Contact information:
Name: Carlos J. Soto
Email Address: carlossoto@umass.edu
My Links:
ORCID: 0000-0003-0645-5770
My research interests lie at the intersection of geometry and statistics. This would include statistical shape analysis, manifold methods, differential privacy, and functional data analysis. I am currently interested in Reproducing Kernel Hilbert spaces, Lie Groups, and regression methods on manifoldss.
- Preprint: Carlos Soto. Rao Differential Privacy. arxiv, 2025 --
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- Preprint: Aditya Kulkarni and Carlos Soto. Differentially Private Geodesic and Linear Regression. arxiv, 2025 --
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- Carlos Soto, Matthew Reimherr, Aleksandra Slavkovic, and Mark Shriver. Differentially Private Human Faces via Radial Curve Representation. International Conference on Learning Representations, 2025 --
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- 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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