Tommaso Rigon

Postdoctoral Associate at Duke University

About Me

I am a Postdoctoral Associate in Statistics at the department of Statistical Science of Duke University. I currently live in Durham, a city in North Carolina, USA.

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

Academic Positions

Duke University

https://www.duke.edu

Postdoctoral Associate

2020 - Present

Research Associate

2019 - 2020

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

I have been Research Associate at Duke university and I am currently a Postdoctoral Associate in the same institution. I work with prof. Amy Herring and prof. David Dunson. My current research focuses on Bayesian methods for robust clustering, dimensionality reduction and biostatistical applications.

Collegio Carlo Alberto

https://www.carloalberto.org

Research Affiliate

2017 - 2019

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

  • Best Ph.D. student in Statistics at Bocconi University in the Academic Year 2016/2017 (09/2017)

Data competitions

Travel award

  • 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. 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].

  2. Lijoi, A., Prünster, I. and Rigon, T. (2020). Sampling hierarchies of discrete random structures. Statistics and Computing. In press. [Link]

  3. Lijoi, A., Prünster, I. and Rigon, T. (2020). The Pitman–Yor multinomial process for mixture modeling. Biometrika. In press. [Link].

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

  5. 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].

  6. 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. (2020+). An enriched mixture model for functional clustering. Submitted. [ArXiv].

  2. Lijoi, A., Prünster, I. and Rigon, T. (2020+). Finite-dimensional discrete random structures and Bayesian clustering. Submitted. [Link].

  3. Rigon, T., Scarpa, B. and Petrone, S. (2020+). Enriched Pitman-Yor processes. Submitted. [ArXiv].

  4. Legramanti, S., Rigon, T. and Durante, D. (2020+). Bayesian testing for exogenous partition structures in stochastic block models. Submitted.

  5. Rigon, T., Herring, A. H. and Dunson, D. B. (2020+). A generalized Bayes framework for probabilistic clustering. Submitted. [ArXiv].

  6. Legramanti, S., Rigon, T., Durante, D. and Dunson D. B. (2020+). Extended stochastic block models. Submitted. [ArXiv] [GitHub repository]

Refereed conference proceedings, publications in monographs, discussions

  1. 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].

  2. 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].

  3. 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., Scarpa, B., editors). [Link] [GitHub Repository].