Publications and Projects
Publications and Projects
Research grouped by topic, from ongoing projects and preprints to published work. Published articles are listed from newest to oldest; preprint years refer to the latest arXiv version.
Research grouped by topic, from ongoing projects and preprints to published work. Published articles are listed from newest to oldest; preprint years refer to the latest arXiv version.
Pegoraro (2026). Persistence Spheres: Bi-continuous representations of persistence diagrams. ICLR.
Pegoraro et al. (2026). Predicting ionic conductivity of sulfide glass solid-state electrolytes using max-flow methodology. Nat. Commun.
Pegoraro & Secchi (2026). Functional data representation with merge trees. JCGS.
Pegoraro & Beraha (2022). Projected statistical methods for distributional data on the real line with the Wasserstein metric. JMLR.
Stable and statistically tractable representations of topological summaries and measures through Hilbert-space embeddings.
Sharp inverse exponents for kernel discrepancies, universal restrictions for Lipschitz embeddings, and the ReLU kernel.
Pegoraro, M. (2026). Persistence Spheres: A bi-continuous linear representation of measures for partial optimal transport. Submitted. arXiv.
Pegoraro, M. (2026). Persistence Spheres: Bi-continuous representations of persistence diagrams. The Fourteenth International Conference on Learning Representations (ICLR). arXiv.
Statistical models and inference for spatial data, random point patterns and persistence diagrams.
Subsampling corrections for iid covering radii. With N. Chenavier.
Extreme covering radii for non-uniform samples: Smooth domains and embedded manifolds. With N. Chenavier.
Pegoraro, M., & Beraha, M. (2026). Weighted persistence intensity regression. Submitted. arXiv.
Domanin, D. A., Pegoraro, M., Trimarchi, S., Domanin, M., & Secchi, P. (2024). Persistence diagrams for exploring the shape variability of abdominal aortic aneurysms. Scientific Reports, 14, Article 28132. DOI · arXiv.
Beraha, M., Pegoraro, M., Peli, R., & Guglielmi, A. (2021). Spatially dependent mixture models via the logistic multivariate CAR prior. Spatial Statistics, 46, Article 100548. DOI · arXiv.
Data analysis with Reeb graphs, including uncertainty quantification.
Pegoraro, M., Conforti, A., & Carrière, M. (2026). Building confidence regions for Reeb graphs using the interleaving distance. Submitted. arXiv.
Pegoraro, M., & Fajstrup, L. (2026). Circular max-flow for periodic data via Reeb graphs. Submitted. arXiv.
Pegoraro, M., Christensen, R., Sørensen, S. S., Lindbjerg, A. R., Du, T., Bokor Bleile, Y., Svane, A. M., Biscio, C. A. N., Fajstrup, L., & Smedskjaer, M. M. (2026). Predicting ionic conductivity of sulfide glass solid-state electrolytes using max-flow methodology. Nature Communications, 17, Article 10343. DOI.
Reconstruction of multiparameter persistence modules through the Radon transform.
The Radon transform recovers the multipersistence module. With F. Petit and F. Chazal.
Statistical methods for distributional data in Wasserstein spaces, including principal component analysis and regression on linear and circular domains.
Beraha, M., & Pegoraro, M. (2024). Wasserstein principal component analysis for circular measures. Statistics and Computing, 34, Article 171. DOI · arXiv.
Pegoraro, M., & Beraha, M. (2022). Projected statistical methods for distributional data on the real line with the Wasserstein metric. Journal of Machine Learning Research, 23(37), 1–59. Publisher · arXiv.
Pegoraro, M., & Beraha, M. (2021). Fast PCA in 1-D Wasserstein spaces via B-splines representation and metric projection. Proceedings of the AAAI Conference on Artificial Intelligence, 35(10), 9342–9349. DOI.
Stable distances and computational methods for tree-shaped topological summaries.
Pegoraro, M. (2025). A persistence-driven edit distance for trees with abstract weights. Submitted. arXiv.
Pegoraro, M. (2025). A finitely stable edit distance for functions defined on merge trees. Submitted. arXiv.
Pegoraro, M., & Secchi, P. (2026). Functional data representation with merge trees. Journal of Computational and Graphical Statistics. Advance online publication. DOI · arXiv.
Pegoraro, M. (2025). A finitely stable edit distance for merge trees. AIMS Mathematics, 10(7), 17179–17231. Special issue: Recent Advances in Algebraic Topology and Applications. DOI · arXiv.
Pegoraro, M. (2025). A graph-matching formulation of the interleaving distance between merge trees. AIMS Mathematics, 10(6), 13025–13081. Special issue: Recent Advances in Algebraic Topology and Applications. DOI · arXiv.
Cavinato, L., Pegoraro, M., Ragni, A., Sollini, M., Erba, P. A., & Ieva, F. (2022). Imaging-based representation and stratification of intra-tumor heterogeneity via tree-edit distance. Scientific Reports, 12, Article 19607. DOI · arXiv.
Additional research projects, conference papers and abstracts.
A dataset of meshes of the aorta for topological data analysis. With D. A. Domanin, S. Trimarchi, M. Domanin and P. Secchi.
Conforti, A., Pegoraro, M., & Carrière, M. (2026). Building confidence regions for Reeb graphs using the interleaving distance. In Statistical Science: From Theory to Applied Research III: SIS-FENStatS 2026, Short Papers, Contributed Sessions 2, pp. 26–31. Springer. DOI.
Pegoraro, M. (2026). Persistence Spheres: Bi-continuous linear representations of persistence diagrams. Some early stage results. In Proceedings of the Geometry, Topology, and Machine Learning Workshop, Proceedings of Machine Learning Research, 325, 251–261. Publisher.
Cavinato, L., Ferrara, L., Pegoraro, M., Erba, P. A., & Ieva, F. (2025). Topological data analysis applied to radiomics (topiomics) data in recurrent prostate cancer. In Methodological and Applied Statistics and Demography III: SIS 2024, Short Papers, Contributed Sessions 1, pp. 379–385. Springer. DOI.
Pegoraro, M., & Secchi, P. (2022). Functional data representation with merge trees. In Classification and Data Science in the Digital Age: Book of Abstracts IFCS 2022, p. 70. Instituto Nacional de Estatística. Conference volume.
Guglielmi, A., Beraha, M., Gianella, M., Pegoraro, M., & Peli, R. (2021). A transdimensional MCMC sampler for spatially dependent mixture models. In CLADAG 2021: Book of Abstracts and Short Papers, pp. 112–115. Firenze University Press. Conference volume.
Beraha, M., Pegoraro, M., Peli, R., & Guglielmi, A. (2020). A Bayesian approach for modelling dependence among mixture densities. In Book of Short Papers SIS 2020, pp. 608–613. Pearson. Institutional record.
Pegoraro, M. (2023). Data Analysis with Merge Trees. PhD thesis, Data Science and Computation, Università di Bologna. Supervisor: Piercesare Secchi.
Pegoraro, M. (2021). Geometric Data Analysis: Between Equivalence Classes and non-Euclidean Spaces. PhD thesis, Mathematical Models and Methods for Engineering, Politecnico di Milano. Supervisor: Piercesare Secchi.