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Annuncio di seminario
by Piccioni, Mauro 09 Nov '16

09 Nov '16

Dipartimento di Matematica, Sapienza Università di Roma Martedì 15 novembre Ore 14:00, Aula di Consiglio Seminario di Probabilità e Statistica Matematica Random dynamical systems: contraction and recurrence propertie Marc Peigné, Université de Tours Abstract: Consider a proper metric space X and a sequence (Fn) of i.i.d. random continuous mappings X → X. It induces the stochastic dynamical system (SDS) Xn = Fn ◦ · · · ◦ F1(x) starting at x ∈ X. We study existence and uniqueness of invariant measures, under some assumptions of contraction on the Fn, as well as recurrence and ergodicity of this process. We will consider two main examples: the case where the Fn are affine maps of the real line and the case where Xn is the reflected random walk on the positive real line. Tutti gli interessati sono invitati a partecipare. Per qualsiasi richiesta di informazione scrivere a piccioni(a)mat.uniroma1.it

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CEMRACS 2017 - Announcement (fwd)
by Tiziano Vargiolu 07 Nov '16

07 Nov '16

********************************************** ** ** CEMRACS 2017 SUMMER SCHOOL ** ** July 17 - August 25 ** MARSEILLE FRANCE ** ********************************************** The 2017 session of the CEMRACS summer school will be held from July 17 to August 25, at CIRM, Luminy (Marseille). This annual summer school on numerical methods has been existing for 20 years. The 2017 session will be dedicated to numerical methods for stochastic problems, including: * control problems, * uncertainty quantification, * mean field models, see the web page of the school for more information: http://smai.emath.fr/cemracs/cemracs17/ The summer school is organized as follows: * The first ten days are dedicated to lectures on numerical methods, including HPC training on computers. * During the next five weeks, Phd or Postdoc students work in groups of 2 or 3 on a numerical project that has been proposed by an industrial or a research department. Specialists of numerical methods and IT will support them. We are currently making three calls: *Call 1. Call 1 is for attending the lectures of the summer school (July 17-26), see the program at http://smai.emath.fr/cemracs/cemracs17/summerschool.html. *Call 2. Call 2 regards the submission of numerical projects: research departments and industrials can submit their own numerical projects. It is a way for them to accelerate the study of a numerical problem or of a numerical method. A project may include students who will work on it, or it may not include students. In the latter case, we will assign students to the project, see Call 3 below. More information is available at http://smai.emath.fr/cemracs/cemracs17/projects.html. Examples from the 2016 edition can be found at http://smai.emath.fr/cemracs/cemracs16/research/conf.php. *Call 3. Call 3 concerns students without a project who would like to work on a project, as defined in Call 2. So, this call is dedicated to Phd students and postdoc students that are not already part of a submitted project. They will be assigned to one of the projects without students (or with not enough students). Projects may end up with a publication, on the underlying numerical method or on some corresponding application. Proceedings will be edited after the event. The registration/accommodation fees are detailed on http://smai.emath.fr/cemracs/cemracs17/. For hosting a project, a participation to global expenses of CEMRACS is asked. For further details, please contact us. Emails should be sent to one of the organizers (see the list below) or at the email address cemracs17(a)smai.emath.fr Could you please circulate the information to colleagues and to industrial contacts that might be interested ? Best regards, the organizing committee: Bruno Bouchard (bouchard(a)ceremade.dauphine.fr) Jean-Fran?ois Chassagneux (chassagneux(a)univ-paris-diderot.fr) Fran?ois Delarue (delarue(a)unice.fr) Emmanuel Gobet (emmanuel.gobet(a)polytechnique.edu) Jer?me Lelong (jerome.lelong(a)imag.fr) - - - - - - - - - - - - - - - - - - - - - - - - - - Fran?ois Delarue Professeur Universit? Nice Sophia-Antipolis Laboratoire Dieudonn? Parc Valrose France-06108 Nice Cedex 02 (33) (0)4 92 07 62 80 http://math.unice.fr/~delarue

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Annuncio Sisbayes 2017 Workshop, Roma 7-8 febbraio 2017
by Brunero Liseo 07 Nov '16

