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Bandi assegni di ricerca presso POLIMI
by Laura Maria Sangalli 17 Oct '23

17 Oct '23

Vi prego di fare circolare il seguente avviso di bandi di assegni di ricerca SECS/S01 tra i potenziali interessati Bando di selezione per il conferimento di assegni di ricerca per lo svolgimento del programma di ricerca denominato: “STATISTICAL LEARNING PER IL PROGETTO GRINS – GROWING RESILIENT, INCLUSIVE AND SUSTAINABLE.WP2 PROGETTO GRINS – PNRR PE9 SPOKE7_2023_ASSEGNI_DMAT_14” Responsabili: Prof. Piercesare Secchi, Prof.ssa Francesca Ieva https://www.polimi.it/personale-docente/lavorare-al-politecnico/bandi-per-a… Scadenza: 18-10-23 Colloquio: 25-10-23 *** Bando di selezione per il conferimento di assegni di ricerca per lo svolgimento del programma di ricerca denominato: “MODELLI E ANALISI PER DATI CLINICI PROVENIENTI DA PROGETTI DELLE RETI CARDIOLOGICA E NEUROLOGICA.ATTIVITÀ WG6 PROGETTO HEALTH BIG DATA-PROTOCOLLO DI INTESA CON ALLEANZA CONTRO IL CANCRO_2023_ASSEGNI_DMAT_12” Responsabile: Prof.ssa Francesca Ieva https://www.polimi.it/personale-docente/lavorare-al-politecnico/bandi-per-a… Scadenza: 18-10-23 Colloquio: 26-10-23 —— 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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One World ABC Seminar: Stefan Radev - 26th October, 1pm Uk time
by Massimiliano Tamborrino 17 Oct '23

17 Oct '23

Dear all, our next One World ABC Seminar www.warwick.ac.uk/oneworldabc<http://www.warwick.ac.uk/oneworldabc> is quickly approaching. We are happy to inform you that our next OWABC speaker is Stefan Radev<https://faculty.rpi.edu/stefan-radev> (Rensselaer Polytechnic Institute, US), who will talk about "More expressive amortized Bayesian inference via joint learning and self-consistency?" on Thursday, the 26th October at 1pm UK time (please notice the different time!) Abstract: We propose “jointly amortized neural approximation” (JANA) to simultaneously learn intractable likelihood functions and posterior densities arising in Bayesian surrogate modeling and simulation-based inference. To this end, we train three complementary networks in an end-to-end fashion: 1) a summary network to compress individual data points, sets, or time series into informative embedding vectors; 2) a posterior network to learn an amortized approximate posterior; and 3) a likelihood network to learn an amortized approximate likelihood. The interaction between these networks opens a new route to amortized marginal likelihood and posterior predictive estimation. Furthermore, we observe that we can increase the efficiency of amortized inference by leveraging the marginal likelihood as a proxy for the self-consistency of Bayes’ rule. Upon perfect approximation, the marginal likelihood is constant across all parameter values by definition. However, approximation error leads to undesirable variance in the marginal likelihood estimates across different parameter values. We formulate violations of this symmetry as a loss function applicable in both likelihood-based and simulation-based scenarios. We demonstrate the utility of these approaches on illustrative Bayesian models, including both simple examples and representative cases. The talk will be streamed on MS Teams, and it can be joined via this link https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZWQwZjg3NTktNjY4OS00… or via Meeting ID: 389 057 974 73 Passcode: H9uMjk We're looking forward to seeing you next week, best, Massimiliano on the behalf of the OWABC Organisers ------ Dr. Massimiliano Tamborrino Associate Professor Department of Statistics University of Warwick https://warwick.ac.uk/tamborrino

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Seminario a Napoli
by epirozzi 16 Oct '23

16 Oct '23

Avviso di Seminario il giorno 25 Ottobre 2023 alle ore 14:30 Anna Vidotto Dipartimento di Matematica e Applicazioni Università degli studi di Napoli FEDERICO II terrà un seminario dal Titolo: The Malliavin-Stein Approach and an Improved Second-Order Poincaré Inequality. Abstract: In this talk we will introduce the so-called Malliavin-Stein approach for obtaining quantitative central limit theorems for functionals of Gaussian fields, that this combination works admirably well was discovered by Nourdin and Peccati in their seminal paper from 2009. Then we will present an improved version of the second-order Gaussian Poincaré inequality, first introduced in Chatterjee (2009) and Nourdin et al. (2009), and consequent applications. Il seminario si terrà nella Sala Professori Primo livello al Dipartimento di Matematica e Applicazioni, Università degli Studi di Napoli FEDERICO II, Complesso di Monte Sant'Angelo, Via Cintia, Napoli. Sarà anche possibile seguirlo online mediante Teams al seguente link: https://teams.microsoft.com/l/meetup-join/19%3aMQ4RZDBo_0G-K_PHxKtktVYAczOG… -- Enrica Pirozzi Professore Associato di Probabilità e Statistica, Dipartimento di Matematica e Applicazioni, Università di Napoli FEDERICO II Via Cintia, 80126, NAPOLI https://www.docenti.unina.it/enrica.pirozzi

