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Concorso RTD.B - UPO (scadenza 22/09/2019)
by enea giuseppe bongiorno 12 Sep '19

12 Sep '19

*Concorso RTDB 13/D1 - SECS-S/01 (Statistica)* *Scadenza: 22 settembre 2019* *Università del Piemonte Orientale* Cari colleghi, con la presente si ricorda che è in scadenza il bando per un posto di ricercatore a tempo determinato, lettera B, per il settore concorsuale 13/D1 (Statistica), settore scientifico disciplinare SECS-S/01 (Statistica) presso il Dipartimento di Studi per l'Economia e l'Impresa (DiSEI) dell'Università del Piemonte Orientale. Il bando è disponibile alla pagina: https://www.uniupo.it/alta-formazione-aziende-lavoro/concorsi/concorsi-il-p… Si prega di diffondere la comunicazione tra i potenziali interessati. Cordiali saluti, -- ------ Enea Bongiorno, PhD Università degli Studi del Piemonte Orientale Via Perrone 18, 28100, Novara, Italia Tel: +390321375317 <0321%20375317> e-mail: enea.bongiorno(a)uniupo.it

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seminario di Barbara Martinucci
by Enrica Pirozzi 09 Sep '19

09 Sep '19

AVVISO di SEMINARIO Alcuni risultati sul processo del telegrafo in presenza di barriere elastiche Dr. Barbara Martinucci Dipartimento di Matematica, Università degli Studi di Salerno (Abstract in allegato) Il seminario si terrà il giorno 10 Settembre 2019 ore 12:00 nell?Aula D al primo livello del Dipartimento Matematica e Applicazioni, Università di Napoli FEDERICO II, Complesso di Monte Sant'Angelo, Via Cintia, Napoli. -- Enrica Pirozzi Dipartimento di Matematica e Applicazioni Universita' di Napoli FEDERICO II Via Cintia, Monte S.Angelo, 80126, NAPOLI, ITALY Tel. 081 675634 https://www.docenti.unina.it/ENRICA.PIROZZI

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Seminar Guindani @Bocconi
by Giacomo Zanella 06 Sep '19

06 Sep '19

We are glad to announce the first DEC Statistics Seminar of the Fall 2019 series: Thursday, September 12 12:30 Meeting room 3-e4-sr03, Bocconi University, Via Roentgen 1, 3rd floor Michele Guindani (University of California, Irvine) Title: Bayesian Approaches to Dynamic Model Selection Abstract: In many applications, investigators monitor processes that vary in space and time, with the goal of identifying temporally persistent and spatially localized departures from a baseline or ``normal" behavior. In this talk, I will first discuss a principled Bayesian approach for estimating time-varying functional connectivity networks from brain fMRI data. Dynamic functional connectivity, i.e., the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature. Our method utilizes a hidden Markov model for classification of latent neurological states, achieving an estimation of the connectivity networks in an integrated framework that borrows strength over the entire time course of the experiment. Furthermore, we assume that the graph structures, which define the connectivity states at each time point, are related within a super-graph, to encourage the selection of the same edges among related graphs. Then, I will propose a Bayesian nonparametric model selection approach with an application to the monitoring of pneumonia and influenza (P&I) mortality, to detect influenza outbreaks in the continental United States. More specifically, we introduce a zero-inflated conditionally identically distributed species sampling prior which allows borrowing information across time and to assign data to clusters associated to either a null or an alternate process. Spatial dependences are accounted for by means of a Markov random field prior, which allows informing the selection based on inferences conducted at nearby locations. We show how the proposed modeling framework performs in an application to the P&I mortality data and in a simulation study and compare with common threshold methods for detecting outbreaks over time, with more recent Markov switching based models, and with other Bayesian nonparametric priors that do not take into account spatio-temporal dependence. Kind regards, Giacomo Zanella The DEC statistics seminars schedule is available at http://www.unibocconi.eu/statseminar Please note: if you are a guest and you do not have a Bocconi ID Card to access to the Bocconi Buildings, please communicate your participation by sending an email to arianna.colombo(a)unibocconi.it *************************************************** Giacomo Zanella Assistant Professor - Statistics DEC - Department of Decision Sciences Bocconi University

