Carlos is interested in information geometry. This would include statistical shape analysis, manifold methods, differential privacy, and functional data analysis. He enjoys theoretical challenges as well as computational implementation.

• Carlos Soto, Karthik Bharath, Matthew Reimherr, and Aleksandra Slavkovic. Shape And Structure Preserving Differential Privacy. Advances in Neural Information Processing Systems, 35, 2022 --

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• Carlos Soto, Darshan Bryner, Audrey Dalgarno, Nicola Neretti, and Anuj Srivastava. TADBay: A Bayesian construction of topologically associated domains To be published in: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages , 2022 --

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• Carlos Soto, Audrey Dalgarno, Darshan Bryner, Benjamin McLaughlin, Nicola Neretti, and Anuj Srivastava. Representation of chromosome conformations using a shape alphabet across modeling methods. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 151-156, 2021 --

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• Carlos Soto, Darshan Bryner, Nicola Neretti, and Anuj Srivastava. Toward a three-dimensional chromosome shape alphabet. Journal of Computational Biology, 2021 --

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• Carlos J Soto, Peiyao A Zhao, Kyle N Klein, David M Gilbert, and Anuj Srivastava. Statistical comparisons of chromosomal shape populations.In 2021 IEEE 18th International Symposium on Biomedical Imaging(ISBI), pages 788 - 791. IEEE, 2021 --

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• Matthew Reimherr, Karthik Bharath, and Carlos Soto. Differential privacy over riemannian manifolds. Advances in Neural Information Processing Systems, 34, 2021 --

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• Jose Cordova, Carlos Soto, Mostafa Gilanifar, Yuxun Zhou, Anuj Srivastava, and Reza Arghandeh. Shape preserving incremental learning for power systems fault detection. IEEE control systems letters, 3(1): 85 - 90, 2018 --

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