07 Nov '16

SISbayes 2017 Workshop, Roma 7-8 February 2017. SISbayes 2017 is the first Workshop organized by the section on Bayesian Statistics of the Italian Statistical Society. SISbayes main goals are - To promote and coordinate applied and theoretical research in the statistical field - To encourage and organize specialized sessions and ad hoc tutorials at national and international statistical meetings - To disseminate Bayesian ideas and methods among young students and scholars in both Statistics and other fields. - To foster the application of the Bayesian paradigm in science, industry, business, finance, and official statistics. The workshop will consists of * Two tutorials delivered by prof. Alessandra Guglielmi - Politecnico di Milano: Bayesian Nonparametrics. prof. Michela Cameletti - Università di Bergamo: An Introduction to Bayesian Computing with INLA. * Five invited sessions * A Poster presentation session followed by a Poster exhibition. The website http://stat-events.wixsite.com/sisbayes2017 contains all the necessary information and it will be periodically updated. SISbayes 2017 is hosted and financially supported by Sapienza Università di Roma. Participants can register on the website. The registration fee (100 Euros; 60 Euros for students) includes admission to tutorials and conference sessions, congress materials, coffee breaks, poster session aperitif (Tuesday 7th) and lunch (Wednesday 8th) Brunero Liseo (on behalf of the Scientific Committee and the Organizing Committee) -- Organizing Committee S. Arima (Sapienza) M. Barbieri (Roma Tre) P. Brutti (Sapienza) F. De Santis (Sapienza) C. Grazian (Sapienza) S. Gubbiotti (Sapienza) B. Liseo (Sapienza) M. Perone Pacifico (Sapienza) S. Polettini (Sapienza) V. Sambucini (Sapienza) A. Tancredi(Sapienza) L. Tardella (Sapienza) Scientific Committee F. Corradi (Firenze) B. Liseo (Sapienza) A. Mattei (Firenze) A. Mira (Lugano) A. Ongaro (Milano Bicocca) S. Petrone (Bocconi) L. Ventura (Padova) ============================================ Brunero Liseo *Dip. di metodi e modelli per il territorio, l'economia e la finanza * *Sapienza Università di Roma* *Viale Castro Laurenziano, 9 Roma I-00161 *Tel. +39 06 49766973 Fax +39 06 4957606 *http://www.memotef.uniroma1.it/users/liseo-brunero <http://www.memotef.uniroma1.it/users/liseo-brunero>* =============================================

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Giornata di studi in onore di Gabriella Salinetti
by Luca Tardella 07 Nov '16

07 Nov '16

Il giorno 6 febbraio 2017 si terra' presso il Dipartimento di Scienze Statistiche di Sapienza Università di Roma la giornata di studi: "Advances in Probability, Statistics and Decision Theory" *** A Conference in Honor of Gabriella Salinetti*** E' disponibile il seguente sito web dedicato alla giornata con l'elenco dei relatori invitati e un programma preliminare http://stat-events.wixsite.com/gabriellaconference Nelle prossime settimane sarà aggiornato con ulteriori dettagli. L'iscrizione al convegno è gratuita, ma necessaria per ragioni organizzative. E' già possibile registrarsi sul sito compilando un semplice modulo on line. E' inoltre possibile contattare gli organizzatori al seguente indirizzo: gabriellaconference(a)gmail.com. Cordiali saluti

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Second Announcement : Third STAR workshop on Random Graphs
by Ben Hansen 04 Nov '16

04 Nov '16

Second Announcement : Third STAR workshop on Random Graphs The workshop will take place on Thursday 26 and Friday 27 January 2017 on the campus of the University of Utrecht (more details of the location and schedule will be added later). This will be the 3rd STAR Workshop on Random Graphs. The first took place in 2012 and the second in 2015. We are very pleased to announce the following prominent researchers have agreed to deliver plenary talks: - Marián Boguñá (Barcelona) - Mihyun Kang (Graz) - Vincent Tassion (Geneva/Zürich) - Nick Wormald (Melbourne) A limited number of slots for contributed talks are available. If you would like to propose a short talk, please let us know by e-mailing Ben Hansen at benhansen09(a)gmail.com. (In case of too much interest, selection will be based on relevance of the talk and the career stage of the speaker.) The workshop is open to all and free of charge, but please register to let us know that you are coming by sending an e-mail to: benhansen09(a)gmail.com The website of the workshop can be found at the address : http://www.math.uu.nl/stochsem/WorkshopRGs2017/

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Postdocs in Riemannian Manifold Optimization, Deep Learning and Information Geometry at RIST
by Luigi Malagò 03 Nov '16

