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seminario Emilio Porcu a Roma Tor Vergata
by Anna Vidotto 30 Oct '19

30 Oct '19

Dear all, I would like to announce the following talk that will be held on Thursday *November* *7th* 2019, at *5 PM*, in *Aula D’Antoni* of the Department of Mathematics, University of Rome Tor Vergata. *Speaker:* Emilio Porcu (Trinity College Dublin) *Title:* Modeling Temporally Evolving and Spatially Globally Dependent Data *Abstract:* The last decades have seen an unprecedented increase in the availability of data sets that are inherently global and temporally evolving, from remotely sensed networks to climate model ensembles. This paper provides an overview of statistical modeling techniques for space–time processes, where space is the sphere representing our planet. In particular, we make a distinction between (a) second order‐based approaches and (b) practical approaches to modeling temporally evolving global processes. The former approaches are based on the specification of a class of space–time covariance functions, with space being the two‐dimensional sphere. The latter are based on explicit description of the dynamics of the space–time process, that is, by specifying its evolution as a function of its past history with added spatially dependent noise. We focus primarily on approach (a), for which the literature has been sparse. We provide new models of space–time covariance functions for random fields defined on spheres cross time. Practical approaches (b) are also discussed, with special emphasis on models built directly on the sphere, without projecting spherical coordinates onto the plane. We present a case study focused on the analysis of air pollution from the 2015 wildfires in Equatorial Asia, an event that was classified as the year's worst environmental disaster. The paper finishes with a list of the main theoretical and applied research problems in the area, where we expect the statistical community to engage over the next decade. Kind regards, Anna Vidotto --------- Anna Vidotto PostDoc Researcher Dipartimento di Matematica Università degli Studi di Roma Tor Vergata https://sites.google.com/view/annavidotto

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postdoc in Trento
by Carlo Orrieri 30 Oct '19

30 Oct '19

Dear all, The Department of mathematics at Università di Trento, invites applications for a postdoctoral position in all the areas of mathematics. Details of the postdoctoral position and information for applicants are available at the call for applications: https://www.unitn.it/ateneo/bando/60412/dipartimento-di-matematica-avviso-d… <https://www.unitn.it/ateneo/bando/60412/dipartimento-di-matematica-avviso-d…> Deadline for applications: 2 DECEMBER 2019 at 12.00 (Italian time). Best wishes Carlo Orrieri

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Fwd: 3d Physics Informed Machine Learning - January 13-17, 2020
by Mateusz Wilinski 29 Oct '19

