Victor Chardès, Ph.D.
I'm a Flatiron Research Fellow in the Center for Computational Biology at the Flatiron Institute, interested in reverse engineering the intricate array of regulatory processes that drive cellular life. My current research focuses on inferring, from omics data, biophysically motivated models describing the expression of genes in cells. I am happy to use recent advances from generative AI, as long as they are a good tools to infer robust, interpretable models of cellular processes. In this direction, I also aim to build assumption-free methods to denoise single-cell RNA-seq data. Download my CV.
Publications
- Chardès, V. (2025). Random Matrix Theory-guided sparse PCA for single-cell RNA-seq data. arXiv preprint 2509.1542 [pdf].
- Zhang, S., Maddu, S., Qiu, X., Chardès, V. (2025). Inferring stochastic dynamics with growth from cross-sectional data. Accepted at NeurIPS 2025 [pdf].
- Maddu, S.*, Chardès, V.*, Shelley, M. (2025). Learning stochastic processes with intrinsic noise from cross-sectional biological data. Proceedings of the National Academy of Sciences, 122 (37) e2420621122 [article, pdf].
- Chardès, V.*, Maddu, S.*, & Shelley, M. J. (2023). Stochastic force inference via density estimation. NeurIPS 2023 AI for Science Workshop [pdf].
- Chardès, V.*, Mazzolini, A.*, Mora, T., & Walczak, A. M. (2023). Evolutionary stability of antigenically escaping viruses. Proceedings of the National Academy of Sciences, 120(44), e2307712120 [article, pdf].
- Chardès, V., Vergassola, M., Walczak, A. M., & Mora, T. (2022). Affinity maturation for an optimal balance between long-term immune coverage and short-term resource constraints. Proceedings of the National Academy of Sciences, 119(8), e2113512119 [article, pdf].
- Ferretti, F., Chardès, V., Mora, T., Walczak, A. M., & Giardina, I. (2022). Renormalization group approach to connect discrete-and continuous-time descriptions of Gaussian processes. Physical Review E, 105(4), 044133 [article, pdf].
- Ferretti, F., Chardès, V., Mora, T., Walczak, A. M., & Giardina, I. (2020). Building general Langevin models from discrete datasets. Physical Review X, 10(3), 031018 [article, pdf].