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]. He is also Associate Editor of Biometrika, the Journal of Computational and Graphical Statistics and the Journal of Multivariate Analysis.

Research

His research is characterized by an interdisciplinary approach at the intersection of Bayesian methodology and modern applications 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 received

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].

Service, Teaching and Outreach

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. He is also the founder and one of the leading organizers of the Data Science hackathons series Stats under the Stars, where he has chaired the first [SUS1] and fifth [SUS5] edition. In the occasion of SUS5, he has proposed and developed (in collaboration with BUILT) the Bocconi Data Science Challenge platform. For this platform, he has received in 2020 the Bocconi Innovation in Teaching Award. Throughout the years he has also served in the ASA Scientific Committee for the Laplace Award [2018], in the IMS Committee on Nominations [2020], and he is currently Vice-Chair [2021] (Chair [2022]) of the Blackwell-Rosenbluth Award by ISBA. He is also one of the founders and leading organizers of the research initiative StartUpResearch, and of the online webinar series Junior Bayes Beyond the Borders.

Research

For a Complete List of Publications refer to my curriculum vitae and Google Scholar profile. Codes can be found in my GitHub repository.
Fasano, A., Durante, D. and Zanella, G. [2022+]. Scalable and accurate variational Bayes for high-dimensional binary regression models. Biometrika. [in press].
Cao, J., Durante, D. and Genton, M.G. [2022+]. Scalable computation of predictive probabilities in probit models with Gaussian process priors. Journal of Computational and Graphical Statistics. [in press].
Fasano, A. and Durante, D. [2022+]. A class of conjugate priors for multinomial probit models which includes the multivariate normal one. Journal of Machine Learning Research. [in press].
Legramanti, S., Rigon, T., Durante, D. and Dunson, D.B. [2022+]. Extended stochastic block models with application to criminal networks. Annals of Applied Statistics. [in press].
Fasano, A., Rebaudo, G., Durante, D. and Petrone, S. [2021]. A closed-form filter for binary time series. Statistics and Computing. 31, 1–20.
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: 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]. Invited discussion of "Sparse graphs using exchangeable random measures". Journal of the Royal Statistical Society: Series B, 79, 55–56.
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.