29 Oct '19

Workshop for those interested in applying Machine Learning to physical problems. The organizers also provide a number of travel grants. Best, Mateusz ---------- Forwarded message --------- Da: Andrey Lokhov <andrey.lokhov(a)gmail.com> Date: mar 29 ott 2019 alle ore 02:12 Subject: 3d Physics Informed Machine Learning - January 13-17, 2020 To: Michael Chertkov <chertkov(a)math.arizona.edu>, Lokhov, Andrey < lokhov(a)lanl.gov>, Mohan, Arvind Thanam <arvindm(a)lanl.gov> Dear Colleagues, We would appreciate if you could share the workshop announcement below with your colleagues, and attract the attention of graduate students and postdocs to the available travel grants. Thank you, Michael Chertkov, Andrey Lokhov, Arvind Mohan ------------------- The Center for Nonlinear Studies and The Information Science & Technology Institute at Los Alamos National Laboratory are pleased to announce an upcoming conference: [image: 3ea459d06f4f4153b23454d39692e8df.jpg] *3d Physics Informed Machine Learning* *January 13-17, 2020* *Inn and Spa at Loretto, Santa Fe, New Mexico* *Workshop website: **cnls.lanl.gov/piml2020* <http://cnls.lanl.gov/piml2020> Previous editions of the workshop are available at cnls.lanl.gov/piml2018 and cnls.lanl.gov/piml2016. *Topics:* - Theory and Methods *(Monday and Tuesday morning)* Deep learning, graphical models, optimization, tensor and message-passing methods, statistical learning - Algorithms and Applications *(Tuesday afternoon and Wednesday)* Physics, computer science, biology, dynamical systems, novel computational paradigms (quantum, memristors), etc. - Special Topic: Spatio-Temporal Flows *(Thursday and Friday morning)* Dynamical systems, fluid mechanics and turbulence *Important Dates:* - Financial support requests: *November 15, 2019* - Abstract submissions: *November 15, 2019* - Travel grant awards decisions: *December 6, 2019* - Workshop dates: *January 13-17, 2020* *Travel Grants for Students and Postdocs:* A limited number of travel grants are available for selected graduate students and postdocs to help offset the cost of attending the conference. Those interested should submit an application, including poster abstract and CV, before the deadline of *November 15, 2019*. We will make funding decisions by *December 6, 2020*. Please submit your application to: *piml2020(a)lanl.gov* <piml2020(a)lanl.gov>. *Posters:* Participants are encouraged to submit a poster, which will be displayed throughout workshop. We will also schedule a slot for poster discussions, tentatively scheduled for Monday, the first day of the workshop. Depending on the schedule, some posters will be selected for longer contributed presentations throughout the week. If you are applying for a travel grant, then you are required to submit your poster abstract by Friday, November 15, 2019. *Summary:* This workshop continues discussions and explorations started in January 2016 and January 2018 at the first and second editions of the workshop. A revolution in statistics and machine learning (ML) is underway. Modern algorithms can now learn high level abstractions via hierarchical models, leading to break-through accuracy in benchmarks for computer vision, language, etc. Underlying these advances is a strong and deep connection to various aspects of applied mathematics and statistical physics. For example, proper choice of statistical force allows to screen interaction and learn graphical models governing multi-dimensional distributions efficiently, gauge transformations from physics guide incorporation of symmetries in the neural network design, dynamical system interpretation helps to understand and improve performance of most efficient deep learning schemes, etc. This workshop seeks perspectives on leveraging the deep connection between ML and physics, but now with the goal to better understand and model physical systems, static and dynamic. We invite experts both in machine learning techniques as well as domain science applications. In this third edition of PIML we will focus on applications of interest to our LANL and DOE sponsors, specifically on improving scale-reduced, large eddy modeling and simulations of turbulence that arise in various mechanical-engineering, aerospace and climate applications; building reduced models for infrastructures (energy systems, cyber-physical systems, etc.); guiding development of inverse/design approaches in quantum physics, e.g. related to tensor networks; designing new computational paradigms (such as related to quantum and neuromorphic computing). The workshop discussions are aimed towards approaches and methods for physical modeling applications where a big-data, black-box approach to ML is only a starting point. We seek participants who may suggest innovative approaches that extend application agnostic ML techniques by incorporating complex constraints imposed by physical principles (e.g. conservation laws, causality, symmetries, entropy principles and related). The workshop format assumes 7 talks a day, late afternoon discussions and poster session. We encourage application of young researchers for fellowships (see website for details). We plan for active participation of LANL researchers and program managers across directorates and divisions interested in the physics informed learning. This emerging area of research has many aspects of computational co-design, and draws on LANL's strengths in statistical physics, modern applied mathematics, theoretical and applied computer science, infrastructure modeling and simulations, fluids and materials modeling, and high performance computing. Looking forward, we view physics informed learning as a viable path for LANL and DOE toward truly predictive multi-scale modeling, optimization, inference and leading for plethora of relevant applications in sciences and engineering. *Organizing Committee:* Michael Chertkov (University of Arizona) Andrey Lokhov (Los Alamos National Laboratory) Arvind Mohan (Los Alamos National Laboratory) [image: bed3f21384084acb905f2c173a3fa658.jpg]

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seminario Claudio Macci a Pisa
by Dario Trevisan 28 Oct '19

28 Oct '19

Dear all, this is to announce the following research talk by Claudio Macci, to be held in Pisa. PLACE: Sala Seminari, Dipartimento di Matematica, Università di Pisa (first floor). DATE & TIME: Wednesday, Nov 6, 2019, 3 PM SPEAKER: Claudio Macci (Università Roma Tor Vergata) TITLE: Multivariate fractional (possibly compound) Poisson processes ABSTRACT: we present multivariate space-time fractional Poisson processes by considering common random time-changes of a finite-dimensional vector of independent classical (nonfractional) Poisson processes. In some cases we also consider compound processes. We obtain some equations in terms of some suitable fractional derivatives and fractional difference operators, which provides the extension of known equations for the univariate processes. In particular we show that, for each fixed $t\geq 0$, the conditional joint distribution of the components given their sum is multinomial, as happens for the nonfractional Poisson processes. Thus we consider a suitable multivariate "alternative" fractional Poisson process, and we study its asymptotic behavior for large times (large and moderate deviations). The results appear in two papers with Luisa Beghin. Best, Dario -- Dario Trevisan, Università degli Studi di Pisa, Dipartimento di Matematica, Largo Bruno Pontecorvo 5, 56127 - Pisa (PI) Italy telephone: (+39) 050 2213832 mobile: (+39) 331 2899761 Skype: dario-trevisan e-mail: dario.trevisan AT unipi.it webpage: http://people.dm.unipi.it/trevisan/

