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Call for expressions of interest in a research fellow position at the University of Milano-Bicocca
by marina pireddu 03 Jul '23

03 Jul '23

Dear All, we would like to advertise a research fellowship, related to PRIN 2022 "Evolutionary Poisson Games: Theory and Applications", that will soon be available at the University of Milano-Bicocca, of the duration of 18 months, starting from the beginning of 2024. The net monthly salary will be about 1490 euros. The main goal of the research activity is the investigation of socio-economic phenomena by means of an evolutionary game theoretic approach, which leads to nonlinear dynamic models, to be studied via analytical, simulative and computational methods, in particular in relation to their stability, bifurcations and chaotic behavior. The research unit is composed by Fausto Cavalli, Ahmad Naimzada, Daniela Visetti and myself. Expressions of interest can be sent to marina.pireddu(a)unimib.it Best regards, Marina Pireddu

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RTDA STATISTICA POLIMI
by Laura Maria Sangalli 01 Jul '23

01 Jul '23

E' appena stata bandita una posizione RTD-A presso il Dipartimento di Matematica del Politecnico di Milano nel settore SECS-S/01 sul tema: "Sviluppo e implementazione di modelli e algoritmi per analisi di dati complessi". Selezione pubblica per l'assunzione di 1 Ricercatore a tempo determinato (Junior) ai sensi dell'art. 24, comma 3, lettera a) Legge 30.12.2010, n. 240, Tempo pieno - Settore Concorsuale 13/D1 - STATISTICA, bandita con Decreto Direttoriale n. 6288 del 31/05/2023, pubblicato sulla G.U. del 20/06/2023, n. 46 (Codice procedura: 2023_RTDA_DMAT_4). Scadenza: 20-07-23 Dettagli della procedura: https://www.polimi.it/personale-docente/lavorare-al-politecnico/bandi-e-con… Programma di Ricerca: ll mondo della data science è attualmente impegnato nello sviluppo di strumenti per l'analisi di dati complessi (ad es. dati ad alta dimensione, gerarchici, non euclidei o con dipendenza spaziale e/o temporale) provenienti da diversi campi di applicazione. In questo contesto, si sono resi necessari modelli e algoritmi flessibili per fare esplorazione, inferenza e predizione in modo affidabile.La ricerca si concentrerà sullo sviluppo, l'implementazione e la validazione di modelli e algoritmi nell'ambito dello Statistical Learning con l'obiettivo di migliorare l'efficienza statistica e computazionale rispetto all'attuale stato dell’arte. La proposta è coerente con il tema del PNRR Ricerca e TT, High Performance Computing, Quantum Computing e riguarda tematiche di ricerca relative alla linea 'matematica per lo sviluppo sostenibile' del progetto del Dipartimento di Eccellenza del DMAT (www.mate.polimi.it/eccellenza/<http://www.mate.polimi.it/eccellenza/>). Per informazioni: Prof. Simone Vantini – simone.vantini(a)polimi.it<mailto:francesca.ieva@polimi.it> —— Laura Maria Sangalli MOX - Dipartimento di Matematica Politecnico di Milano Piazza Leonardo da Vinci 32 20133 Milano - Italy (+39) 02 2399 4554 laura.sangalli(a)polimi.it<mailto:laura.sangalli@polimi.it> https://sangalli.faculty.polimi.it

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Estensione deadline special issue DEAF
by salvatore federico 30 Jun '23

30 Jun '23

Buongiorno, comunico che la deadline per la special issue DEAF "Advances in Optimal Control and Dynamic Games in Economics, Finance and Insurance" https://www.springer.com/journal/10203/updates/23587054 <https://www.springer.com/journal/10203/updates/23587054> di cui era stato dato annuncio in precedenza alla lista, è stata estesa al 30 settembre 2023 Salvatore Federico

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Seminario di Giovanni Conforti a Roma Tre il 7 luglio 2023
by Pietro Caputo 30 Jun '23

30 Jun '23

Venerdì 7 luglio 2023 alle ore 14:30, Aula M3 Dipartimento di Matematica e Fisica di Roma Tre, Largo San Leonardo Murialdo,1- 00146 Roma. Speaker: Giovanni Conforti (CMAP Ecole Polytechnique, Paris) Title: Stable convexity profiles for Hamilton Jacobi Bellman equations and applications to entropic optimal transport. Abstract: It is well known that solutions of the Hamilton-Jacobi-Bellman (HJB) equation with a convex terminal condition remain convex at all times. In this sense, the set of convex functions is stable under the action of the HJB equation and this result can be derived as a simple consequence of the Prékopa-Leindler inequality. In this talk I will illustrate a probabilistic method based on the analysis of coupling by reflection on a system of forward-backward stochastic differential equations (FBSDEs) that prompts the existence of stable sets for the HJB equation composed of functions that are only "asymptotically convex", but not necessarily convex. If time allows, I will move on to illustrate two consequences of this result to the Schrödinger problem, a.k.a. entropic optimal transport problem. The first is that the optimal coupling satisfies the logarithmic Sobolev inequality and the second one is that Sinkhorn's algorithm converges exponentially fast in the number of iterations to the optimal coupling. Partially based on joint work with A.Durmus and G.Greco

