February 2026 - Random - Mailing list — Dipartimento di Matematica, UniPI

[DEADLINE APPROACHING SOON] PhD and Postdoc opportunities at University of Oslo
by Leonardo Tarquini 27 Feb '26

27 Feb '26

Dear All, Deadline approaching soon for some opportunities within the group of Risk and Stochastics at the Department of Mathematics, University of Oslo. Up to 2 PhD positions in stochastic analysis and computational mathematics. Connection to SURE-AI community Read here: https://www.jobbnorge.no/en/available-jobs/job/294743/phd-research-fellow-i… One Postdoc position in stochastic analysis for research lovers in a simmering group. Read here: https://www.jobbnorge.no/en/available-jobs/job/294745/postdoctoral-research… Deadline for application March 1st, 2026! Please help us distribute this information widely. Best regards, Giulia and Leonardo

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SPASS seminars: G. Cipolloni and R. Fukuizumi - Tue 3 Mar
by Elia Bisi 26 Feb '26

26 Feb '26

Dear Colleagues, We would like to invite you to the following two SPASS seminars (joint organised by UniPi, SNS, UniFi and UniSi) on Tuesday 3rd March: 14.00-15.00: Logarithmically correlated fields from large random matrices (Aula Tricerri, Dipartimento di Matematica e Informatica, Università di Firenze) by Giorgio Cipolloni (Tor Vergata). 15.00-16.00: Phi^4_d model associated with the harmonic oscillator (Aula Riunioni ex-DMA, Dipartimento di Matematica, Università di Pisa) by Reika Fukuizumi (Waseda University, Japan). Both talks will be also streamed online here: meet.google.com/ydw-gsjr-vza On behalf of the organisers, Elia Bisi ------------------- Abstract (Cipolloni): We study the Brownian evolution of large non-Hermitian matrices and show that their log-determinant converges to a 2+1 dimensional Gaussian field in the Edwards-Wilkinson regularity class, i.e. logarithmically correlated for the parabolic distance. This gives a dynamical extension of the celebrated result by Rider and Virag (2006) proving that the fluctuations of the eigenvalues of Gaussian non-Hermitian matrices converge to the Gaussian Free Field. ------------------- Abstract (Fukuizumi): We present recent developments in the mathematical study of the stochastic Gross–Pitaevskii equation, as a mathematical model for Bose–Einstein condensates subject to thermal fluctuations, such as those occurring near the critical temperature of condensation. In this regime, interactions between the condensate and the surrounding thermal cloud of non-condensed atoms must be incorporated in a way consistent with the fluctuation–dissipation principle, ensuring relaxation toward the correct physical equilibrium. The damping and noise terms are precisely balanced so that the dynamics formally admits a Gibbs measure associated with the Gross–Pitaevskii Hamiltonian as an invariant measure. This talk, which will be on the existence of the local / global solution, construction of the Gibbs measure, and long time behaviors, is a summary of several collaborative works with A. de Bouard, A. Debussche, A. Deya, T. Iwabuchi, L. Thomann. ————————— Elia Bisi Assistant Professor (RTT) University of Florence Department of Mathematics & Computer Science Personal homepage Florence Probability group

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OWABI- Shreya Sinha Roy - February 26 - 11am Uk time
by Umberto Picchini 26 Feb '26