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Fwd: TOFFEe workshop @IMT, Lucca, 24-25 October
by Irene Crimaldi 05 Sep '19

05 Sep '19

Dear all, we kindly ask you to advertise the 1st TOFFEe workshop and contribute with your latest results on methodologies for fighting disinformation. TOFFEe - https://toffee.imtlucca.it/ - is an interdisciplinary research project funded by IMT School For Advanced Studies Lucca. The project ambition is to overcome some of the limitations of existing social platforms, which often dedicate little attention to trustworthy interactions among peers and to reliability of information. The aim of the project is to increase people's confidence on the data they get and who they follow, while minimising the risk of exposure to fake information and malicious actions. As part of the dissemination activities of the project, on * October 24th and 25th*, 2019, we organise an International Workshop where different approaches for fighting fakes will be exposed. You can find all infos at https://toffee.imtlucca.it/. We would be delighted if you could join us. The registration to the workshop is free, just fill the form at https://toffee.imtlucca.it/. We kindly ask you to register before the * 9th of October*. The registration comprehends two coffee breaks and one lunch. Analogously, we would be delighted if you could contribute to the workshop with your latest results. The slot for the contributions last nearly 15 minutes, plus some time for questions. You can send us a 2 pages abstract at the present email address. The deadline for sending the abstract is on the *25th of September*. We will return our decision on the *2nd of October*. Looking forward to meeting you in Lucca in October, we send you our best regards, Rocco De Nicola (project coordinator) Guido Caldarelli Irene Crimaldi Marinella Petrocchi Fabio Saracco

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Autumn School in Financial Mathematics in Verona
by Alessandro Gnoatto 04 Sep '19

04 Sep '19

It is a pleasure to announce the Autumn School in Financial Mathematics 2019 October 7-8, 2019 University of Verona Department of Economics A two-day autumn school in financial mathematics will be held in Verona on October 7-8 2019 The school is open to both professionals, students and academics and provides two intense courses: - A Pricing Framework for Valuation Adjustments - Algorithmic Differentiation for Monte-Carlo Simulations and Applications in Mathematical Finance (MVA, Portfolio Risk, Hedging). Interest Rate Models and the Hedging of Interest Rate Derivatives The lectures will be given by - Christian Fries (LMU München and DZ Bank) - Andrea Pallavicini (Banca IMI and Imperial College London) For further information and registration, please visit http://dse.univr.it/asfm/ (Apologies for cross posting) Best Regards, -- Prof Alessandro Gnoatto, PhD Dipartimento di Scienze Economiche Università degli Studi di Verona Via Cantarane 24 37129, Verona, Italy Room 1.05 Tel: +39 045 802 8537 Homepage: www.alessandrognoatto.com<http://www.alessandrognoatto.com> E-mail: alessandro.gnoatto(a)univr.it<mailto:alessandro.gnoatto@univr.it> -------------------------------------------------- View my research on my SSRN Author page: http://ssrn.com/author=1615989 --------------------------------------------------

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Fwd: Postdoc position in Cambridge
by Antonio DI CRESCENZO 04 Sep '19

04 Sep '19

---------- Messaggio inoltrato --------- Da: Sebastian Andres <sa836(a)cam.ac.uk> Data: mer 4 set 2019 alle 10:21 Dear all, We invite applications for a postdoc position in probability and statistical mechanics at the University of Cambridge, starting in Spring 2020. For further details please see http://www.jobs.cam.ac.uk/job/22585/ We would be grateful if you would forward the link to potential candidates. Many thanks in advance and best wishes, Sebastian Andres -- ============================================= Antonio Di Crescenzo Dipartimento di Matematica Università degli Studi di Salerno Via Giovanni Paolo II, n. 132 <https://maps.google.com/?q=Via+Giovanni+Paolo+II,+n.+132+%0D%0A84084+Fiscia…> 84084 Fisciano (SA) Italy Tel. +39-089-963349 Fax: +39-089-963303 E-mail(1): adicrescenzo(a)unisa.it E-mail(2): adicresc(a)gmail.com Web: http://www.unisa.it/docenti/antoniodicrescenzo/index =============================================