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Assegno di ricerca annuale - One-year post-doc position - Università Ca' Foscari Venezia
by Antonella Basso 30 Jun '23

30 Jun '23

Segnalo il seguente assegno di ricerca presso il Dipartimento di Economia dell’Università Ca’ Foscari Venezia Titolo: Modelli di reti neurali per l’analisi di dati finanziari cross-sectional nel tempo, con applicazione alla valutazione dei prezzi delle abitazioni in Italia Il principale obiettivo del progetto è di studiare l’adattabilità di adeguati modelli a reti neurali ad un contesto dinamico che considera però dati longitudinali e non serie storiche temporali. SSD e/o settore concorsuale: SECS-S/06 Responsabile scientifico e tutor: Prof.ssa Antonella Basso durata: 12 mesi SCADE IL 17/07/2023 ORE 12:00 ORA ITALIANA: Link: https://www.unive.it/data/12138/ -------------------- I would like to point out the following research grant (post-doc) at the Department of Economics of Ca' Foscari University of Venice Subject: Neural networks models for pooled cross-sectional financial data across time, with application to housing pricing in Italy The main goal of the project is to study the adaptability of appropriate neural network models to a dynamical context which considers longitudinal data instead of time series data. Duration: 12 months Deadline: 17/07/2023 at 12:00 Italian time Link: https://www.unive.it/data/12138/ Antonella Basso -- Antonella Basso Dipartimento di Economia Università Ca' Foscari Venezia Fondamenta S. Giobbe - Cannaregio 873 30121 Venezia - Italy Tel. +39-041-2346914 E-mail address:basso@unive.it Web page:https://www.unive.it/data/persone/5591751

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Seminario di Wei Wu in Sapienza il 4 Luglio
by Lorenzo Taggi 29 Jun '23

29 Jun '23

Si comunica che Wei Wu (NYU Shangai) terrà un seminario presso il Dipartimento di Matematica di Sapienza Università di Roma in Sala di Consiglio, Martedì 4 Luglio ore 14:00, dal seguente titolo e abstract: Title: Massless phases for the Villain model in d>=3 Abstract: The XY and the Villain models are models which exhibit the celebrated Kosterlitz-Thouless phase transitions in two dimensions. The spin wave conjecture, originally proposed by Dyson and by Mermin and Wagner, predicts that at low temperature, spin correlations of these models are closely related to Gaussian free fields. I will review the historical background and discuss some recent progress on this conjecture in d>=3. Based on the joint work with Paul Dario (CNRS).

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Expression of interest for postdoc positions
by Daniele Cappelletti 29 Jun '23

29 Jun '23

Dear all, We are asking for expressions of interest in postdoctoral positions that will soon be available at the Polytechnic University of Turin, Polytechnic University of Milan, or Pisa university. The hired postdocs will develop mathematical results in the setting of stochastic reaction networks, which are a family of continuous-time Markov chains with polynomial transition rates extensively used as models in biochemistry. Specifically, the hired postdocs will work on stochastic process convergence, stationary behaviour (existence and characterization), and/or extensions of the classical models to include spatial heterogeneity (stochastic diffusions, processes on a graph, etc). More details on the project can be found here <https://constrained.polito.it/>. To send an expression of interest, please use this form <https://questionari.polito.it/442865?lang=en>. For any further question, please write to constrained(a)polito.it Daniele, Andrea, Luisa

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(PMS)^2 talk - Cazzaniga - July 3 at 17.00
by Carlo Orrieri 27 Jun '23

27 Jun '23

Dear colleagues, We are happy to announce the following hybrid - that is, in person with online streaming - talk: Speaker: Alberto Cazzaniga (Area Science Park) Title: What is the probability that a random symmetric tensor is close to rank-one? Abstract: We address the problem of estimating the probability that a real symmetric tensor is close to rank-one tensors, motivated by the many applications of low-rank approximation. We discuss how the question can be addressed by studying metric invariants of the real Veronese variety thanks to the Weyl's tube formula. We describe the role of the reach and the curvature coefficients with respect to the Bombieri-Weyl metric in obtaining an explicit estimate, and outline the main ideas employed to tackle their calculation. We conclude by discussing some asymptotic results for the case of rational normal curves. Based on joint work with A. Lerario and A. Rosana. Date and time: Monday July 3, 17:00-18:00 (Rome time zone) Place: Laboratorio didattico, dipartimento di matematica dell’università di Pavia, via Ferrata 5, Pavia. Entra nella riunione in Zoom https://us02web.zoom.us/j/81154155086?pwd=Rit2Ymd3eE1lUWIrc0ErQlRMcGNVdz09 <https://us02web.zoom.us/j/81154155086?pwd=Rit2Ymd3eE1lUWIrc0ErQlRMcGNVdz09> ID riunione: 811 5415 5086 Passcode: 317594 This talk is part of the (PMS)^2: Pavia-Milano Seminar series on Probability and Mathematical Statistics organized jointly by the universities Milano-Bicocca, Pavia, Milano-Politecnico. Participation is free and welcome! Best regards The organizers (Carlo Orrieri, Maurizia Rossi, Margherita Zanella)