26 Feb '26

Dear all, The next OWABI www.warwick.ac.uk/owabi <http://www.warwick.ac.uk/owabi> seminar is quickly approaching. Our next speaker is Shreya Sinha Roy <https://warwick.ac.uk/fac/sci/statistics/staff/research_students/sinharoy/> (University of Warwick), who will talk about "Prequential posteriors" on *Thursday the 26th February at 11am UK* time, with an abstract reported below. The talk will be streamed on the OWABI MS Team channel General | OWABI Seminar: One World Approximate Bayesian Inference Seminar | Microsoft Teams <https://teams.microsoft.com/l/team/19%3AdhZ_4e_XLNJzCXPAMzTvT6BZ5KShEETkd_w…>, at the link below *Join: *_https://teams.microsoft.com/meet/32189045171547?p=HltIhSAIPRyOBZfj2M <https://teams.microsoft.com/meet/32189045171547?p=HltIhSAIPRyOBZfj2M>_ Meeting ID: 321 890 451 715 47 Passcode: Ek7Su3TJ We are looking forward to seeing you at the next OWABI seminar, Best, Umberto on the behalf of the OWABI seminar organisers *Speaker*: Shreya Sinha Roy <https://warwick.ac.uk/fac/sci/statistics/staff/research_students/sinharoy/> (University of Warwick) *Title*: Prequential posteriors *Abstract:* Data assimilation is a fundamental task in updating forecasting models upon observing new data, with applications ranging from weather prediction to online reinforcement learning. Deep generative forecasting models (DGFMs) have shown excellent performance in these areas, but assimilating data into such models is challenging due to their intractable likelihood functions. This limitation restricts the use of standard Bayesian data assimilation methodologies for DGFMs. To overcome this, we introduce prequential posteriors, based upon a predictive-sequential (prequential) loss function; an approach naturally suited for temporally dependent data which is the focus of forecasting tasks. Since the true data-generating process often lies outside the assumed model class, we adopt an alternative notion of consistency and prove that, under mild conditions, both the prequential loss minimizer and the prequential posterior concentrate around parameters with optimal predictive performance. For scalable inference, we employ easily parallelizable wastefree sequential Monte Carlo (SMC) samplers with preconditioned gradient-based kernels, enabling efficient exploration of high-dimensional parameter spaces such as those in DGFMs. We validate our method on both a synthetic multi-dimensional time series and a real-world meteorological dataset; highlighting its practical utility for data assimilation for complex dynamical systems. *Keywords*: diffusion models, simulation based inference, sequential methods. *Reference*: S. S. Roy, R. Everitt, C. Robert, R. Dutta. Prequential posteriors <https://arxiv.org/pdf/2511.17721v1>. Preprint at ArXiv:2511.17721, 2025. -- ____________________________________________________________________ Umberto Picchini, Full Professor of Mathematical Statistics https://umbertopicchini.github.io/ , Bluesky: @upicchini.bsky.social

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Simone Padoan in Venice, Seminar at Ca' Foscari University of Venice - 2 March 2026
by Roberto CASARIN 26 Feb '26

26 Feb '26

Dear Colleagues, Prof. Simone Padoan from Bocconi University will give the seminar *Accurate Bayesian inference for tail risk extrapolation in time series* Monday, 2 March, at 12:15 CET in Meeting Room 1, by the Department of Economics, San Giobbe Campus, Ca' Foscari University of Venice. All the interested people are warmly invited to attend in person or online! Best regards Roberto Casarin It will also be possible to attend on Zoom here: https://unive.zoom.us/j/83496270992?pwd=Oy3eumWCbEZweVbK6aasre418l043U.1 More information available here: https://www.unive.it/data/agenda/3/109019 Title Accurate Bayesian inference for tail risk extrapolation in time series Abstract Temporal dependence is common in many applications, from financial time series to those in climate science, and it affects the behavior of extremes by inducing clustering. As a result, time series data contain less effective information than independent samples, leading to greater uncertainty in extreme value analysis. In this work, we study the Peaks-Over-Threshold (POT) framework, the most widely used method for modeling and estimating univariate extremes, within (strictly) stationary time series under weak mixing conditions. We propose a Bayesian inferential procedure based on the Generalized Pareto (GP) model, specifically tailored to time series settings, which enables extrapolation of both marginal (pertaining to the stationary distribution) and dynamic (conditional on the past) tail risks. We investigate the theoretical accuracy guarantees for our approach and to do so we derived some global properties of the underlying likelihood that had not been previously analyzed. Extensive simulations and real-data applications demonstrate the good inferential performance and practical relevance of the proposed methodology. -- Roberto Casarin, PhD Professor of Econometrics Ca' Foscari University of Venice San Giobbe 873/b - 30121 Venezia, Italy http://sites.google.com/view/robertocasarin/ https://www.unive.it/vera <https://www.unive.it/isba2024>

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Seminar Löcherbach at Tor Vergata, March 2
by Michele Salvi 26 Feb '26