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Fwd: Ph.D. position in “Big data techniques for maintenance decision making under uncertainty” at TU/e
by Antonio DI CRESCENZO 03 Sep '19

03 Sep '19

---------- Messaggio inoltrato --------- Da: Kapodistria, S. <S.Kapodistria(a)tue.nl> Data: mar 3 set 2019 alle 13:36 Oggetto: Ph.D. position in “Big data techniques for maintenance decision making under uncertainty” at TU/e A: CC: Kapodistria, S. <S.Kapodistria(a)tue.nl> Dear colleagues, can you please distribute to your network the following PhD position advertisement? Thank you very much in advance. Best regards, Stella ———————————————————————————— Stella Kapodistria Department of Mathematics and Computer Science, Eindhoven University of Technology Postal address: P.O. Box 513, 5600 MB, Eindhoven, The Netherlands A PhD position in “Big data techniques for maintenance decision making under uncertainty” is available at Eindhoven University of Technology. For more information, see below. In the framework of the national NWA-ORC project “PrimaVera: Predictive maintenance for Very effective asset management <https://www.tue.nl/en/news/news-overview/primavera-predictive-maintenance-f…>”, there is a PhD position available in the Stochastics Section within the Department of Mathematics and Computer Science in Eindhoven University of Technology, in The Netherlands. The PrimaVera consortium, led by Twente University, consists of several Dutch higher education institutes and companies. Just-in-time maintenance requires a very accurate, enhanced physically based data-driven assessment of the system’s health and its future evolution guaranteeing the reliability, availability and affordability of the system through accurate monitoring, prognostics and diagnostics. This is organized around the following key game changers: • *Development of a big data health index*. Intelligent systems are equipped with sensors collecting data real-time. Analyzing the collected data can guarantee the reliability, availability and affordability of the system through accurate monitoring, prognostics and diagnostics. However, when dealing with especially large and complex systems, a key challenge for engineers is how to assess the present condition. By measuring the dynamics of the asset/network, modelling the dynamic behavior and by combining the real-time information, we aim to assess the present condition in the form of a health index and predict the remaining useful life (based on an assumed usage profile) using big data analytic techniques stemming from machine learning and artificial intelligence. • *Causal discovery of failures and root cause analysis.* A key step in failure prevention is to understand why systems fail. Data-driven techniques are good at finding correlations between failure modes, but finding causal relations is challenging. Combinations with physical models and domain knowledge will create crucial actionable insight and condition assessment. These associations will be incorporated in a dynamic estimation and inference paradigm framework for extracting causal graph dynamics, which can be efficiently queried to find the root causes of specific failures. • *Model-enhanced big data techniques for decision making under uncertainty*. To cope with the complex reality, we need hybrid approaches leveraging the advantages of statistical methods (guaranteed performance and uncertainty bounds), data mining approaches (superior in situations where rules are not yet known), and physics of failure models (causality). We propose to combine big data analytic techniques stemming from machine learning and artificial intelligence with the more classical decision-making approaches (stochastic dynamic programming, Markov decision models). This combination goes in two directions: (i) on the one hand, we propose to use big data analytics to automatically learn the health index model and other relevant failure models with the objective of optimally fitting the decision objective instead of the classical data fitting objective. (ii) On the other hand, we propose to use decision-making approaches to improve the data analytic techniques, e.g., by incorporating an exploration vs exploitation approach in the maintenance decision making to cope with the uncertainty in the information. *Job description* The project is methodologically oriented. It is on the interface of statistical operations research and machine learning, and it is strongly inspired by industrial challenges. The PhD project will be conducted in an international team and in close collaboration with academic and industrial partners of PrimaVera. The main goal of the PhD project is to develop mathematical tools/models/algorithms for (some of) the above-mentioned key game changers, and thus get a deeper insight. The research will be concluded with a PhD thesis. A small teaching load is part of the job. *Job requirements *Preference will be given to candidates in any branch of