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Double seminar: Francesco D'Amore & Isabella Ziccardi at Tor Vergata
by Michele Salvi 16 Oct '23

16 Oct '23

Dear all, On Thursday, October 19th, at 14h00 (change of usual time!) in Aula Dal Passo at the Math Department of Roma Tor Vergata, RoMaDS (https://www.mat.uniroma2.it/~rds/about.php) will host the seminars 14h00 - Francesco D'Amore (Aalto University, Espoo) "The Strong Lottery Ticker Hypothesis and the Random Subset Sum Problem” Abstract: The Strong Lottery Ticket Hypothesis (SLTH) posits that randomly-initialized neural networks contain subnetworks (strong lottery tickets) that achieve competitive accuracy when compared to sufficiently small target networks, even those that have been trained. Empirical evidence for this phenomenon was first observed by Ramanujan et al. in 2020, spurring a line of theoretical research: Malach et al. (2020), Pensia et al. (2020), da Cunha et al. (2022), and Burkholz (2022) have analytically proved formulations of the SLTH in various neural network classes and under different hypotheses. In this presentation, we provide an overview of the state-of-the-art theoretical research on the SLTH and its connection with the Random Subset Sum (RSS) problem in theoretical computer science. While previous works on the SLTH ensure that the strong lottery ticket can be obtained via unstructured pruning, we demonstrate how recent advances in the multidimensional generalization of the RSS problem can be leveraged to obtain forms of structured pruning. Additionally, we highlight how refining the RSS results would yield tighter formulations of the SLTH. This presentation is based on a joint work with Arthur da Cunha and Emanuele Natale that will be presented at NeurIPS 2023. 15h00 - Isabella Ziccardi (Bocconi) "Distributed Self-Stabilizing MIS Algorithms" Abstract: I will discuss self-stabilizing distributed algorithms that find a Maximal Independent Set in an n-vertex graph. These algorithms share the feature of utilizing randomization to break symmetry. I will compare them based on the following parameters: the number of states used by each node, the knowledge of the graph required by each node, and their stabilization time. The first three algorithms are obtained by reconsidering some existing algorithms and making them self-stabilizing by introducing additional states. The first algorithm gets along with three states, but each node must have knowledge of ∆, the maximum node degree. In the second algorithm, nodes only need to be aware of their degree, but each node v has O(∆(v)) states, where ∆(v) is the degree of v. The third algorithm also requires O(∆(v)) states for each node v, but it works in the restricted beeping communication model. All three algorithms stabilize in O(log n) rounds with high probability. Lastly, I will talk about two algorithms that aim to create self-stabilizing algorithms with a constant number of states while requiring no knowledge of the underlying graph. The first algorithm is a natural process that, despite its simplicity, has received limited attention in the literature. It stabilizes in O(polylog(n)) rounds for specific graph families but may exhibit slower convergence time on general graphs. The final algorithm is a modification of this simple process, that tries to fix the particular situations that slow down the convergence. We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming: https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFw…"Tid"%3a"24c5be2a-d764-40c5-9975-82d08ae47d0e"%2c"Oid"%3a"650fc4a8-4cec-4bd2-87bc-90d134074fe6"} <https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFw…> The seminar is part of the Excellence Project MatMod@TOV.

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One world probability seminar October 19th
by Alberto Chiarini 16 Oct '23