26 Feb '26

Dear all, On Monday, March 2nd, at 14h00 in Aula Dal Passo in the Math Department of the University of Rome Tor Vergata, RoMaDS (https://www.mat.uniroma2.it/~rds/events.php) will host Eva Löcherbach (Université Paris 1 - Panthéon-Sorbonne) with the seminar “Conditional propagation of chaos for mean field systems of interacting neurons” Abstract: Joint work with Dasha Loukianova and Elisa Marini We consider a system of N interacting particles, described by SDEs driven by Poisson random measures, where the coefficients depend on the empirical measure of the system. Every particle jumps with a jump rate depending on its position. When this happens, all the other particles of the system receive a small random kick which is distributed according to a random variable belonging to the domain of attraction of an alpha-stable law, suitably scaled, for some alpha in between 0 and 2. We call these jumps ''collateral jumps''. Moreover, the jumping particle itself undergoes a macroscopic, ''main'' jump. Such systems appear in the modeling of large neural networks, such as the human brain. I will discuss how the system behaves in the large population limit, and how this limiting behavior depends on the value of alpha. In particular, using a representation of the collateral jump sum as a time-changed random walk, I will explain how we obtain convergence in law, in Skorokhod space, of the system to a limit infinite-exchangeable system of SDEs driven by a common stable process (or a common Brownian motion) which arises due to the central limit theorem and constitutes a common noise term in the dynamics of all the limit particles. Finally, I will discuss how this is related to the property of ''conditional propagation of chaos’'. We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming: Seminar Löcherbach | Meeting-Join | Microsoft Teams <https://teams.microsoft.com/meet/31686276169081?p=u3ZZbGzRuTvsF5IifN> The seminar is part of the Excellence Project MatMod@TOV.

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Rajarshi Guhaniyogi and Sharmistha Guha Seminars at Ca' Foscari University of Venice - 4 March 2026
by Roberto CASARIN 26 Feb '26

26 Feb '26

Dear Colleagues, I'm pleased to invite you to the Department Seminars, which will be held on Wednesday, 4th of March, 10:00-12.30 CET, in Meeting Room 1 with the following schedule: - 10:00-11:00 CET: Rajarshi Guhaniyogi (Texas A&M University), *Bridging Statistical, Scientific and Artificial Intelligence: Trustworthy Deep Neural Networks for Complex Structured Data* - 11.30- 12.30 CET: Sharmistha Guha (Texas A&M University), *Neural Network Gaussian Processes for Multiplex Networks: Joint Modeling of Dynamics and Attributes under Partial Observation* All the interested people are warmly invited to attend in person or online! Best regards Roberto Casarin (and on behalf of the Organizing Committee) More information available here: Link Zoom: bit.ly/insem-2425 ID: 880 2639 9452 Passcode: InSem-2425 https://www.unive.it/data/agenda/3/110469 https://www.unive.it/data/agenda/3/110466 Title: Bridging Statistical, Scientific and Artificial Intelligence: Trustworthy Deep Neural Networks for Complex Structured Data Abstract: The explosive expansion of large, structured datasets is radically transforming the landscape of statistical inference, unlocking unprecedented possibilities while simultaneously introducing formidable challenges. Although hierarchical Bayesian methods offer a gold standard for principled inference and rigorous uncertainty quantification, they falter in terms of scalability when confronted with the high dimensionality and sheer scale of modern data. Deep Neural Networks (DNNs) have made remarkable strides on the scalability front, yet their use in the literature remains largely as opaque, black-box that are ill-suited for inference, particularly with structured data. To overcome these barriers, we introduce DNN-based generative models, precisely engineered for two complex and impactful domains: (i) functional output regression with functional and network-valued inputs, and (ii) functional factor modeling for multivariate functional datasets. Our framework leverages the connection between variational deep Gaussian processes and DNNs, delivering transparent and interpretable inference via well-calibrated uncertainty quantification, and achieving rapid, Markov Chain Monte Carlo (MCMC)-free optimization for high-resolution, large-sample scenarios. We reveal how Bayesian nonparametric principles and cutting-edge deep learning can be seamlessly unified, positioning deep Gaussian process priors at the forefront for scalable generative modeling of object-valued data. Demonstrating the framework’s practical strength, we showcase compelling real-world applications, including image-on-image regression and large-scale remote sensing for the carbon-water cycle. This work is in collaboration with the postdoctoral scholar Dr. Yeseul Jeon, as well as scientists from the UC San Francisco Medical School. Title: Neural Network Gaussian Processes for Multiplex Networks: Joint Modeling of Dynamics and Attributes under Partial Observation Abstract: Terrorism networks are dynamic, multiplex, and often partially observed, demanding uncertainty-aware inference. This talk presents Dynamic Joint Learner, a Bayesian framework that jointly models the co-evolution of multiplex layers and node attributes using shared, time-varying latent factors. These latent trajectories are governed by neural network Gaussian processes, combining deep-network expressiveness with principled uncertainty propagation. The method supports predictive inference on hidden links, evolving organizational attributes (size, ideology, leadership, operational capacity), and emergent communities, including friend-foe structures. Simulation studies and an application to interactions among prominent terrorist organizations show improved performance over existing approaches for link prediction, attribute forecasting, and clustering, with calibrated uncertainty. The framework offers a practical toolkit for analysts working with partially observed, co-evolving networks and is broadly applicable beyond counter-terrorism. -- Roberto Casarin, PhD Professor of Econometrics Ca' Foscari University of Venice San Giobbe 873/b - 30121 Venezia, Italy http://sites.google.com/view/robertocasarin/ https://www.unive.it/vera <https://www.unive.it/isba2024>