applied mathematics and to candidates from a different background (e.g. engineering) but with strong mathematical foundation. Research experience will be highly valued. Strong knowledge of applied probability, statistics and operations research is highly desirable. The successful applicant will hold a Master’s degree in Mathematics, Applied Mathematics, Computer Science, Engineering Sciences or related fields. All applications should include a cover letter, curriculum vitae, and transcripts. Proficiency in English is also required. *Team* The PhD project will be carried out at the stochastics section within the department of Mathematics and Computer Science (M&CS), TU/e. M&CS has a vibrant international environment, with almost 50% of the scientific staff being non-Dutch nationals and more than 100 PhD candidates. It has extensive experience in helping new (foreign) employees settle in. The research team consists of the candidate, Dr. Alessandro Di Bucchianico ( a.d.bucchianico(a)tue.nl) and Dr. Stella Kapodistria (s.kapodistria(a)tue.nl). Collaboration with the academic and industrial members of the PrimaVera consortium is highly supported. In particular, within TU/e, there will be a close cooperation with the Smart Manufacturing and Maintenance research program of the Data Science Center Eindhoven, the Eindhoven AI Systems Institute (EAISI) and with PrimaVera researchers from the Department of Industrial Engineering and Innovation Sciences. *Terms of employment* - PhD candidates are appointed as temporary university employees for a four-year period (after nine months you will have an evaluation as to whether the research is expected to result in a PhD degree after four years). - The terms of employment are governed by the Collective Labour Agreement of Universities in The Netherlands, with a monthly salary starting at 2325 Euro in the first year, and increasing to 2972 Euro in the fourth year, and an additional 8% holiday allowance and 8% end-of-year bonus. An extensive fringe benefits package is included. Further details can be found *here* <http://www.tue.nl/en/working-at-tue/why-tue/compensation-and-benefits/>. Additional budget allows for extensive research visits abroad and conference attendance. - For information regarding the university, please visit the website of *TU/e* <http://www.tue.nl/>. *Here* <https://www.tue.nl/en/our-university/student-sports-centre-eindhoven/> you can find information about the sports facilities on campus. Information about Eindhoven can be found *here* <https://brainporteindhoven.com/>. The HR International Backoffice provides support with immigration procedures. - Information about terms of employment can be obtained from the HR services office, (pzwin[at]tue.nl). *Application procedure *In order to apply, please submit a · Cover letter (2 page max), which includes a motivation of your interest in the vacancy and an explanation of why you would fit well for the project. · Detailed curriculum vitae. · List of courses you have taken in Master’s and Bachelor’s programs (including grades). · Results of a recent English language test, or other evidence of your English language capabilities (TOEFL, IELTS, etc.). · Name and contact information of references. Upon selection, letters of recommendation, evaluating the candidate's research experience, will be required from two or more references. Please include the contact information of at least two references in your submitted material. *Information* For further information, please contact or consult, · about the research position: Dr. Alessandro Di Bucchianico, e-mail: a.d.bucchianico[at]tue.nl, phone: +31 40 247 2902 or Dr. S. Kapodistria, e-mail: s.kapodistria[at]tue.nl, phone: +31 40 247 5825 · about the employment conditions: HR advisor, e-mail: pzwin[at]tue.nl, phone +31 40 247 2321 · about the project: *click here* <https://www.tue.nl/en/news/news-overview/primavera-predictive-maintenance-f…> · about the department: *click here* <http://www.tue.nl/en/university/departments/mathematics-and-computer-scienc…> · about the stochastics section: *click here* <https://www.tue.nl/en/university/departments/mathematics-and-computer-scien…> *Application deadline* The deadline for applications is *October 19, 2019*. However, if you are interested, we invite you to apply at *click here <https://jobs.tue.nl/en/vacancy/phd-position-interface-of-statistical-operat…>* as soon as possible. Selection will begin immediately and continue until the position has been filled. -- ============================================= Antonio Di Crescenzo Dipartimento di Matematica Università degli Studi di Salerno Via Giovanni Paolo II, n. 132 <https://maps.google.com/?q=Via+Giovanni+Paolo+II,+n.+132+%0D%0A84084+Fiscia…> 84084 Fisciano (SA) Italy Tel. +39-089-963349 Fax: +39-089-963303 E-mail(1): adicrescenzo(a)unisa.it E-mail(2): adicresc(a)gmail.com Web: http://www.unisa.it/docenti/antoniodicrescenzo/index =============================================

