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Call for interest - Assegno di ricerca @ Polimi
by Daniele Marazzina 17 Sep '20

17 Sep '20

Con preghiera di diffusione. Grazie mille -------------------------------------------------- Il Qfinlab, laboratorio di finanza quantitativa del Dipartimento di Matematica del Politecnico di Milano, in collaborazione con il gruppo di ricerca del Professor Agasisti del Dipartimento di Ingegneria Gestionale, sta valutando la possibilità di bandire un assegno di ricerca della durata di un anno su tematiche relative all'educazione finanziaria, creazione di percorsi per le scuole e analisi di impatto. Eventuali persone interessate possono inviare il loro cv all'indirizzo qfinlab(a)polimi.it . Per informazioni sul gruppo, si rimanda a www.qfinlab.polimi.it Cordialmente Daniele Marazzina Emilio Barucci

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seminar De Palma
by Dario Trevisan 17 Sep '20

17 Sep '20

Dear all, sorry for another message about today's seminar by G. De Palma. I just want to mention that there was an extra bracket in the google meet link. If you want to follow online please use the following link: meet.google.com/sdy-qmac-kch Regards, 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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PhD opportunity at the University of Leipzig
by Giovanni Conforti 16 Sep '20

16 Sep '20

Dear colleagues, Please find below the announcement for two Phd positions in the group of Max von Renesse. Best Regards Giovanni Conforti At the university of Leipzig we have two vacant PhD positions in the stochastic analysis group with a duration of three years under the supervision of Max von Renesse. Possible research directions concern applications of stochastic analysis e.g. in geometry, physics or statistics. Together with the Max Planck Institute for Mathematics in the Sciences (MPI-MIS) and some world class research groups Leipzig offers a lively and stimulating environment for research in a variety of intertwining mathematical disciplines. Each position comes with a teaching load of 2 hrs per week during semesters. The positions are to be filled by October 15th preferably. Some knowledge of basic German is helpful but no must. The ideal candidate (m./f.) has an excellent recent Masters or Diploma degree in mathematics or theoretical physics with a strong background in a combination of at least two of the following fields: stochastic analysis, differential geometry, (stochastic) PDE, optimal transport theory or statistics. Applications should include a CV, transcript of university diplomas and a list of up to three potential contacts for letters of recommendation. Further inquiries and applications should be sent to Prof. Dr. Max von Renesse, renesse(a)uni-leipzig.de. Application deadline is 24 September 2020.

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seminar by De Palma -- change of room
by Dario Trevisan 15 Sep '20

15 Sep '20

Dear colleagues, the previously announced seminar by G. De Palma, to be held on Thursday, September 17, in Pisa, will take place in a larger room (Aula Magna), to safely accommodate more participants (up to 25 people). If you wish to join us in person please write me an email. Regards, Dario %%% Location: Aula Magna, Dipartimento di Matematica, Pisa (due to restrictions to the number of participants, we strongly suggest to attend online) Google Meet Link: meet.google.com/sdy-qmac-kch) Time: September 17, 2020, 2 PM Speaker: Giacomo De Palma (MIT) Title: Adversarial robustness guarantees for random deep neural networks Abstract: The reliability of most deep learning algorithms is fundamentally challenged by the existence of adversarial examples, which are incorrectly classified inputs that are extremely close to a correctly classified input. We study adversarial examples for deep neural networks with random weights and biases and prove that the L^1 distance of any given input from the classification boundary scales at least as sqrt(n), where n is the dimension of the input. We also extend our proof to cover all the L^p norms. Our results constitute a fundamental advance in the study of adversarial examples, and encompass a wide variety of architectures, which include any combination of convolutional or fully connected layers with skipped connections and pooling. We validate our results with experiments on both random deep neural networks and deep neural networks trained on the MNIST and CIFAR10 datasets. Given the results of our experiments on MNIST and CIFAR10, we conjecture that the proof of our adversarial robustness guarantee can be extended to trained deep neural networks. This extension will open the way to a thorough theoretical study of neural network robustness by classifying the relation between network architecture and adversarial distance. -- 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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Three Postdoc positions at WIAS-Interacting random systems group
by Luisa Andreis 14 Sep '20

