The article: Pozza, Durante and Szabo [2025+]. Skew-symmetric approximations of posterior distributions. has been accepted for publication in the Journal of the Royal Statistical Society, Series B (Statistical Methodology).
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Daniele Durante Ph.D.
Daniele Durante
Associate Professor of Statistics
- Department of Decision Sciences, Bocconi University. Via Roentgen 1, 20136 Milano, Italy. Office 3D1-05.
- daniele DOT durante AT unibocconi DOT it
Short Bio
Welcome to my personal webpage! I am an Associate 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 and structured phenomena, especially in social sciences. For my research I received the following recognitions.
- the Susie Bayarri Lecture [ISBA]
- the COPSS Emerging Leader Award [COPSS]
- the Leonardo da Vinci Medal [Ministry of University and Research]
- the Mitchell Prize (twice) [ISBA and ASA]
- the Laplace prize [SBSS Section of ASA]
- the Byar Award [Biometrics Section of ASA]
- the Early-Career Scholar Award for Contributions to Statistics [SIS]
- the ISBA Lifetime Members Junior Researcher Award [ISBA]
- the Ph.D. Thesis Award in Statistics [SIS]
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 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], as Vice-Chair [2021] and Chair [2022] of the Blackwell-Rosenbluth Award by ISBA, in the ISBA Board of Directors [2024-2026] and in the European Regional Committee of the Bernoulli Society [2025-2029].
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–MUR 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.
Research
For a Complete List of Publications refer to my curriculum vitae and Google Scholar profile. Codes can be found in my GitHub repository.
| [40] Romanò, G., Castiglione, C., and Durante, D. [2025+]. Dependent stochastic block models for age-indexed sequences of directed causes-of-death networks. arXiv:2510.01806. [submitted]. |
| [39] Romanò, G., Aliverti, E., and Durante, D. [2025+]. Bayesian local clustering of age–period mortality surfaces across multiple countries. arXiv:2504.05240. [submitted]. |
| [38] Zens, G., Dìaz, C., Durante, D. and Patacchini, E. [2025+]. Low-rank bilinear autoregressive models for three-way criminal activity tensors. arXiv:2505.01166. [submitted]. |
| [37] Pavone, F., Durante, D. and Ryder, R. [2025+]. Phylogenetic latent space models for network data. arXiv:2502.11868. [submitted]. |
| [36] Anceschi, N., Castiglione, C., Rigon, T., Zanella, G., and Durante, D. [2025+]. Optimal and computationally tractable lower bounds for logistic log-likelihoods. arXiv:2410.10309. [submitted]. |
| [35] Pozza, F., Durante, D. and Szabo, B. [2025+]. Skew-symmetric approximations of posterior distributions. Journal of the Royal Statistical Society, Series B (Statistical Methodology), In Press. |
| [34] Durante, D., Gaffi, F., Lijoi, A. and Pruenster, I. [2025]. Partially exchangeable stochastic block models for (node-colored) multilayer networks. Journal of the American Statistical Association. 120, 1811–1827. |
| [33] Lu, C., Durante, D., and Friel, N.B. [2025+]. Zero-inflated stochastic block modeling of efficiency-security tradeoffs in weighted criminal networks. Journal of the Royal Statistical Society, Series A (Statistics in Society), In Press. |
| [32] Legramanti, S., Durante, D. and Alquier, P. [2025]. Concentration of discrepancy-based approximate Bayesian computation via Rademacher complexity. Annals of Statistics. 53, 37–60. |
| [31] Karling, M.J., Durante, D. and Genton, M.C. [2024]. Conjugacy properties of multivariate unified skew-elliptical distributions. Journal of Multivariate Analysis. 204, 105357. |
| [30] Durante, D., Pozza, F. and Szabo, B. [2024]. Skewed Bernstein-von Mises theorem and skew-modal approximations. Annals of Statistics. 52, 2714–2737. |
| [29] Pavone, F., Legramanti, S. and Durante, D. [2024]. Learning and forecasting of age–specific period mortality via B–spline processes with locally–adaptive dynamic coefficients. Annals of Applied Statistics. 18, 1965–1987. |
| [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] 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. |
| [26] 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. |
| [25] Fasano, A., Durante, D. and Zanella, G. [2022]. Scalable and accurate variational Bayes for high-dimensional binary regression models. Biometrika. 109, 901–919. |
| [24] Legramanti, S., Rigon, T. and Durante, D. [2022]. Bayesian testing for exogenous equivalence structures in stochastic block–models. Sankhya A. 84, 108–126. |
| [23] 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. |
| [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, Series A (Statistics in Society), 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, Series B (Statistical Methodology), 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, Series C (Applied Statistics), 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. |
Recent News
I had the privilege of being selected to deliver the Susie Bayarri Lecture at the 2026 ISBA World Meeting.
Articles' updates!
- The article: Romanò, Aliverti and Durante [2025]. Bayesian local clustering of age-period mortality surfaces across multiple countries is on ArXiv.
