Tommaso Rigon

Academic awards

• Young Talents Award 2021. University of Milano-Bicocca, Accademia Nazionale dei Lincei
• Savage Award 2020 (Theory and Methods). American Statistical Association (ASA), International Society for Bayesian Analysis (ISBA)
• Best Ph.D. student in Statistics at Bocconi University in the Academic Year 2016/2017 (09/2017)

Data competitions

• Best objective prediction at Stat under the Stars 4, Palermo, Italy (06/2018)
• Winner of the Young-CLADAG data contest, Milan, Italy (09/2017)
• Winner of the Bocconi summer school data competition, Como, Italy (07/2017)

Travel awards

• Travel award (400£) for O’Bayes 2019 conference, Warwick, UK (06/2019)
• Travel award (accommodation) for the BNP12 conference, Oxford, UK (06/2019)
• ISBA travel award (250$) for ISBA 2018 world meeting, Edinburgh, UK (06/2018)
• ISBA travel award (700$) for the O’Bayes 2017 conference, Austin, Texas (12/2017)

Articles in refereed journals

1. Catalano, M., Lijoi, A., Prünster, I. and Rigon, T. (2023). Bayesian modeling via discrete nonparametric priors. Japanese Journal of Statistics and Data Science, forthcoming.

2. Rigon, T. and Aliverti E. (2023) Conjugate priors and bias reduction for logistic regression models. Statistics and Probability Letters, forthcoming. [ArXiv]

3. Rigon, T., Herring, A. H. and Dunson, D. B. (2023). A generalized Bayes framework for probabilistic clustering. Biometrika, forthcoming. [Link].

4. Lijoi, A., Prünster, I. and Rigon, T. (2023). Finite-dimensional discrete random structures and Bayesian clustering. Journal of the American Statistical Association (T&M), forthcoming. [Link].

5. Zito, A., Rigon, T. and Dunson, D. B. (2023). Inferring taxonomic placement from DNA barcoding aiding in discovery of new taxa. Methods in Ecology and Evolution, 14, 529–542 [Link] [GitHub Repository]

6. Reverberi, C., Rigon, T., Solari, A., Hassan, C., Cherubini, P., GI Genius CADx Study Group and A. Cherubini (2022). Experimental evidence of effective human-AI collaboration in medical decision‐making. Scientific Reports, 12(14952) [Link]

7. Rigon, T. (2022). An enriched mixture model for functional clustering. Applied Stochastic Models in Business and Industry, forthcoming [Link].

8. Zito, A., Rigon, T., Ovaskainen, O. and Dunson, D. B. (2022). Bayesian modelling of sequential discoveries. Journal of the American Statistical Association (T&M), forthcoming. [Link] [GitHub Repository].

9. Legramanti, S., Rigon, T., Durante, D. and Dunson D. B. (2022). Extended stochastic block models with application to criminal networks. Annals of Applied Statistics 16(4), 2369–2395. [Link] [GitHub repository].

10. Favaro, S., Panero, F. and Rigon, T. (2021). Bayesian nonparametric disclosure risk assessment. Electronic Journal of Statistics 15(2), 5626–5651. [Link].

11. Rigon, T. and Durante, D., (2021), Tractable Bayesian density regression via logit stick-breaking priors. Journal of Statistical Planning and inference 211, 131–142. [Link] [GitHub Repository].

12. Legramanti, S., Rigon, T. and Durante, D. (2020). Bayesian testing for exogenous partition structures in stochastic block models. Sankhya A: The Indian Journal of Statistics, 84, 108–126. [Link] [GitHub repository].

13. Lijoi, A., Prünster, I. and Rigon, T. (2020). Sampling hierarchies of discrete random structures. Statistics and Computing 30, 1591–1607. [Link].

14. Lijoi, A., Prünster, I. and Rigon, T. (2020). The Pitman–Yor multinomial process for mixture modeling. Biometrika 107(4), 891–906. [Link].

15. Durante, D. and Rigon, T. (2019). Conditionally conjugate mean-field variational Bayes for logistic models. Statistical Science 34(3), 472–485. [Link] [GitHub Repository].

16. Rigon, T., Durante, D. and Torelli, N. (2019). Bayesian semiparametric modelling of contraceptive behavior in India via sequential logistic regressions. Journal of the Royal Statistical Society, Series A 182(1), 225–247. [Link] [GitHub Repository].

17. Durante, D., Canale, A. and Rigon, T. (2019). A nested expectation-maximization algorithm for latent class models with covariates. Statistics and Probability Letters 146, 97–103. [Link] [GitHub Repository].

Submitted and working papers

1. Rigon, T., Scarpa, B. and Petrone, S. (2023+). Enriched Pitman-Yor processes. Submitted. [ArXiv].

Refereed conference proceedings, publications in monographs, discussions

1. Legramanti S., Rigon, T. and Durante, D. (2022). Bayesian clustering of brain regions via extended stochastic block models. In Book of Short Papers of the Italian Statistical Society (Balzanella, A., Bini, M., Cavicchia, C., Verde, R.) 2022. ISBN: 9788891932310. [Link]

2. Zito, A., Rigon, T. and Dunson, D. B. (2021). Modelling of accumulation curves through Weibull survival functions. In Book of Short Papers of the Italian Statistical Society 2021 (Perna, C., Salvati, N. and Schirripa Spagnolo, F., editors). ISBN: 9788891927361. [Link - Part 1] [Link - Part 2].

3. Rigon, T., Aliverti, E., Russo, M., and Scarpa, B. (2021). A discussion on: “Centered partition processes: Informative priors for clustering” Paganin, S., Herring, A. H., Olshan, A. F., Dunson, D. B., et al. (2021) in Bayesian Analysis 16(1) 301–370. [Link].

4. Aliverti, E., Paganin, S., Rigon, T. and Russo, M. (2019). A discussion on: “Latent nested nonparametric priors” by Camerlenghi, F., Dunson, D.B., Lijoi, A., Prünster, I. and Rodriguez, A. in Bayesian Analysis 14(4), 1303–1356. [Link].

5. Rigon, T. (2018). Logit stick-breaking priors for partially exchangeable count data. In Book of Short Papers SIS 2018 (Abbruzzo, A., Piacentino, D., Chiodi, M. and Brentari, E., editors). ISBN: 9788891910233. [Link].

6. Caponera, A., Denti, F., Rigon, T., Sottosanti, A. and Gelfand, A. (2018). Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data. In Studies in Neural Data Science (Canale, A., Durante, D., Paci, L. And Scarpa, B., editors). [Link] [GitHub Repository].