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Seminar Löcherbach at Tor Vergata, March 2
by Michele Salvi 26 Feb '26

26 Feb '26

Dear all, On Monday, March 2nd, at 14h00 in Aula Dal Passo in the Math Department of the University of Rome Tor Vergata, RoMaDS (https://www.mat.uniroma2.it/~rds/events.php) will host Eva Löcherbach (Université Paris 1 - Panthéon-Sorbonne) with the seminar “Conditional propagation of chaos for mean field systems of interacting neurons” Abstract: Joint work with Dasha Loukianova and Elisa Marini We consider a system of N interacting particles, described by SDEs driven by Poisson random measures, where the coefficients depend on the empirical measure of the system. Every particle jumps with a jump rate depending on its position. When this happens, all the other particles of the system receive a small random kick which is distributed according to a random variable belonging to the domain of attraction of an alpha-stable law, suitably scaled, for some alpha in between 0 and 2. We call these jumps ''collateral jumps''. Moreover, the jumping particle itself undergoes a macroscopic, ''main'' jump. Such systems appear in the modeling of large neural networks, such as the human brain. I will discuss how the system behaves in the large population limit, and how this limiting behavior depends on the value of alpha. In particular, using a representation of the collateral jump sum as a time-changed random walk, I will explain how we obtain convergence in law, in Skorokhod space, of the system to a limit infinite-exchangeable system of SDEs driven by a common stable process (or a common Brownian motion) which arises due to the central limit theorem and constitutes a common noise term in the dynamics of all the limit particles. Finally, I will discuss how this is related to the property of ''conditional propagation of chaos’'. We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming: Seminar Löcherbach | Meeting-Join | Microsoft Teams <https://teams.microsoft.com/meet/31686276169081?p=u3ZZbGzRuTvsF5IifN> The seminar is part of the Excellence Project MatMod@TOV.

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Deep patterns announcement
by Emilio N.M. Cirillo 26 Feb '26

26 Feb '26

Dear Colleagues, We are pleased to announce the workshop Deep Patterns, which will take place from May 18 (10:00) to May 22 (15:00) in EURANDOM (https://www.eurandom.tue.nl/event/deep-patterns/) The workshop aims to foster the emergence of a shared scientific community, encouraging dialogue between diverse methods and viewpoints to better understand the complex nature of patterns and to support progress in an area that is rapidly gaining prominence in modern applied science. We hope many of you will join us! With best regards, Adriano Barra, Emilio Cirillo, C. Spitoni, W. Ruszel -- ************************************ Emilio N.M. Cirillo Dipartimento SBAI Sapienza Universita` di Roma via A. Scarpa 16, 00161 Roma, Italy. Tel.: +39 06 4976 6808 E_mail: emilio.cirillo(a)uniroma1.it ************************************ -- *Fai crescere le giovani ricercatrici e i giovani ricercatori*** *con il 5 per mille alla Sapienza* Scrivi il codice fiscale dell'Università *80209930587 **Cinque per mille <https://www.uniroma1.it/it/node/23149>*