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assegno di ricerca - Pavia
by Enrico Priola 28 Oct '19

28 Oct '19

Cari colleghi, segnalo l'assegno di ricerca, SSD MAT/06 (Probabilità e Statistica Matematica), titolo: "Processi Stocastici'' (scadenza 27/11/2019) http://bandi.miur.it/bandi.php/public/fellowship/id_fellow/164453 L'assegno è bandito all'interno del progetto di eccellenza MIUR presso il Dipartimento di Matematica dell'Università di Pavia. Grazie per l'attenzione. Enrico Priola -- Enrico Priola Dipartimento di Matematica, Università di Pavia Via Adolfo Ferrata 5, 27100 Pavia tel +39 0382 985639

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Cambio aula - Colloquium di Lorenzo Zambotti a Milano-Bicocca (lunedì 28 ottobre)
by Francesco Caravenna 25 Oct '19

25 Oct '19

Comunico il seguente cambio di edificio / aula: Il colloquium di Lorenzo Zambotti a Milano-Bicocca, lunedì 28 ottobre 2019 ore 16:00, si terrà nell'aula U3-07 dell'edificio U3 (piano terra, Piazza della Scienza), 5 minuti a piedi dal Dipartimento di Matematica e Applicazioni. Il colloquium sarà seguito da un rinfresco. Segue l'annuncio completo. Francesco Caravenna %%%%%%%%%%%%%%%%%%%%%%%%%%%% Colloquium del Dipartimento di Matematica e Applicazioni, Università di Milano-Bicocca & Seminario Matematico e Fisico di Milano Lorenzo Zambotti (Sorbonne Université, Paris) "Regularity structures: from physics to probability, from analysis to algebra, from combinatorics to geometry" lunedì 28 ottobre 2019 ore 16:00, aula U3-07, edificio U3 (piano terra, Piazza della Scienza, Milano) Abstract: In this talk I wish to present some of the ideas at the heart of the theory of regularity structures (RS), introduced by Martin Hairer in 2014. RS are perhaps best described as a theory of Taylor expansions in a fractal (random) setting. I plan to show how this theory is based on a fascinating interplay between several different disciplines, as announced by my title. %%%%%%%%%%%%%%%%%%%%%%%%%%%% -- _________________________________________ Francesco Caravenna Dipartimento di Matematica e Applicazioni Università degli Studi di Milano-Bicocca Via Cozzi 55, 20125 Milano, Italy http://www.matapp.unimib.it/~fcaraven/ _________________________________________

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Fwd: Call for applications, Integrable Probability Summer School
by Cristian Giardina 25 Oct '19

25 Oct '19

---------- Forwarded message --------- From: Naomi Kraker <kraker(a)maths.ox.ac.uk> Date: Fri, Oct 25, 2019 at 2:47 PM Subject: Call for applications, Integrable Probability Summer School To: Naomi Kraker <admin(a)claymath.org> The CMI-HIMR Integrable Probability Summer School will be held 27 July-7 August 2020 at the Mathematical Institute, University of Oxford. We would be grateful if you would circulate the below announcement to anyone who might be interest in applying. Thank you very much. With best regards, Naomi Naomi Kraker Administrative Manager Office of the President, Clay Mathematics Institute Andrew Wiles Building Radcliffe Observatory Quarter Woodstock Road Oxford OX2 6GG U.K. Email: admin(a)claymath.org Tel: +44 (0)1865 615155 www.claymath.org *CMI-HIMR Integrable Probability Summer School* *27 July – 7 August 2020* *Mathematical Institute, University of Oxford* The domain of integrable probability seeks to understand universal phenomena through exactly solvable probabilistic systems of algebraic and representation theoretic origin. This two-week postgraduate mathematics summer school will feature mini-courses and research talks on methods to find and analyze such solvable systems, as well as on more probabilistic approaches to expand the universality classes around them. Eight mini course speakers will each deliver three hours of lectures. Daily problem sessions and exercise presentations will help to reinforce the material from the courses. In addition to the mini course lectures, guest speakers will present research talks designed to complement the mini course topics. *Organisers: *Alexei Borodin (MIT) and Ivan Corwin (Columbia) *Mini course lecturers: *Jinho Baik (Michigan), Hugo Duminil-Copin (IHES), Vadim Gorin (MIT and Wisconsin, Madison), Rick Kenyon (Yale), Greta Panova (USC), Jeremy Quastel (Toronto), Fabio Toninelli (Lyon 1), Michael Wheeler (Melbourne) *Research Talk Speakers: * Amol Aggarwal (Harvard), Evgeni Dimitrov (Columbia), Yan Fyodorov (KCL), Alan Hammond (UC Berkeley), Alisa Knizel (Columbia), Pierre Le Doussal (ENS Paris), Leonid Petrov (Virginia), Tomohiro Sasamoto (Tokyo Inst Tech), Li-Cheng Tsai (Rutgers), Jon Warren (Warwick), Nicos Zygouras (Warwick) For graduate students or early career researchers who work in this area or nearby fields. *Registration: * http://claymath.org/events/cmi-himr-integrable-probability-summer-school Student places are offered with accommodation and weekday meals. This summer school is jointly funded by the Clay Mathematics Institute and the Heilbronn Institute for Mathematical Research, with support from the Mathematical Institute of the University of Oxford.