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postdocs
by Marco Scarsini 03 Sep '19

03 Sep '19

The Department of Economics and Finance at LUISS has some openings for postdocs https://economiaefinanza.luiss.it/en/research/research-grants-0 two of them on the following research projects: Stochastic methods in algorithmic game theory, Games on networks and Markov chains. ******************************************************* Marco Scarsini Dipartimento di Economia e Finanza Luiss Viale Romania 32 00197 Roma, ITALY URL: http://docenti.luiss.it/scarsini/

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A``LATE-SUMMER'' DAY IN PROBABILITY AND STATISTICAL PHYSICS: 27 September 2019 (Picco e Pires da Silva Castro)
by Francesca Romana Nardi 29 Aug '19

29 Aug '19

Carissimi, Siamo felici di annunciarvi la ottava giornata di seminari: A ``LATE-SUMMER'' DAY IN PROBABILITY AND STATISTICAL PHYSICS University of Florence Friday 27 September 2019 Lecturers: Pierre Picco (Marseille) and Rui Pires da Silva Castro (Eindhoven) Location: Aula Tricerri Viale Morgagni 67, Firenze Informazioni su come arrivare alla pagina: https://www.dimai.unifi.it/vp-285-come-arrivare-how-to-get.html Note pratiche: Stiamo prenotando un catering con cibi vegerariani e non, percio` abbiamo bisogno del numero di persone che vogliono mangiare insieme. A coloro che fossero interessati (per una migliore organizzazione) chiedo di mandare un email a francescaromana.nardi(a)unifi.it <mailto:francescaromana.nardi@unifi.it> ; gianmarco.bet(a)unifi.it <mailto:gianmarco.bet@unifi.it> ; angela.caporicci(a)unifi.it <mailto:angela.caporicci@unifi.it> , con l'intenzione di partecipare al seminario, al coffe break e al pranzo. PROGRAMMA Prof. Pierre Picco (CNRS Marseille) Title: One-dimentional Ising model with long range interactions. A review of results Abstract: Introductory lecture In the first talk I will make an quick historical survey of the rigorous results on the one-dimensional Ising model with long-range interactions. A first part will be dedicated to uniqueness of the Gibbs states (Ruelle (1968); Dobrushin (1968); Bricmont, Lebowitz & Pfister (1986)) and the regularity of the free energy when the decay of the potential is fast enough (Dobrushin (1973) Cassandro & Olivieri (1981) and its extensions in particular Capocaccia, Campanino & Olivieri (1983). A second part will be dedicated to the existence of phase transition starting from the Kac-Thompson conjecture (1968) the Dyson results (1969), the Frohlich \& Spencer result (1982), the Imbrie result (1982) the Aizenmann, Chayes, Chayes & Newman result on the Thouless effect (1988), Imbrie & Newman result on the Berezinsky, Kosterlitz & Thouless transition (1988). A third part will be dedicated to present results in the phase transition regime that started with Frohlich & Spencer (1981), Cassandro, Ferrari, Merola & Presutti (2001) and its extensions in particular by Cassandro, Merola, Picco & Rosikov (2014) on the definition of an interface and its fluctuations, and on a Minlos & Sinai theorem on the phase separation problem by Cassandro, Merola & Picco (2017). Seminar In the second talk I will review heuristic arguments that were invoked to conjecture the existence of a phase transition at low temperature in particular the Landau argument. I will present toy models where the fluctuation of interfaces and localisation of the droplet in the Minlos & Sinai theory will be explained. I will give an algorithmic definition of one-dimensional contours of Cassandro, Ferrari, Merola & Presutti. Prof. Rui Pires da Silva Castro (Eindhoven University of Technology) Title: Testing for the presence of communities in inhomogeneous random graphs Abstract: Many complex systems can be viewed as a network/graph consisting of vertices (e.g., individuals) connected by edges (e.g., a friendship relation). Often one believes there is some sort of community structure, where some vertices are naturally grouped together (e.g., more densely connected between themselves than to the rest of the network). Much of the community detection literature is concentrated around methods that extract communities from a given network. Our goal in this work is different, and we attempt to understand how difficult is it to determine if a network has real communities. Furthermore, we are primarily interested in the case of small or very small communities, for which many existing results and methods are not applicable. We cast this problem as a binary hypothesis test, where the null model corresponds to a graph without community structure, and the alternative model almost the same, but it also includes a planted community - that is, a small subset of the vertices has higher connection probability than under the null. The main question is to determine the minimal size and “strength” of the planted community that will allow detection. The seminal work of Arias-Castro and Verzelen tackled this problem when the null model is a homogeneous random graph. In our work, however, we consider the case where the null model is inhomogeneous, as this is somewhat closer to realistic scenarios. In particular, we present a scan test and provide conditions under which it is able to detect the presence of a small community. These results are valid for a wide variety of parameter choices. Furthermore, we show that for some parameters choices the scan test is optimal, and no other test can perform better (e.g, detect smaller or weaker planted communities). Finally, we extend this scan test to adapt to many parameters of the model when the null is a rank-1 generalized random graph. In the first part of the talk I will describe the above formulation and ensuing results, with illustrative examples and briefly touching upon the analytical methodology. In addition, I will discuss the related problem of characterizing cliques in rank-1 random graphs, which provides some insights on the role of inhomogeneity. The second part of the talk will go deeper into more technical aspects and ensuing insights. This presentation is based on joint work with Kay Bogerd and Remco van der Hofstad (https://arxiv.org/abs/1805.01688 and ongoing work). Program: 11.00-11.45 Introductory lecture: Picco 11.45-12.00 Break 12.00-12.45 Seminar: Picco 13.00-14.30 Lunch 14.30-15.15 Introductory lecture: Pires da Silva Castro 15.15-15.30 Break 15.30-16.30 Seminar: Pires da Silva Castro Organizers: G. Bet, F. Caravenna, N. Cancrini, E.N.M. Cirillo, P. Dai Pra, A. De Masi, D. Fanelli, F. Flandoli, C. Giardina`, R. Livi, F. Martinelli, I.G. Minelli, F.R. Nardi, E. Presutti, B. Scoppola, E. Scoppola. Ricordo che ciascuno oratore fara` una lezione introduttiva e divulgativa di 45 minuti pensata proprio per i non esperti, seguita da altri 45 minuti di tipo seminario (vedi programma). Maggiori informazioni e aggiornamenti sono reperibili alla pagina web http://web.math.unifi.it/users/fnardi/seminari/ Vi aspettiamo numerosi Francesca R. Nardi e Gianmarco Bet Dipartimento di Matematica e Informatica Università degli Studi di Firenze Viale Morgagni 67, Firenze, Italy

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FCPNLO 7
by Enrico Scalas 29 Aug '19

29 Aug '19

Car* Collegh*, vi segnalo che la settima edizione di Fractional Calculus Probability and Non-Local Operators si terra' presso l'Universita' del Sussex dal 26 al 28 settembre 2019. Piu' informazioni sono disponibili a: http://www.sussex.ac.uk/maths/research/fcpnlo-7 La registrazione e' aperta ed e' obbligatoria ma gratuita. Cordiali saluti Enrico Scalas

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