03 Nov '16

[apologies for cross-posting] The Romanian Institute of Science and Technology (RIST) has an opening for 3 postdoc positions, in the context of the DeepRiemann project “Riemannian Optimization Methods for Deep Learning”, funded by European structural funds through the Competitiveness Operational Program (POC 2014-2020). The appointments will be for 2 years, with possible extensions up to 4 years. The DeepRiemann project aims at the design and analysis of novel training algorithms for Neural Networks in Deep Learning, by applying notions of Riemannian optimization and differential geometry. The task of the training a Neural Network is studied by employing tools from Optimization over Manifolds and Information Geometry, by casting the learning process to an optimization problem defined over a statistical manifold, i.e., a set of probability distributions. The project is highly interdisciplinary, with competences spanning from Machine Learning to Optimization, Deep Learning, Statistics, and Differential Geometry. The objectives of the project are multiple and include both theoretical and applied research, together with industrial activities oriented to transfer knowledge, from the institute to a startup or spin-off of the research group. The positions will be part of the new Machine Learning and Optimization group, which will be performing research at the intersection of Machine Learning, Stochastic Optimization, Deep Learning, and Optimization over Manifolds, including nature-inspired optimization algorithms, from the unifying perspective of Information Geometry. The group is one of two newly-formed groups in Machine Learning at RIST, where about 20 new postdoctoral research associates and research software developers will be hired in the next year. The official job announcement can be seen here: http://rist.ro/en/details/news/postdoc-positions-in-machine- learning-optimization-deep-learning-and-information-geometry.html # Jobs Description # The three open positions will focus on different and overlapping aspects of the project: 1) Optimization Algorithms over Statistical Manifolds with Applications to Deep Learning The postdoctoral researcher will conduct research on the design and implementation of novel first- and second-order methods for the optimization of functions defined over statistical models, such as exponential families, the Gaussian distribution, and Markov Random Fields. In particular the research will focus on the optimization problem associated to the training of a Neural Network, which will be studied from the perspective of Optimization over Manifolds with non-Euclidean geometries. The postdoctoral candidate will develop optimization algorithms which explicitly take into account the Riemannian and dual-affine Hessian geometries of the search space, given by the Fisher information metric. Such algorithms should be efficient, scalable, largely parallelizable, and suitable for the large scale and big data settings. Keywords: stochastic gradient descent, Riemannian manifolds, dually-flat Hessian geometries, optimization over manifolds, second-order optimization methods, large scale optimization, nature-inspired optimization algorithms 2) Theory of Neural Networks The postdoctoral researcher will contribute towards the definition of a probabilistic and geometric framework for the study of deep Neural Networks aimed at a better understanding of the working mechanisms behind the success of Deep Learning. The analysis is focused towards a better understanding of what enables Deep Learning systems to achieve remarkable successes, and of its limits, in classification and predictive analysis, with a particular focus on the learning task, in order to guide the development of novel algorithms. The research will include the study of the representational power of various Neural Networks topologies, by taking into consideration the impact of hierarchical architectures with respect to the expressiveness of the network; and the analysis of the number of local minima and of saddle points, which appear during the training and which strongly affect convergence of the optimization algorithms, depending on the nature of the network. Keywords: theory of neural networks, deep learning, learning representations, expressive power of neural networks. 3) Information Geometry of dually-flat Hessian Manifolds The postdoctoral researcher will focus on the characterization of first and second-order Riemannian and affine geometries of statistical models, or more in general Hessian manifolds, aimed at the study of optimization methods over statistical manifolds. Neural Networks can be modeled as statistical manifolds, or manifolds of Neural Networks, and the training of the network can be studied as an optimization problem over a non-linear manifold, where the weights of the network are the parameters of the model and thus the variables of the function to be optimized. The purpose of the analysis is to introduce alternative second-order approximations of a function over a statistical manifold, depending on the choice of the connection and thus how parallel transport is implemented, which are at the basis of the design of second-order optimization methods. Keywords: information geometry, geometry of statistical models, natural gradient, dually-flat manifolds, Hessian geometries. # Desired Qualifications # - PhD in machine learning, theoretical computer science, stochastic optimization, statistics, applied mathematics, including fields such as manifold optimization, differential geometry, statistical mechanics, and related fields - Strong publication record - Strong analytical skills, such as problem solving and logical thinking - Enthusiasm to work in a multidisciplinary and international research environment - Good written and oral communication skills in English Knowledge in machine learning is helpful, but not strictly mandatory. Doctoral students close to the competition of their thesis will also be considered. The positions are to start as early as December 2016 or at any agreed later date. Applications will be reviewed as they are received. RIST offers competitive salaries and top-level working conditions. The net salary for these positions will be around 2.000 euro per month, with a possible increase up to 2.200 euro, based on new tax incentives for research and development activities in Romania. Positions are endowed with travel resources. The cost of living in Cluj is significantly lower than in Western Europe or the USA (e.g., it is 1/3 of the cost of living in London, UK). # How to Apply # In order to apply to this position, the candidate should send an email to deepriemann.jobs(a)rist.ro, mentioning in the subject of the email "DeepRiemann Postdoc Application". The email should include among the attachments: - A cover letter - A complete CV with full list of publications - A short research statement (max 3 pages), which describes your research interests and explain why your skills, knowledge and experience makes you a suitable candidate for one or more of the open positions - The pdfs of 2 selected publications - Name and contact information of up to 3 referees, which will be contacted directly by the institute for a reference letter Informal inquiries can be sent to Dr. Luigi Malagò <malago(a)rist.ro>, Principal Investigator of the DeepRiemann project. # About the Institute # The Romanian Institute of Science and Technology is a non-governmental, not-for-profit, independent research institute, founded in 2009, with the purpose of offering scientists a place to conduct research in Romania with top working conditions, comparable to those you can find in Western Europe. RIST currently performs research on computational and experimental neuroscience, computational intelligence, machine learning, and dynamical systems. The institute is currently undergoing a phase of significant and sustained growth supported by European and Romanian structural funds, with plans for hiring around 20 new scientists and researchers in the next year. The institute has close contacts with the Babeș-Bolyai University, which is the largest university in Romania, and with the Technical University of Cluj-Napoca. The institute is located in Cluj-Napoca, in the heart of Transylvania, which has been named by Lonely Planet as the top region to visit in 2016. Cluj is a welcoming and innovative city, recently listed as one of the major tech hubs (with a 9% growth per year for IT industry). Cluj has 12 universities, over 70k students every year, and an extremely vibrant startup scene, which has recently named the Silicon Valley of Transylvania. Cluj is Europe's friendliest city for foreigners, according to a study by the UK Office of National Statistics, and Romania is no. 16 in the Internations best expat destinations of 2016. About 1100 French citizens, 800 Italian citizens and 500 German citizens live in Cluj (source). The city has an international airport, only 8km away from the city center, with flights to more than 30 European destinations. More information about Cluj: https://vimeo.com/171864331 https://vimeo.com/166021527 -- Luigi Malagò Principal Investigator Romanian Institute of Science and Technology - RIST Address: Str. Cireşilor 29, 400487 Cluj-Napoca, Romania Homepage: http://www.luigimalago.it Office: +40 364 408794 Mobile: +39 340 6249299