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Seminario a Padova: Alekos Cecchin
by Markus 25 Oct '19

25 Oct '19

Seminario di Analisi e Probabilità all'Università di Padova Martedì 29 ottobre 2019 alle ore 14:30 in aula seminario 430 del Dipartimento di Matematica (via Trieste, 63, 35121 Padova) Alekos Cecchin (Université de Nice Sophia-Antipolis, France) terrà un seminario dal titolo "Selection by vanishing common noise for potential finite state mean field games" Abstract: By adding a suitable Wright-Fisher common noise in the dynamics of the law, uniqueness is restored for finite state mean field games, for any shape of the costs. We then focus on a class of mean field games in which the costs admit a potential in the simplex of probability measures. Firstly, by modifying the cost in the mean field game, we show that the unique solution of the randomized mean field game is equal to the unique minimizer of a suitable stochastic control problem stated in the simplex, called potential randomized game. Thus we study the limit as the intensity of the common noise vanishes. On the one hand, the optimal trajectories converge to the mean field game solutions which are minimizers of the corresponding potential game (not randomized), that is a deterministic optimal control problem in the simplex. On the other hand, the classical solution to the second order master equation converges to the gradient of the value function of the related potential game; we select a particular weak solution, in the sense of distributions, of the master equation, which can be written as multidimensional system of hyperbolic PDEs stated in the simplex. Lastly, we establish uniqueness of such solution in a suitable class. Joint work with Francois Delarue. Tutti gli interessati sono invitati a partecipare. Gli organizzatori Martino Bardi, Claudio Marchi, Markus Fischer

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4-year postdoctoral position in "Modelling Collective Behaviour"
by Tiziano De Angelis 25 Oct '19

25 Oct '19

Dear All, I was asked to circulate the announcement below for a 4-year postdoctoral position in "Modelling Collective Behaviour". It might be of interest to candidates with a PhD in Statistics or Applied Maths, should you know any who is looking for a postdoc. https://jobs.leeds.ac.uk/Vacancy.aspx?ref=EPSMA1003 Best wishes Tiziano

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Seminario di probabilita' di Daniel Valesin (Groningen) a Roma Tre
by Alexandre Stauffer 24 Oct '19

24 Oct '19

SEMINARIO Probabilita' Daniel Valesin (University of Groningen) Titolo: On the threshold of spread-out contact process percolation Lunedi' 04 Novembre 2019 ORE 16:30 Dipartimento di Matematica e Fisica Universita' degli Studi Roma Tre Largo San Leonardo Murialdo,1 - Pal.C - Aula 211 Abstract We study the stationary distribution of the (spread-out) d-dimensional contact process from the point of view of site percolation. In this process, vertices of ^d can be healthy (state 0) or infected (state 1). With rate one infected individuals recover, and with rate lambda they transmit the infection to some other vertex chosen uniformly within a ball of radius R. The classical phase transition result for this process states that there is a critical value lambda_c(R) such that the process has a non-trivial stationary distribution if and only if lambda > lambda_c(R). In configurations sampled from this stationary distribution, we study nearest-neighbor site percolation of the set of infected sites; the associated percolation threshold is denoted lambda_p(R). We prove that lambda_p(R) converges to 1/(1-p_c) as R tends to infinity, where p_c is the threshold for Bernoulli site percolation on ^d. As a consequence, we prove that lambda_p(R) > lambda_c(R) for large enough R, answering an open question of [Liggett, Steif, AIHP, 2006] in the spread-out case. Joint work with Bala'zs Ra'th. Alexandre

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