- The article: Zens, Dìaz, Durante and Patacchini [2025]. Low-rank bilinear autoregressive models for three-way criminal activity tensors is on ArXiv.
Articles' updates!
- The article: Durante, Gaffi, Lijoi, Pruenster, [2025+]. Partially exchangeable stochastic block models for (node-colored) multilayer networks has been accepted for publication in the Journal of the American Statistical Association.
- The article: Lu, Durante, Friel [2025+]. Zero-inflated stochastic block modeling of efficiency-security tradeoffs in weighted criminal networks has been accepted for publication in the Journal of the Royal Statistical Society: Series A.
- The article: Pavone, Durante and Ryder [2025]. Phylogenetic latent space models for network data is on ArXiv.
I have been promoted to (tenured) Associate Professor!
Articles' updates!
- The article: Anceschi, Rigon, Zanella, and Durante [2024+]. Optimal lower bounds for logistic log-likelihoods is on ArXiv.
- The article: Lu, Durante, Friel [2024+]. Zero-inflated stochastic block modeling of efficiency-security tradeoffs in weighted criminal networks is on ArXiv.
- The article: Durante, Gaffi, Lijoi and Pruenster [2024+]. Partially exchangeable stochastic block models for multilayer networks is on ArXiv.
New article available online on arXiv! Pozza, Durante and Szabo, [2024+]. Skew-symmetric approximations of posterior distributions.
The articles: Durante, Pozza, Szabo [2024+]. Skewed Bernstein-von Mises theorem and skew-modal approximations and Legramanti, Durante, Alquier [2024+]. Concentration of discrepancy-based approximate Bayesian computation via Rademacher complexity have been both accepted for publication in the Annals of Statistics.
Our article: Legramanti, Rigon, Durante and Dunson [2022]. Extended stochastic block models with application to criminal networks (Annals of Applied Statistics) has been awarded the the Mitchell Prize assigned by ISBA to "an outstanding paper that describes how a Bayesian analysis has solved an important applied problem".
I have been awarded the the COPSS Emerging Leader Award assigned by the Committee of Presidents of Statistical Societies [COPSS] to "early career statistical scientists who show evidence of and potential for leadership and who will help shape and strengthen the field".
Congratulations also to my former Ph.D. student Francesco Pozza for receiving the 2024 ASA-SBSS Laplace Award.
Articles' updates!
- The article: Pavone, Legramanti, Durante [2024]. Learning and forecasting of age–specific period mortality via B–spline processes with locally–adaptive dynamic coefficients has been accepted for publication in the Annals of Applied Statistics.
- The article: Karling, Durante, and Genton. [2024+]. Conjugacy properties of multivariate unified skew-elliptical distributions is now online on ArXiv.
I have been recently awarded two prestigious grants and I will be hiring soon!
Send me an e–mail if you are interested in knowing more about one of these projects.Congratulations to the winners of the 2022 Blackwell-Rosenbluth Award by j-ISBA. It has been a honor to serve as chair in the 2022 scientific committee, and as vice-chair in 2021 edition.
I have been awarded the Bocconi Excellence in Teaching Award for my Machine Learning course in the MSc on Data Science and Business Analytics.
I have joined the Editorial Board of the Journal of Multivariate Analysis as Associate Editor. Currently, I serve as Associate Editor also of Biometrika and of the Journal of Computational and Graphical Statistics.
I have been awarded the first edition of the Early-Career Scholar Award for Contributions to Statistics assigned each year by the Italian Statistical Society [SIS] to the best statistician in Italy under the age of 40.
Highlights and Projects
TEDx talk. In 2018, I have been invited to give a TEDx talk on network science. The title of the talk is The Hidden Geometry of our Relations and can be watched here.
Stats under the Stars [SUS]. I am the founder and one of the leading organizers of Stats under the Stars [SUS], a series of Data Science hackathons for university students organized annually, since 2015, all over Italy and endorsed by the Italian Statistical Society [SIS]. See SUS1, SUS2, SUS3, SUS4, SUS5 and SUS6.
StartUpResearch [SUR]. I am one of the leading organizers of StartUpResearch [SUR] a 2-days research meeting where groups of junior researchers (Ph.D. and postdocs), advised by international senior scholars, met to develop new methods and models to analyze datasets from neuroscience. See here the Springer book Studies in Neural Data Science which collects the research outputs of SUR. See also the Significance article about this initiative.
Junior Bayes Beyond the Borders. I am one of the founders and organizers of the world webinar series Junior Bayes Beyond the Borders [JB^3], founded in 2020 to transform the restrictions of the COVID-19 pandemic into an opportunity to offer visibility to outstanding junior researchers worldwide. The initiative has then continued as an online dynamic environment where junior scholars can present their research beyond space-time-budget barriers.
Data Science Challenges Platform. I am the creator and developer [in collaboration with BUILT] of the Bocconi Data Science Challenges Platform, an online training environment for data–analytics challenges that can be used by students as a gaming and learning experience in the context of the Data Science courses. For this platform, I have received in 2020 the Bocconi Innovation in Teaching Award.