Tommaso Rigon

Assistant Professor at University of Milano-Bicocca

Email: tommaso.rigon@unimib.it

About Me

I am an Assistant Professor in Statistics at the department of Economics, Management and Statistics (DEMS) of University of Milano-Bicocca. I currently live in Milan, Italy.

I am also member of the Datalab at University of Milano-Bicocca, the BayesLab at the Bocconi Institute for Data Science and Analytics (BIDSA), the “de Castro” Statistics Initiative at Collegio Carlo Alberto, and the MIDAS Complex Data Modeling Research Network.

My complete curriculum vitae is available at this link. For more information about myself, you can also visit my GitHub and Google scholar profiles.

I am interested in Bayesian methods for the analysis of complex data, covering the theoretical, applicative, and computational aspects. I try to balance these three macro-areas in my research.

Download curriculum vitae

Teaching @ Unimib

Ph.D. courses

B.Sc. courses (corsi laurea triennale)

B.Sc. & M.Sc. thesis (tesi triennale e magistrale)

Other projects

Academic Positions

University of Milano-Bicocca

https://en.unimib.it

Assistant Professor

2020 - Present

Department of Economics, Management and Statistics (DEMS), University of Milano-Bicocca, Milan, Italy

I am currently an Assistant Professor (Ricercatore SECS-S/01, Legge 240/10 tipo A), at University of Milano-Bicocca.

Duke University

https://www.duke.edu

Postdoctoral Associate

2020 - 2020

Research Associate

2019 - 2020

Department of Statistical Science, Duke University, Durham, North Carolina, USA

I have been Research Associate and Postdoctoral Associate at Duke university. I worked with prof. Amy Herring and prof. David Dunson. My research focused on Bayesian methods for robust clustering, dimensionality reduction and sequential species discovery.

Collegio Carlo Alberto

https://www.carloalberto.org

Research Affiliate

2017 - 2020

Fondazione "de Castro" and Collegio Carlo Alberto, Turin, Italy

I have been a Research affiliate at the Statistics initiative, Fondazione “de Castro” and Collegio Carlo Alberto.

Education

Bocconi University

https://www.unibocconi.eu/

Ph.D. in Statistical Sciences

2015 - 2020

Bocconi University, Milan, Italy

I have been awarded a merit based 4-year fellowship. I worked under the joint supervision of prof. Antonio Lijoi and prof. Igor Prünster. Thesis title: Finite-dimensional nonparametric priors: theory and applications. Ph.D. awarded with honors.

University of Padova

https://www.unipd.it

M.Sc. in Statistical Sciences

2013 - 2015

Università degli studi di Padova, Padua, Italy

Final mark: 110/110 with laude. Advisor: Bruno Scarpa. Thesis title: Functional telecommunication data: a Bayesian nonparametric approach. I attended with honors the PhD courses: Theory and Methods of Inference, Statistical Models.

University of Padova

https://www.unipd.it

B.Sc. in Statistics, Economics & Finance

2010 - 2013

Università degli studi di Padova, Padua, Italy

Final mark: 110/110 with laude. Advisor: Nicola Sartori. Thesis title: Box-Cox transformation: an analysis based on the likelihood.

Awards

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

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)

Publications

Articles in refereed journals

  1. 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. [ArXiv].

  2. Legramanti, S., Rigon, T., Durante, D. and Dunson D. B. (2022). Extended stochastic block models with application to criminal networks. Annals of Applied Statistics, forthcoming. [ArXiv] [GitHub repository].

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

  4. 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].

  5. Legramanti, S., Rigon, T. and Durante, D. (2020). Bayesian testing for exogenous partition structures in stochastic block models. Sankhya A. [Link] [GitHub repository].

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

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

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

  9. 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].

  10. 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. (2022+). An enriched mixture model for functional clustering. Submitted. [ArXiv].

  2. Lijoi, A., Prünster, I. and Rigon, T. (2022+). Finite-dimensional discrete random structures and Bayesian clustering. Submitted. [Link].

  3. Rigon, T., Scarpa, B. and Petrone, S. (2022+). Enriched Pitman-Yor processes. Submitted. [ArXiv].

  4. Rigon, T., Herring, A. H. and Dunson, D. B. (2022+). A generalized Bayes framework for probabilistic clustering. Submitted. [ArXiv].

  5. Zito, A., Rigon, T. and Dunson, D. B. (2022+). Inferring taxonomic placement from DNA barcoding allowing discovery of new taxa. Submitted. [ArXiv]

  6. Reverberi, C., Rigon, T., Solari, A., Hassan, C., Cherubini, P. and A. Cherubini (2022+) Hybrid intelligence decision making: successful human-AI integration in optical diagnosis. Submitted.

  7. Rigon, T. and E. Aliverti (2022+) Conjugate priors and bias reduction for logistic regression models. Submitted. [ArXiv]

Refereed conference proceedings, publications in monographs, discussions

  1. 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].

  2. 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].

  3. 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].

  4. 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].

  5. 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].

Tommaso Rigon - tommaso.rigon@unimib.it