Publications

Articles in refereed journals

  • Colombi A., Argiento, R., Camerlenghi, F., & Paci, L. Mixture modeling via vectors of normalized independent finite point processes. arXiv, 2023, [Link arXiv].
  • Colombi A., Argiento, R., Paci, L., & Pini, A. Learning block structured graphs in Gaussian graphical models. Journal of Graphical and Computational Statistics, 2023, [Link]. [Link arXiv].
  • Codazzi L., Colombi A., Gianella M., Argiento R., Paci L., Pini A., Gaussian graphical modeling for spectrometric data analysis, Computational Statistics & Data Analysis, 2022 [Link].
  • R packages

  • Block Graph Structural Learning (BGSL), main creator.
  • Functional Graphical Models (FGM), created along with Codazzi L. and Gianella M.
  • Awards

  • Winner of prize Best Objective Prediction at hackathon Stats under the stars (SUS), Ancona, Italy, June 20 - 21, 2023
    Team members : Teresa Bortolotti, Alessandro Colombi, Luca Presicce, Valentina Zangirolami
  • Best talk in Applications and Computation, BAYSM, 2021.
    Talk title : Learning Block Structured Graphs in Gaussian Graphical Models
  • About Me

    I currently am a Ph.D student in Statistics at the University of Milano-Bicocca. Prior to joining Bicocca University, I graduated in Mathematical Engineering at Politecnico di Milano.
    My research interests lain in Bayesian methods to model complex data, in particular I focued on undirected Gaussian graphical models and Mixture Models for clustering. I like to place particular attention to the computational details of the problem as I am fon of C++ programming, expecially when coupled with an R interface. Check out the list of my R packages here.

    Curriculum Vitae

    Contact

    Oterwise, write me at: a.colombi10@campus.unimib.it

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