publications

Google Scholar InspireHEP

2026

  1. Analytic regression of Feynman integrals from high-precision numerical sampling
    Oscar Barrera , Aurélien Dersy , Rabia Husain , Matthew D Schwartz , and Xiaoyuan Zhang
    Journal of High Energy Physics, 2026

    A paper on reconstructing exact analytic forms of Feynman integrals from high-precision numerical samples using lattice-reduction techniques and prior knowledge of the function space.

    DOI HTML PDF
    BibTeX 
    @article{barrera2026analytic,
  title = {Analytic regression of Feynman integrals from high-precision numerical sampling},
  author = {Barrera, Oscar and Dersy, Aur{\'e}lien and Husain, Rabia and Schwartz, Matthew D and Zhang, Xiaoyuan},
  journal = {Journal of High Energy Physics},
  volume = {2026},
  number = {1},
  pages = {14},
  year = {2026},
  publisher = {Springer Berlin Heidelberg},
  doi = {10.1007/JHEP01(2026)014},
}

2025

  1. Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function
    Jason B Gibson , Ajinkya C Hire , Philip M Dee , Oscar Barrera , Benjamin Geisler , Peter J Hirschfeld , and Richard G Hennig
    npj Computational Materials, 2025

    A machine-learning paper on learning the electron-phonon spectral function in a way that makes superconductor screening faster and more sample-efficient.

    DOI Code
    BibTeX 
    @article{gibson2025accelerating,
  title = {Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function},
  author = {Gibson, Jason B and Hire, Ajinkya C and Dee, Philip M and Barrera, Oscar and Geisler, Benjamin and Hirschfeld, Peter J and Hennig, Richard G},
  journal = {npj Computational Materials},
  volume = {11},
  number = {1},
  pages = {7},
  year = {2025},
  publisher = {Nature Publishing Group UK London},
  doi = {10.1038/s41524-024-01475-4},
}
  2. Constrained Tabular Diffusion for Finance
    Michael Cardei , Jose Munoz , Oscar Barrera , Shreyas Chandrahas , and Partha Saha
    In Proceedings of the 6th ACM International Conference on AI in Finance, 2025

    A constrained diffusion approach for tabular financial data that enforces hard feasibility requirements during sampling rather than hoping they emerge from training alone.

    DOI HTML
    BibTeX 
    @inproceedings{cardei2025constrained,
  title = {Constrained Tabular Diffusion for Finance},
  author = {Cardei, Michael and Munoz, Jose and Barrera, Oscar and Chandrahas, Shreyas and Saha, Partha},
  booktitle = {Proceedings of the 6th ACM International Conference on AI in Finance},
  pages = {543--551},
  year = {2025},
  doi = {10.1145/3768292.3770358},
}
  3. Compliant Generative Diffusion for Finance
    Michael Cardei , Jose M Munoz , Oscar Barrera , Shreyas K Chandrahas , and Partha Saha
    In NeurIPS 2025 Workshop: Generative AI in Finance, 2025

    A workshop paper on compliant diffusion models for financial data generation, focused on producing realistic samples without violating application-level constraints.

    HTML PDF
    BibTeX 
    @inproceedings{cardei2025compliant,
  title = {Compliant Generative Diffusion for Finance},
  author = {Cardei, Michael and Munoz, Jose M and Barrera, Oscar and Chandrahas, Shreyas K and Saha, Partha},
  booktitle = {NeurIPS 2025 Workshop: Generative AI in Finance},
  year = {2025},
}

2024

  1. Ancestral spin information in gravitational waves from black hole mergers
    O Barrera and I Bartos
    Astroparticle Physics, 2024

    A follow-up study showing how black-hole spin can retain a memory of merger ancestry and become an observational handle on hierarchical formation channels.

    DOI arXiv
    BibTeX 
    @article{barrera2024ancestral,
  title = {Ancestral spin information in gravitational waves from black hole mergers},
  author = {Barrera, O and Bartos, I},
  journal = {Astroparticle Physics},
  volume = {156},
  pages = {102919},
  year = {2024},
  publisher = {North-Holland},
  doi = {10.1016/j.astropartphys.2023.102919},
}

2022

  1. Ancestral Black Holes of Binary Merger GW190521
    Oscar Barrera and Imre Bartos
    The Astrophysical Journal Letters, 2022

    A short Letter on what present-day merger signals can still tell us about the earlier generations of black holes that produced them.

    DOI arXiv
    BibTeX 
    @article{barrera2022ancestral,
  title = {Ancestral Black Holes of Binary Merger GW190521},
  author = {Barrera, Oscar and Bartos, Imre},
  journal = {The Astrophysical Journal Letters},
  volume = {929},
  number = {1},
  pages = {L1},
  year = {2022},
  publisher = {IOP Publishing},
  doi = {10.3847/2041-8213/ac5f47},
}