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.

  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].

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].