DANIELE DURANTE

Daniele Durante is an Assistant Professor of Statistics at the Department of Decision Sciences of the Bocconi University, and a Research Affiliate at the Bocconi Institute for Data Science and Analytics (BIDSA), the DONDENA Centre for Research on Social Dynamics and Public Policy, and the Laboratory for Coronavirus Crisis Research. His research is characterized by an interdisciplinary approach at the intersection of Bayesian methodology, modern applications, and statistical machine learning to develop flexible, interpretable and computationally tractable probabilistic representations for complex, high-dimensional and network-related phenomena, especially in health and social sciences.

For more information visit https://danieledurante.github.io/web/.

• Post-doctoral Research Fellow. Department of Statistical Sciences, University of Padova, ITA. (2016–2017). Research Topic: Bayesian nonparametrics for functional and complex data. Advisor: Bruno Scarpa.
• Ph.D. in Statistical Sciences. Department of Statistical Sciences, University of Padova, ITA. (2013–2016). Research Topic: Bayesian nonparametric modeling of network data. Advisors: Bruno Scarpa. Co-advisor: David B. Dunson.
• Visiting Research Scholar. Department of Statistical Sciences, Duke University, USA. (2014–2015). Research Topic: Bayesian learning for high-dimensional low sample size data. Advisors: David B. Dunson.
• M.Sc. in Statistical Sciences cum Laude. Department of Statistical Sciences, University of Padova, ITA. (2010–2012). Research Topic: Locally adaptive Bayesian covariance regression. Advisors: Bruno Scarpa and David B. Dunson.
• B.Sc. in Statistics, Economy and Finance cum Laude. Department of Statistical Sciences, University of Padova, ITA. (2007–2010). Research Topic: Structural equation models for ordered categorical data. Advisor: Bruno Scarpa.

Network Science — Bayesian Methods and Computation in High Dimension — Categorical Data — Complex Data — Latent Variables Models — Computational Social Science — Demography 

• Legramanti S., Rigon, T. and Durante, D. (2021). Bayesian testing for exogenous equivalence structures in stochastic block–models. Sankhya A. In Press.
• Rigon, T. and Durante, D. (2021). Tractable Bayesian density regression via logit stick-breaking priors. Journal of Statistical Planning and Inference, 211, 131–142.
• Legramanti, S., Durante, D. and Dunson D.B. (2020). Bayesian Cumulative Shrinkage for Infinite Factorizations. Biometrika. 107, 745–752.
• Durante, D. and Guindani, M. (2020). Bayesian methods in brain networks. Wiley StatsRef–Statistics Reference Online, 1–10.
• Durante, D. (2019). Conjugate Bayes for Probit Regression via Unified Skew-Normal Distributions. Biometrika. 106, 765–779.
• Durante, D. and Rigon, T. (2019). Conditionally Conjugate Mean-Field Variational Bayes for Logistic Models. Statistical Science. 34, 472–485.
• Durante, D., Canale, A. and Rigon, T. (2019). A Nested Expectation–Maximization Algorithm for Latent Class Models with Covariates. Statistics & Probability Letters. 146, 97–103.
• 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, 225–247.
• Canale, A., Durante, D. and Dunson D.B. (2018). Convex Mixture Regression for Quantitative Risk Assessment. Biometrics. 74, 1331–1340.
• Russo, M., Durante, D. and Scarpa, B. (2018). Bayesian Inference on Group Differences in Multivariate Categorical Data. Computational Statistics & Data Analysis. 126, 136—149.
• Durante, D. and Dunson, D. B. (2018). Bayesian Inference and Testing of Group Differences in Brain Networks. Bayesian Analysis. 13, 29–58.
• Durante, D., Dunson, D. B. and Vogelstein, J. T. (2017). Nonparametric Bayes Modeling of Populations of Networks. Journal of the American Statistical Association. 112, 1516–1530 (with discussion).
• Durante, D., Mukherjee, N. and Steorts, R. C. (2017). Bayesian Learning of Dynamic Multilayer Networks. Journal of Machine Learning Research. 18, 1–29.
• Wang, L., Durante, D., Jung, R. E. and Dunson, D. B. (2017). Bayesian Network-Response Regression. Bioinformatics. 33, 1859–1866.
• Durante, D. (2017). A Note on the Multiplicative Gamma Process. Statistics & Probability Letters. 122, 198–204.
• Durante, D., Paganin, S., Scarpa, B. and Dunson, D. B. (2017). Bayesian Modelling of Networks in Complex Business Intelligence Problems. Journal of the Royal Statistical Society: Series C. 66, 555–580.
• Durante, D. and Dunson, D. B. (2016). Locally Adaptive Dynamic Networks. Annals of Applied Statistics. 10, 2203–2232.
• Durante, D. and Dunson, D. B. (2014). Nonparametric Bayes Dynamic Modelling of Relational Data. Biometrika. 101, 883–898.
• Durante, D. and Dunson, D. B. (2014). Bayesian Dynamic Financial Networks with Time-Varying Predictors. Statistics & Probability Letters. 93, 19–26.
• Durante, D., Scarpa, B. and Dunson, D. B. (2014). Locally Adaptive Factor Processes for Multivariate Time Series. Journal of Machine Learning Research. 15, 1493–1522.