics. I was advised by professor John Cunningham. Prior to Columbia, I studied Mathematics and Statistics at Oxford, and I spent a couple of years working in quant finance and actuarial science.

Software

I have built and maintain R and Python implementations of CoDaCoRe, which can be found on CRAN. For any questions or comments related to these, please drop me a line at eg2912@columbia.edu.

Publications

Authors shown in the order as published (lead author listed first).

  • Elliott Gordon-Rodriguez, Thomas P. Quinn, and John P. Cunningham. “Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome.” Neural Information Processing Systems (NeurIPS), 2022. [paper] [poster]

  • Elliott Gordon-Rodriguez. “Advances in Machine Learning for Compositional Data.” Doctoral Dissertation, Columbia University, 2022. [download]

  • Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Andres Potapczynski, and John Cunningham. “On the Normalizing Constant of the Continuous Categorical Distribution.” arXiv, 2022. [paper]

  • Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, and John P. Cunningham. “On a novel probability distribution for zero-laden compositional data.” Proceedings of CoDaWork, 2022. [paper]

  • Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, and John P. Cunningham. “Fast log-ratio selection for high-dimensional compositional data.” CoDaWork Abstracts, 2022.

  • Elliott Gordon-Rodriguez, Thom Quinn, and John Cunningham. “Learning Sparse Log-Ratios for High-Throughput Sequencing Data.” Bioinformatics, 2022. [paper]

  • Elliott Gordon-Rodriguez. “On Disentanglement and Mutual Information in Semi-Supervised Variational Auto-Encoders.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021. [paper]

  • Thom Quinn, Elliott Gordon-Rodriguez, and Ionas Erb. “A critique of differential abundance analysis, and advocacy for an alternative.” arXiv, 2021. [paper]

  • Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Geoff Pleiss and John Cunningham. “Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning.” ICBINB workshop, Neural Information Processing Systems (NeurIPS), 2020. [paper] [poster]

  • Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, and John Cunningham. ‘‘The Continuous Categorical: a Novel Simplex-Valued Exponential Family." International Conference on Machine Learning (ICML), 2020. [paper] [poster]

Miscellaneous

I have written up a cheat sheet for statistical inference, as well as a set of solution keys for the corresponding PhD qualifying exams at Columbia (1990-2017). Credit to my colleague Wenda Zhou for helping to crack the hardest of these.