16 Oct '23

Ricevo e inoltro: ------------------------------------------------------ Dear all. The next session of the One World Probability Seminar will be on October 19 (Thursday) from 14:00 to 16:00 UTC.The speakers of this session are Martin Hairer (Imperial College and EPFL) and Hao Shen (Wisconsin-Madison). Title, abstract and the zoom link are below the signature and can be also found on the website https://www.owprobability.org/one-world-probability-seminar. Kind regards, Ilya Chevyrev and Julio Backhoff. Talk 1 : Stochastic quantization of Yang-Mills Speaker : Martin Hairer Abstract : We report on recent progress on the problem of building a stochastic process that admits the (hypothetical in 3D) Yang-Mills measure as its invariant measure. One interesting feature of our construction is that it preserves gauge-covariance in the limit even though it is broken by our UV regularisation. This is based on joint work with Ajay Chandra, Ilya Chevyrev, and Hao Shen. Talk 2 : Invariant measure and universality of the 2D Yang-Mills Langevin dynamic Speaker: Hao Shen Abstract : In [CCHS20] by Chandra, Chevyrev, Hairer and S., a Langevin dynamic for 2D Yang-Mills (YM) was constructed on 2D torus. In this talk we discuss some new results based on a joint paper with Chevyrev [CS22]. We prove that the 2D YM measure is invariant for the Langevin dynamic constructed in [CCHS20]. Our argument relies on a combination of regularity structures, lattice gauge-fixing, and Bourgain’s method for invariant measures. In particular, we prove a universality result which states that for a wide class of lattice YM gauge theories, their corresponding Langevin dynamics converge to the same continuum dynamic constructed in [CCHS20]. An important step is a proof of uniqueness for the mass renormalisation of the gauge-covariant continuum Langevin dynamic, which allows us to identify the limit of discrete approximations. As corollaries we obtain a gauge-fixed decomposition of the YM measure into a Gaussian free field and an almost Lipschitz remainder, and a proof of universality for the 2D YM measure under a wide class of discrete approximations. Zoom-link: https://univienna.zoom.us/j/66242024127?pwd=K201d0FGQ1dvZHIzZERlUnNSbUlRQT09 Meeting ID: 662 4202 4127 Passcode: 188885 If you are having trouble with zoom, or if the capacity of the zoom room gets exceeded, you can also access to the Youtube live stream at the channel of the seminar: https://www.youtube.com/channel/UCiLiEQGTp6bZEhuHDM-WNWQ

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special issue of "Decisions in Economics and Finance" on "The Mathematics of Subjective Probability"
by Pietro Rigo 13 Oct '23

13 Oct '23

The Journal "Decisions in Economics and Finance" is announcing a special issue on "The Mathematics of Subjective Probability". The main topics are: Probability theory Theory of choice under uncertainty Game theory and learning Mean fields games Stochastic optimization and control Optimal transport Possible connections with economic phenomena would be greatly appreciated. The special issue follows the conference that took place in Milan in september 2023: https://www.msp2023.campus.unimib.it/ Submission will take place through the journal platform, upon specifying the special issue of interest. The deadline is december 31st 2023. Gianluca Cassese, Federica Masiero, Pietro Rigo, Barbara Vantaggi

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Call for expression of interest -2 research fellowships at University of Cagliari
by Maria Infusino 13 Oct '23

13 Oct '23

Ricevo e inoltro volentieri. ————————————————————————————————————————————————- The Department of Mathematics and Computer Science of the University of Cagliari will shortly open calls for *two research fellowships* for 12 months within a multidisciplinary research project on the study of networks and graph structures. We are seeking for candidates with a background in one of these topics: - inferential statistics, statistical modeling (supervisor Silvia Columbu) - numerical linear algebra (supervisors Caterina Fenu and Alessandro Buccini) - logic (supervisor Stefano Bonzio) - computer science and visualization (supervisor L Davide Spano) Background in the analysis of networks under their different theoretical and applied aspects are highly valued. The positions will be opened at the Department of Mathematics and Computer Science of the University of Cagliari. The official calls will be advertised before the end of 2023, but we encourage potential interested applicants to get in touch with us to express their interest. There is no specific deadline, but we encourage applicants to express their interest before October 31st, 2023. Silvia Columbu (silvia.columbu(a)unica.it <http:///horde/imp/smartmobile.php#compose?to=silvia.columbu%40unica.it>), Caterina Fenu (kate.fenu(a)unica.it <http:///horde/imp/smartmobile.php#compose?to=kate.fenu%40unica.it>), Stefano Bonzio (*stefano.bonzio(a)unica.it <stefano.bonzio(a)unica.it>*), Alessandro Buccini (*alessandro.buccini(a)unica.it <alessandro.buccini(a)unica.it>*), Davide Spano (*davide.spano(a)unica.it <davide.spano(a)unica.it>*) *Brief description of the project:* *GraphNet: models, computation and estimation in networks and graph analysis *is an interdisciplinary research project with the global objective of integrating and developing mathematical, statistical and computer science tools to provide a thorough comprehension of the complex association between graph theory and network science. The primary project's aim is to bridge together the abstract mathematical structure of graphs and the computational and inferential approaches in the description of real-world networks arising from applications. We will deal with 3 objectives: theoretical aspects of graph structures, analysis of case studies, and implementation of appropriate computational and visualization tools.

