Short Bio

Welcome to my personal webpage! I am 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, I was a Post–Doctoral Fellow in the Department of Statistical Sciences at the University of Padova, where I 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 my Ph.D. experience, I have been a Visiting Research Scholar [2014–2015] in the Department of Statistical Sciences at Duke University [USA]. I am also Associate Editor of Biometrika, the Journal of Computational and Graphical Statistics and the Journal of Multivariate Analysis.

Research

My 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 social sciences. For my research I received

In 2019–2023, I have been also co–PI of the PRIN–MIUR 2017 grant "Unfolding the SEcrets of LongEvity: Current Trends and future prospects" [SELECT]. See below for ongoing grants.

Service, Teaching and Outreach

In 2018, I have been invited to give a TEDx talk on the Hidden Geometry of our Relations, and I have chaired the sections j–ISBA and y–SIS. As the chair of these groups, I have organized several sessions at scientific conferences, and I joined the scientific and local organizing committees of different meetings, such as SUR2017, SIS2018, SIS2019, BAYSM2018, and ISBA2024. I am also the founder and one of the leading organizers of the Data Science hackathons series Stats under the Stars, where I have chaired the first [SUS1] and fifth [SUS5] edition. In the occasion of SUS5, I have proposed and the developed [in collaboration with BUILT] the Bocconi Data Science Challenge platform. For this platform, I have received in 2020 the Bocconi Innovation in Teaching Award. Throughout the years I have also served in the ASA Scientific Committee for the Laplace Award [2018], in the IMS Committee on Nominations [2020], and as Vice-Chair [2021] and Chair [2022] of the Blackwell-Rosenbluth Award by ISBA. I am also one of the founders and leading organizers of the research initiative StartUpResearch, and of the online webinar series Junior Bayes Beyond the Borders.

Ongoing Grants

I have been recently awarded two research grants [NEMESIS and CARONTE] aimed at providing substantial advancements in statistical modeling of complex data from criminology [NEMESIS] and demography [CARONTE]. More specifically

  • "sociogeNEsis of criMinal nEtworks: reconStruction, dIscovery and diSruption" [NEMESIS, 2024–2029] is an ERC Starting Grant aimed at combining statistics and social sciences to address key challenges in modeling of complex structures underlying modern criminal networks. It views current barriers in data incompleteness and complexity not as hindrances but as valuable resources to develop innovative modeling perspectives in criminal network analysis.

  • "Causes of deAth dependence stRuctures and the cOmpositioNal effecT on ovErall mortality" [CARONTE, 2023–2025] is a PRIN–MIUR 2022 Grant aimed at bridging demography and recent advancements in functional data analysis, compositional data analysis, graphical models and discrete choice models, to develop a unique statistical modeling framework which can fully learn the complex systems of graphical dependencies underlying causes of death and unveil their combined effects on overall mortality.
Soon I will open several Post–Doc positions under both projects! Send me an e–mail if you are interested in one of the two projects.

Research

For a Complete List of Publications refer to my curriculum vitae and Google Scholar profile. Codes can be found in my GitHub repository.
[33] Durante, D., Gaffi, F., Lijoi, A. and Pruenster, I. [2023+]. Partially exchangeable stochastic block models for multilayer networks. [submitted].
[32] Lu, C., Durante, D., and Friel, N.B. [2023+]. Zero-inflated stochastic block modeling of efficiency-security tradeoffs in weighted criminal networks. [submitted].
[31] Durante, D., Pozza, F. and Szabo, B. [2023+]. Skewed Bernstein-von Mises theorem and skew-modal approximations. [submitted].
[30] Pavone, F., Legramanti, S. and Durante, D. [2023+]. Learning and forecasting of age–specific period mortality via B–spline processes with locally–adaptive dynamic coefficients. [submitted].
[29] Legramanti, S., Durante, D. and Alquier, P. [2023+]. Concentration of discrepancy-based ABC via Rademacher complexity. [submitted].
[28] Anceschi, N., Fasano, A., Durante, D. and Zanella, G. [2023]. Bayesian conjugacy in probit, tobit, multinomial probit and extensions: A review and new results. Journal of the American Statistical Association. 118, 1451–1469.
[27] Fasano, A., Durante, D. and Zanella, G. [2022]. Scalable and accurate variational Bayes for high-dimensional binary regression models. Biometrika. 109, 901–919.
[26] Legramanti, S., Rigon, T., Durante, D. and Dunson, D.B. [2022]. Extended stochastic block models with application to criminal networks. Annals of Applied Statistics. 16, 2369–2395.
[25] 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. 31, 709–720.
[24] 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. 23, 1–26.
[23] Legramanti, S., Rigon, T. and Durante, D. [2022]. Bayesian testing for exogenous equivalence structures in stochastic block–models. Sankhya A. 84, 108–126.
[22] Fasano, A., Rebaudo, G., Durante, D. and Petrone, S. [2021]. A closed-form filter for binary time series. Statistics and Computing. 31, 1–20.
[21] Rigon, T. and Durante, D. [2021]. Tractable Bayesian density regression via logit stick-breaking priors. Journal of Statistical Planning and Inference. 211, 131–142.
[20] Legramanti, S., Durante, D. and Dunson, D.B. [2020]. Bayesian cumulative shrinkage for infinite factorizations. Biometrika. 107, 745–752.
[19] Durante, D. and Guindani, M. [2020]. Bayesian methods in brain networks. Wiley StatsRef-Statistics Reference Online, 1–10.
[18] Durante, D. [2019]. Conjugate Bayes for probit regression via unified skew-normal distributions. Biometrika, 106, 765–779.
[17] Durante, D. and Rigon, T. [2019]. Conditionally conjugate mean-field variational Bayes for logistic models. Statistical Science, 34, 472–485.
[16] 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.
[15] 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.
[14] Canale, A., Durante, D. and Dunson, D. B. [2018]. Convex mixture regression for quantitative risk assessment. Biometrics, 74, 1331–1340.
[13] Russo, M., Durante, D. and Scarpa, B. [2018]. Bayesian inference on group differences in multivariate categorical data. Computational Statistics & Data Analysis, 126, 136–149.
[12] Canale, A., Durante, D., Paci, L., Scarpa, B. [2018]. Connecting statistical brains. Significance, 15, 38–40.
[11] Durante, D. and Dunson, D. B. [2018]. Bayesian inference and testing of group differences in brain networks. Bayesian Analysis, 13, 29–58.
[10] 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].
[9] Durante, D., Mukherjee, N. and Steorts, R. C. [2017]. Bayesian learning of dynamic multilayer networks. Journal of Machine Learning Research, 18, 1–29.
[8] Wang, L., Durante, D., Jung, R. E. and Dunson, D. B. [2017]. Bayesian network-response regression. Bioinformatics, 33, 1859–1866.
[7] Durante, D. [2017]. Invited discussion of "Sparse graphs using exchangeable random measures". Journal of the Royal Statistical Society: B, 79, 55–56.
[6] Durante, D. [2017]. A note on the multiplicative gamma process. Statistics & Probability Letters, 198–204.
[5] 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.
[4] Durante, D. and Dunson, D. B. [2016]. Locally adaptive dynamic networks. Annals of Applied Statistics. 10, 2203–2232.
[3] Durante, D. and Dunson, D. B. [2014]. Nonparametric Bayes dynamic modelling of relational data. Biometrika, 101, 883–898.
[2] Durante, D. and Dunson, D. B. [2014]. Bayesian dynamic financial networks with time-varying predictors. Statistics & Probability Letters, 93, 19–26.
[1] 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.