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Annuncio di seminario
by Piccioni, Mauro 03 Nov '16

03 Nov '16

Dipartimento di Matematica, Sapienza Università di Roma Martedì 8 novembre Ore 14:00, Aula di Consiglio Seminario di Probabilità e Statistica Matematica Pham Thi Da Cam, Université de Tours, France The survival probability of a critical multi-type branching process in i.i.d. random environment Abstract: We consider Galton Watson branching processes of unique type and multi-type in fixed and in random environment. The main target is to observe the asymptotic behaviour of the survival probability of the population. In particular, we utilise the generating function method thanks to the recursive structure of the process in deterministic case and condition to the generating function of offspring distribution in random case. Tutti gli interessati sono invitati a partecipare. Per qualsiasi richiesta di informazione scrivere a piccioni(a)mat.uniroma1.it

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Seminario di Cambyse Rouzé a Pavia
by Carbone Raffaella 02 Nov '16

02 Nov '16

Cari colleghi, vi annuncio che martedì 8 novembre, alle ore 15 precise, nell'aula Beltrami del Dipartimento di Matematica dell'Universita' di Pavia, il Dott. Cambyse Rouzé (University of Cambridge) terrà un seminario dal titolo Convergence properties of a quantum diff usion semigroup via concavity of the quantum entropy power di cui segue il sunto. Distinti saluti, Raffaella Carbone Abstract The entropy power, rst de ned by Shannon in his seminal paper, lead to many celebrated results in information theory and probability theory. In 1985, Costa proved that the entropy power of a random variable with added Gaussian noise of linearly increasing variance is concave with respect to . The proof of this result was later simpli ed by Dembo by an argument based on the so-called Blachman-Stam inequality. This result (and the intermediate steps of its proof) led to many applications ranging from contractivity properties of certain Markov semigroups to entropic versions of the Central limit theorem. In this talk I will discuss a recent analogue of this result proved in the context of quantum harmonic analysis on phase space. I will then use this result to bound the time of convergence in relative entropy of a state evolving under a quantum analogue of the heat semigroup. This is joint work with Dr. Nilanjana Datta and Dr. Yan Pautrat. -- Raffaella Carbone, PhD Ricercatore di Probabilità e Statistica Matematica Dipartimento di Matematica dell'Università degli Studi di Pavia