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Call postdoc @Unisi
by Piero MAZZARISI 13 Oct '23

13 Oct '23

Dear colleagues, I would like to advertise the call for a 18-months postdoc position at the University of Siena on: “Models of dynamic networks for the study of financial systemic risk.” A synthetic description of the project topic can be found here, both in Italian and English: https://titulus-unisi.cineca.it/albo/viewer?view=files/003066882-UNSISIE-20… Deadline for applications: October, 30th 2023. The call as well as instructions for application, can be found here: https://www.unisi.it/node/12841 Do not hesitate to contact me for further information. Best regards, Piero Mazzarisi — Piero Mazzarisi Department of Economics and Statistics University of Siena Personal website: pieromazzarisi.com <https://pieromazzarisi.wordpress.com/>

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seminario Federico Capannoli (16 Ottobre, Sapienza)
by Matteo Quattropani 13 Oct '23

13 Oct '23

Salve a tutti, Segnalo il seguente seminario, che avrà luogo Lunedì 16 Ottobre alle ore 17:00 in Sala di Consiglio (Dipartimento di Matematica, Università La Sapienza): Speaker: Federico Capannoli (Leiden University) Title: Meeting, Coalescence and Consensus on random directed graphs. Abstract: We consider Markovian dynamics on a typical realization of the so-called Directed Configuration Model (DCM), which is a random directed graph with prescribed in- and out-degrees. In this random geometry, we study the meeting time of two random walks starting at stationarity, the coalescence time for a system of coalescent random walks, and the consensus time of the voter model. Indeed, it is known that the latter three quantities are related to each other when the underlying sequence of graphs satisfies certain mean field conditions. We provide a complete characterization of the distribution of meeting, coalescence and consensus time on a typical random graph as a function of a single quantity θ. More precisely we show that, for a typical large graph from the DCM ensemble, the distribution of the meeting time is well-approximated by an exponential random variable. Furthermore, we provide the precise first-order approximation of its expectation, showing that the latter is linear in the size of the graph, and the explicit preconstant θ depends on some easy statistics of the degree sequence. As a consequence, we can analyze the effect of the degree sequence on the expected meeting time and, via some explicit examples, how its regularity/variability play crucial roles in the information diffusion. This is based on a joint work with Luca Avena (University of Florence), Rajat Subhra Hazra (Leiden University) and Matteo Quattropani (Sapienza, University of Rome). Saluti, Matteo Quattropani

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Post-doc position in mathematical statistics in Tartu (Estonia)
by Daniela Bertacchi 12 Oct '23

12 Oct '23

Ricevo e inoltro: ******************************************* *Post-doc position in mathematical statistics in Tartu (Estonia)* https://jobrxiv.org/job/university-of-tartu-27778-postdoc-position-in-mathe… *Announcement* The Institute of Mathematics and Statistics at the University of Tartu is seeking to fill the post of a Postdoctoral Research Fellow in mathematical statistics. The fixed-term position is scheduled for a duration of 1 year, the regular full employment time is 40 hours per week. The institute is located at the University of Tartu Delta Centre, https://delta.ut.ee/en <https://delta.ut.ee/en%E2%80%9D> The successful candidate will join the research project “Statistical modelling with hidden Markov models and model validation” under supervision of Prof Jüri Lember and Ass Prof Kristi Kuljus. Project description: Manyfold Markov models are generalizations of hidden Markov models (HMM). A manyfold Markov model is a multi-dimensional Markov chain, where typically some of the marginal processes are considered as latent variables. Mostly two- or three- dimensional models – called pairwise or triplet Markov models (PMM or TMM) – are studied. Such a general approach yields a large and flexible class of dynamical models, thus, for instance, HMM is just a very narrow subclass of TMMs. The purpose of the project is to study different inference problems related to these Markov models, an important research direction is segmentation or decoding. The research combines theory with practice, involving working out the corresponding probability-theoretical framework as well as necessary dynamic programming algorithms, studying their properties and implementing the methods to real data. Both frequentist and Bayesian settings will be considered. Requirements: Candidates should hold a PhD degree in mathematical statistics or mathematics. Good command of written and spoken English is expected. Candidates are invited to submit their application (CV, list of publications, a scanned copy of the PhD diploma, brief motivation letter, and contacts for at least two references). Application deadline is November 15, 2023; expected start date is January 1, 2024.

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