Education

• (2021 - 2024) Ph. D. in Mathematical Models and Methods in Engineering, Politecnico di Milano

• (2018 - 2021) M. Sc. in Mathematical Engineering, Politecnico di Milano

• (2019 - 2020) Alta Scuola Politecnica, Politecnico di Milano / Politecnico di Torino

• (2015 - 2018) B. Sc. in Mathematical Engineering, Politecnico di Milano

Research

Scientific Articles

• Beraha M, Guindani B, Gianella M and Guglielmi A (2025)
  BayesMix: Bayesian Mixture Models in C++
  Journal of Statistical Software, 112(9), 1-40. DOI: 10.18637/jss.v112.i09

• Codazzi L, Colombi A, Gianella M, Argiento R, Paci L and Pini A (2022)
  Gaussian graphical modeling for spectrometric data analysis
  Computational Statistics & Data Analysis, 174, 107416. DOI: 10.1016/j.csda.2021.107416

• Bellini G, Cipriano M, Comai S, De Angeli N, Gargano, J P, Gianella M, Goi G, Ingrao G, Masciadri A, Rossi G and Salice F (2021)
  Understanding social behaviour in a health-care facility from localization data: a case study
  Sensors, 21(6), 2147. DOI: 10.3390/s21062147

Conference Proceedings

• Gianella M, Guglielmi A (2025)
  Model‑based clustering of spatial time series through the BayesMix library
  In: Pollice A, Mariani P (Eds): Methodological and Applied Statistics and Demography IV, Springer, Cham, pp. 102-107. [SIS 2024]

• Gianella M, Guglielmi A, Lonati G. (2022)
  A Bayesian spatio‑temporal model of PM10 pollutant in the Po Valley
  In: Balzanella A, Bini M, Cavicchia C, Verde R (Eds): Book of short papers SIS 2022, Pearson, Milan, pp. 883‑888. [SIS 2022]

• Gianella M, Beraha M, Guglielmi A (2021)
  Spatially dependent mixture models with a random number of components
  In: Perna C, Salvati N, Schirripa Spagnolo F (Eds): Book of Short Papers SIS 2021, Pearson, Milan, pp. 936–941. [SIS 2021]

• Bellini G, Cipriano M, De Angeli N, Gargano J P, Gianella M, Goi G, Rossi G, Masciadri A, Comai, S. (2020)
  Alzheimer’s Garden: Understanding Social Behaviors of Patients with Dementia to Improve Their Quality of Life
  In: Miesenberger K, Manduchi R, Covarrubias Rodriguez M, Peňáz P (Eds): ICCHP 2020: Computers Helping People with Special Needs, Springer, Cham., pp. 384‑393. [ICCHP 2020]

• Codazzi L, Colombi A, Gianella M, Argiento R, Paci L, Pini A. (2020)
  Functional graphical model for spectrometric data analysis
  In: Pollice A, Salvati N, Schirripa S (Eds): Book of short papers SIS 2020, Pearson, Milan, pp. 852‑856. [SIS 2020]

Ongoing Projects

• (2023) Bayesian nonparametric boundary detection for income areal data (with M. Beraha and A. Guglielmi) [arXiv]

• (202x) A consensus Monte Carlo algorithm for large spatial datasets (with F. A. Quintana and A. Guglielmi) [in preparation]

• (202x) BNP spatial downscaling of COVID‑19 mortality counts (with M. Beraha and A. Guglielmi) [in preparation]

Talks and Seminars

• Bayesian nonparametric boundary detection for income areal data
  (Invited Talk) [CMStatistics 2024, Dec. 2024]

• Consensus Monte Carlo for large areal datasets
  (Contributed talk) [ISBA 2024, Jul. 2024]

• Clustering spatial time series with BayesMix
  (Poster) [BAYSM 2024, Jun. 2024]

• Model‑based clustering of spatial time series through the BayesMix library
  (Contributed Talk) [SIS 2024, Jun. 2024]

• BNP mixture models for boundary detection
  (Contributed Talk) [BAYSM 2023, Nov. 2023]

• BNP spatial downscaling of COVID‑19 mortality counts'
  (Poster) [BNP13, Oct. 2022]

• A Bayesian spatio‑temporal model of PM10 pollutant in the Po Valley
  (Contributed Talk) [SIS 2022, Jun. 2022]

• Reversible Jump computations for spatially dependent mixture models
  (Contributed Talk) [ISBA 2021, Jul. 2021; BAYSM 2021, Sep. 2021]

• Spatially dependent mixture models with a random number of components
  (Contributed Talk) [SIS 2021, Jun. 2021]

• Alzheimer’s Garden: Understanding Social Behaviors of Patients with Dementia to Improve Their Quality of Life
  (Contributed Talk) [ICCHP 2020, Sep. 2020]

How to reach me