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"5th Conference in Memory of Carlo Giannini" (PROGRAMME) (fwd)
by Tiziano Vargiolu 29 Oct '16

29 Oct '16

From: Urga, Giovanni Sent: 29 October 2016 08:59 To: Subject: "5th Conference in Memory of Carlo Giannini" (PROGRAMME) Dear Colleagues Please find attached the programme of the forthcoming conference in Memory of Carlo Giannini to be held next 25 & 26 November at the University of Bergamo. Please feel free to circulate the flyer. Finally, please do not forget to register using the following link http://www00.unibg.it/formconvegni2/maschera.asp?convegno=3576. Looking forward to seeing you in Bergamo in a few week time. Regards Giovanni **************************************************************** Please use both addresses: giovanni.urga(a)unibg.it and g.urga(a)city.ac.uk **************************************************************** Giovanni Urga Professor of Finance and Econometrics Faculty of Finance Cass Business School 106 Bunhill Row London EC1Y 8TZ (U.K.) Tel.+/44/(0)20/70408698 Fax.+/44/(0)20/70408881 www.cass.city.ac.uk/experts/G.Urga https://sites.google.com/site/urgagiovanni/ Director, Centre for Econometric Analysis (CEA@Cass) http://www.cass.city.ac.uk/cea/index.html Professore di Econometria, Department of Management, Economics and Quantitative Methods, Bergamo University (Italy) http://www00.unibg.it/struttura/strutturasmst.asp?rubrica=1&persona=689&nom e=Giovanni&cognome=Urga&titolo=Prof.

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ARPM job opportunity
by Elisa Appolloni 28 Oct '16

28 Oct '16

Dear all, I am reaching out to you because at ARPM (the company for which I work) we are looking to add new resources for our Advanced Risk and Portfolio Management program. Would you mind circulating the message below to interested and qualified candidates? Thank you, Elisa Appolloni *** *The firm* ARPM – Advanced Risk and Portfolio Management <https://www.arpm.co/> is a privately held research institution, directed by Attilio Meucci, based in New York City with virtual offices world-wide. ARPM’s mission is to set and disseminate the standards for advanced quantitative risk management and portfolio management across the financial industry: asset management, banking, and insurance. *The opportunity * ARPM is looking for a new researcher-in-training for a minimum period of 6 months, indefinitely extensible. The successful candidate will review and code practical case studies and theoretical examples in quantitative finance, contributing to the ARPM Lab <https://www.arpm.co/lab/books.php?action=page&id=14>. The successful candidate will work full-time, remotely, constantly communicating via multi-media with the other members of ARPM. The ARPM researcher-in-training position represents a great opportunity for candidates with strong academic background, who wish to apply to real problems in finance the rigorous, research-oriented approach acquired in their schooling. *The progression* ARPM emphasizes the constant intellectual growth of its resources. For the first 6 months the researcher-in- training will be focused on specific projects. At the end of this period (s)he will conduct a presentation on the topics covered. Then, (s)he will start broadening his/her scope, attending the presentations of their peers and seniors, working on broader projects, and acquiring hands-on- knowledge of all the topics of the ARPM Lab <https://www.arpm.co/lab/books.php?action=page&id=14>. The approximate time required to attain the required level of familiarity with the ARPM Lab <https://www.arpm.co/lab/books.php?action=page&id=14> is: two years for a recent master’s graduate; one year for a recent PhD graduate. When ready, the researcher-in-training will be tested on all such topics with an exam. If successful, (s)he will conclude his/her training period, attaining the title of ARPM researcher. The ARPM researcher will then engage in highly quantitative projects with ARPM clients, becoming a profit center. *The candidate* [image: arrow_20]Master’s degree in mathematics, physics, engineering, computer science, statistics, data science, quantitative economics. [image: arrow_20]PhD in hard sciences or master’s degree in quantitative finance is a plus. [image: arrow_20]Very strong command of foundational mathematics, including multivariate calculus and linear algebra. [image: arrow_20]Good knowledge of statistics and probability. [image: arrow_20]Proficiency in MATLAB, Python, or similar programming languages. [image: arrow_20]Good command of English. *Compensation* Competitive For more information, please visit arpm.co <https://www.arpm.co/> and/or contact us at info(a)arpm.co <info(a)arpm.co?Subject=job%20Post> -- *ARPM <http://www.arpm.co/>** - Advanced Risk and Portfolio Management**®*

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