Short Bio

Daniele Durante is an Assistant Professor of Statistics in the Department of Decision Sciences at Bocconi University [Italy] and a Research Affiliate to the Bocconi Institute for Data Science and Analytics, the DONDENA Centre for Research on Social Dynamics and Public Policy and the Laboratory for Coronavirus Crisis Research. Prior to joining Bocconi University in 2017, he was a Post–Doctoral Fellow in the Department of Statistical Sciences at the University of Padova, where he obtained a Ph.D. [2016] and an MS.c. [2012] in Statistics under the supervision of Professor Bruno Scarpa and the co-supervision of Professor David B. Dunson. During his Ph.D. experience, he has been a Visiting Research Scholar [2014–2015] in the Department of Statistical Sciences at Duke University [USA]. Recently, he also joined the Editorial Board of Biometrika and of the Journal of Computational and Graphical Statistics as Associate Editor.

His research is characterized by an interdisciplinary approach at the intersection of Bayesian methodology, modern applications and statistical learning to develop rigorous probabilistic representations which combine flexibility, computational tractability and interpretability in modeling complex, high–dimensional and network–related phenomena, especially in health and social sciences. For his research he has been awarded the Laplace prize [SBSS Section of ASA], the Byar Award [Biometrics Section of ASA], the ISBA Lifetime Members Junior Researcher Award [ISBA], the Mitchell Prize [ISBA and ASA], the Ph.D. Thesis Award in Statistics [Italian Statistical Society], and the Leonardo da Vinci Medal [Italian Ministry of Education, University and Research]. Currently, he is also co–PI [Bocconi Unit] of the PRIN–MIUR grant "Unfolding the SEcrets of LongEvity: Current Trends and future prospects [SELECT]" [2019–2022].

In 2018, he has been invited to give a TEDx talk on the Hidden Geometry of our Relations, and he has chaired the sections j–ISBA and y–SIS. As the chair of these groups, he has organized several sessions at scientific conferences, and he has joined the scientific and local organizing committees of different meetings, such as SUR2017, SIS2018, SIS2019, BAYSM2018 and the series of hackathons Stats under the Stars, where he has been the chair of the first [SUS1] and fifth [SUS5] edition. In the occasion of SUS5, he has proposed and then developed in collaboration with BUILT the Bocconi Data Science Challenge platform.

Research

For a Complete List of Publications refer to my curriculum vitae and Google Scholar profile. Codes can be found in my GitHub repository.

Legramanti, S., Rigon, T., Durante, D. and Dunson, D.B. [2021]. Extended stochastic block models with application to criminal networks. [submitted].
Cao, J., Durante, D. and Genton, M.G. [2020]. Scalable computation of predictive probabilities in probit models with Gaussian process priors. [submitted].
Fasano, A. and Durante, D. [2020]. A class of conjugate priors for multinomial probit models which includes the multivariate normal one. [submitted].
Fasano, A., Durante, D. and Zanella, G. [2020]. Scalable and accurate variational Bayes for high-dimensional binary regression models. [submitted].
Fasano, A., Rebaudo, G., Durante, D. and Petrone, S. [2019]. A closed-form filter for binary time series. [submitted].

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. [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: 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.
Canale, A., Durante, D., Paci, L. and Scarpa, B. [2018]. Connecting statistical brains. Significance, 15, 38–40.
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: 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.