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Seminars in Probability and Finance - Giulia Livieri (LSE London) & Federico Sau (University of Trieste)
by Alberto Chiarini 27 Jun '23

27 Jun '23

========================================================== PADOVA SEMINARS IN PROBABILITY AND FINANCE webpage: http://www.math.unipd.it/~bianchi/seminari/ ========================================================== Dear Colleagues, We would like to invite you to the "Padova seminars in Probability and Finance" this Friday, June 30 starting from 14.30 in Room 2BC30 at the department of Mathematics of the University of Padova. The seminar can also be followed via Zoom from a link to be found at https://www.math.unipd.it/~bianchi/seminari/ This week we will have two contributions: 14:30 - Giulia Livieri (LSE London) 15:30 - Federico Sau (University of Trieste) See further details below. The organizers _____________________________________________________ Speaker: Giulia Livieri (LSE London) Title: Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis Abstract: Causal operators (CO), such as various solution operators to stochastic differential equations, play a central role in contemporary stochastic analysis; however, there is still no canonical framework for designing Deep Learning (DL) models capable of approximating COs. This paper proposes a "geometry-aware'" solution to this open problem by introducing a DL model-design framework that takes suitable infinite-dimensional linear metric spaces as inputs and returns a universal sequential DL model adapted to these linear geometries. We call these models Causal Neural Operators (CNOs). Our main result states that the models produced by our framework can uniformly approximate on compact sets and across arbitrarily finite-time horizons Hölder or smooth trace class operators, which causally map sequences between given linear metric spaces. Our analysis uncovers new quantitative relationships on the latent state-space dimension of CNOs which even have new implications for (classical) finite-dimensional Recurrent Neural Networks (RNNs). We find that a linear increase of the CNO's (or RNN's) latent parameter space's dimension and of its width, and a logarithmic increase of its depth imply an exponential increase in the number of time steps for which its approximation remains valid. A direct consequence of our analysis shows that RNNs can approximate causal functions using exponentially fewer parameters than ReLU networks. Speaker: Federico Sau Title: Spectral gap of the symmetric inclusion process Abstract: In this talk, we consider the symmetric inclusion process on a general finite graph. Our main result establishes universal upper and lower bounds for the spectral gap of this interacting particle system in terms of the spectral gap of the random walk on the same graph. In the regime in which the gamma-like reversible measures of the particle system are log-concave, our bounds match, yielding a version for the symmetric inclusion process of the celebrated Aldous'spectral gap conjecture --- originally formulated for the interchange process and proved by Caputo, Liggett and Richthammer (JAMS 2010). Finally, by means of duality techniques, we draw analogous conclusions for an interacting diffusion-like unbounded conservative spin system known as Brownian energy process, which may be interpreted as a spatial version of the Wright-Fisher diffusion with mutation. Based on a joint work with Seonwoo Kim (SNU, South Korea). The Zoom meeting can be joined via the link to be found at https://www.math.unipd.it/~bianchi/seminari/

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June 29 - OWABC seminar - Jonas Arruda and Yannik Shälte
by Massimiliano Tamborrino 27 Jun '23

27 Jun '23

Dear all, the last One World ABC Seminar<https://www.warwick.ac.uk/oneworldabc> of the season is behind the corner! On Thursday, June 29, at 9.30am UK time, Jonas Arruda and Yannik Shälte (University of Bonn) will talk about "Amortized simulation-based inference for non-linear mixed-effects models" with an reported below. Abstract: Non-linear mixed-effects models are a powerful tool for studying heterogeneous populations in biology, medicine, engineering, and other fields. However, fitting these models is computationally challenging when the description of individuals is complex and the population is large. To address this issue, we propose the use of neural density estimation to approximate entity-specific posterior distributions in an amortized fashion. This approximation is then used to efficiently infer population-level parameters. Applying this approach to problems from single-cell biology, we demonstrate its scalability to large datasets in an unprecedented manner. Moreover, it enables accurate uncertainty quantification, simple testing of hypotheses, and extends to stochastic models, going beyond what established methods can give. In summary, our method constitutes a highly flexible novel approach has the potential to substantially improve the analysis of heterogeneous populations. The talk will be hosted on MS Teams here: https://teams.microsoft.com/l/meetup-join/19%3ameeting_OTYyY2FkMWQtZTkwNi00… We are looking forward to seeing you soon, best, Massimiliano on the behalf of the One World ABC Seminar Organisers ------ Dr. Massimiliano Tamborrino Associate Professor Department of Statistics University of Warwick https://warwick.ac.uk/tamborrino

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