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[STAR Seminars] Wed Feb 25 - Yi Feng
by Leonardo Tarquini 18 Feb '26

18 Feb '26

Dear all, Join us for our upcoming talk in the STAR seminar<https://www.mn.uio.no/math/english/research/projects/storm/events/seminars/…> series on Wednesday, February 25th, from 11:00 to 12:00 (Oslo time). As always, the talk will take place in a hybrid format: participants in Oslo can attend the talk in Room 723 in Niels Henrik Abels hus, whereas the international audience will be able to follow the talk via Zoom<https://uio.zoom.us/j/61698784374>. The speaker is Yi Feng (Aarhus University) with the talk: Title: Continuous-Time Analysis of Heavy Ball Momentum in Min-Max Games Abstract: Since Polyak's pioneering work, heavy ball momentum has been widely studied in minimization problems. However, its role in min-max games remains largely unexplored. As a key component of practical min-max algorithms like Adam, this gap limits their eﬀectiveness. In this talk, I will present a continuous-time analysis for heavy ball momentum in min-max games. I will focus on two aspects: convergence to equilibrium and implicit gradient regularization, which describe how momentum shapes the interaction between algorithmic trajectories and the geometry of the min-max loss landscape. We will see that the behavior of heavy ball momentum in min-max games is significantly diﬀerent from that in minimization. Bio: Yi Feng is a Postdoctoral researcher at Aarhus University. His research focuses on understanding the learning dynamics in games and their applications in machine learning. His research is supported by a Danish Data Science Academy Postdoctoral Fellowship. This seminar is jointly organised in collaboration with Trung Tuyen Truong<https://www.mn.uio.no/math/english/people/aca/tuyentt/> from the Several Complex Variables, Logic and Operator algebras group<https://www.mn.uio.no/math/english/?vrtx=unit-view&areacode=151365> at UiO. We are looking forward to seeing you! Best regards, Giulia, Leonardo, Pere, and David

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SPASS seminar: Tianyi Li
by Mario Maurelli 18 Feb '26

18 Feb '26

Dear colleagues, We would like to invite you to the following SPASS seminar, jointly organized by UniPi, SNS, UniFi and UniSi (abstract below): *Generative Diffusion Models for Lagrangian Turbulence* by *Tianyi Li* (Università degli Studi di Roma Tor Vergata) The seminar will take place on *Tuesday, 24.02.2026* at *14:00 CET* in Saletta Riunioni, Dipartimento di Matematica, UNIPI and streamed online at this link <https://meet.google.com/stc-xonf-yrx>. Best regards Mario Maurelli https://sites.google.com/unipi.it/spass <https://www.google.com/url?q=https://sites.google.com/unipi.it/spass&source…> --------------------------------------- *Title: Generative Diffusion Models for Lagrangian Turbulence* *Abstract: Lagrangian turbulence plays a central role in transport and dispersion, yet modeling the intermittent and non-Gaussian nature of particle trajectories remains a major challenge. In this talk, we present a generative framework based on diffusion models to learn and sample the probability distribution of turbulent particle motion directly from data. The model generates single-particle trajectories in three-dimensional turbulence at high Reynolds numbers and reproduces key statistical properties across scales, including heavy-tailed velocity increments, anomalous scaling, and intermittency near the dissipative range. We further extend the approach to inertial particles (tracers, heavy, and light) and to the probabilistic reconstruction of missing velocity data along trajectories, with applications to real ocean drifter observations. In all cases, the generated signals retain realistic scale-dependent and non-Gaussian features. These results suggest diffusion models as a new probabilistic tool for studying and reconstructing Lagrangian turbulence. This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 882340).*

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