14 Sep '20

Dear colleagues, at the Weierstrass Institute in Berlin we are looking to fill up to three postdoc positions. We would be grateful if you could bring this advertisement to any interested candidate. Here is a brief description of the call: "The successful candidates will analyze large interacting random systems using probabilistic methods within the newly established DFG priority program SPP 2265 "Random Geometric Systems" (https://www.wias-berlin.de/projects/SPP2265/ <https://www.wias-berlin.de/projects/SPP2265/>). The main research topics of these positions are phase transitions during coagulation and condensation. The third position is financed from other funds and offers more thematic flexibility within the research group and organizational responsibility. Please direct scientific queries to Prof. Dr. W. König (Koenig(a)wias-berlin.de <mailto:Koenig@wias-berlin.de>). Deadline for applications: 30.09.2020" For more details and to apply please visit https://www.wias-berlin.de/jobs/job.jsp?lang=1&year=20&number=15 <https://www.wias-berlin.de/jobs/job.jsp?lang=1&year=20&number=15> Best regards, Luisa Andreis Post-Doc researcher WIAS-Weierstrass Institute Mohrenstrasse 39, 10117 Berlin, Germany Personal webpage: https://sites.google.com/view/luisaandreis/home <https://sites.google.com/view/luisaandreis/home> Email: andreis(a)wias-berlin.de <mailto:andreis@wias-berlin.de>

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Seminar announcement
by Dario Trevisan 10 Sep '20

10 Sep '20

Dear colleagues, I would like to announce the following seminar by G. De Palma, in Pisa (and online, of course). Regards, Dario %%% Location: Sala Seminari, Dipartimento di Matematica, Pisa (due to restrictions to the number of participants, we strongly suggest to attend online) Google Meet Link: meet.google.com/sdy-qmac-kch) Time: September 17, 2020, 2 PM Speaker: Giacomo De Palma (MIT) Title: Adversarial robustness guarantees for random deep neural networks Abstract: The reliability of most deep learning algorithms is fundamentally challenged by the existence of adversarial examples, which are incorrectly classified inputs that are extremely close to a correctly classified input. We study adversarial examples for deep neural networks with random weights and biases and prove that the L^1 distance of any given input from the classification boundary scales at least as sqrt(n), where n is the dimension of the input. We also extend our proof to cover all the L^p norms. Our results constitute a fundamental advance in the study of adversarial examples, and encompass a wide variety of architectures, which include any combination of convolutional or fully connected layers with skipped connections and pooling. We validate our results with experiments on both random deep neural networks and deep neural networks trained on the MNIST and CIFAR10 datasets. Given the results of our experiments on MNIST and CIFAR10, we conjecture that the proof of our adversarial robustness guarantee can be extended to trained deep neural networks. This extension will open the way to a thorough theoretical study of neural network robustness by classifying the relation between network architecture and adversarial distance. -- 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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Fw: STAR - STochastics And Risk - Online Seminars kick-off
by michele giordano 09 Sep '20

09 Sep '20

Dear all, It is soon time to kick-off the online seminar series: STAR - STochastics And Risk - Online Seminars. This series of seminars brings together all interested people in probability, stochastic analysis, control, risk evaluation, statistics, with a view towards applications, in particular to renewable energy markets and production. This series brings together the major research themes of the projects SCROLLER<https://www.mn.uio.no/math/english/research/projects/scroller/index.html>, SPATUS<https://www.mn.uio.no/math/english/research/projects/spatus/>, and STORM<https://www.mn.uio.no/math/english/research/projects/storm/>. If you would like to take part to the seminars series please register here.<https://nettskjema.no/a/159180#/page/1> <https://nettskjema.no/a/159180#/page/1> On Friday 18. September from 11:00-12:00 the first seminar will be held, and after that it will run on a weekly basis. The lecture will last for 45 minutes + questions, and afterwards you are invited to bring a cup of coffee/tea and have a chat in our "Coffe in the Stars". The first speaker is Andreas Petersson, with the seminar: Finite element approximation of Lyapunov equations for the computation of quadratic functionals of SPDEs Abstract: We consider the computation of quadratic functionals of the solution to a linear parabolic stochastic partial differential equation (SPDE) with multiplicative Gaussian noise on a bounded domain. The functionals are allowed to be path dependent and the noise is white in time and may be white in space. An operator valued Lyapunov equation, whose solution admits a deterministic representation of the functional of the SPDE solution, is used for this purpose and error estimates are shown in suitable operator norms for a fully discrete approximation of this equation. We also use these estimates to derive weak error rates for a fully discrete approximation of the SPDE itself. In the setting of finite element approximations, a computational complexity comparison reveals that approximating the Lyapunov equation allows us to compute quadratic functionals more cheaply compared to applying Monte Carlo or covariance-based methods directly to the discretized SPDE. We illustrate the theoretical results with numerical simulations. For more details on this event, see: https://www.mn.uio.no/math/english/research/projects/storm/events/seminars/… There you will find a list of all the speakers, abstracts, and more details about the seminar series. If you register to the seminars series, you will receive a mail with the details for the zoom meeting next week. We are looking forward to see you, online! Best regards, Michele and Giulia

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AMASES Annual Conference 2020 - September 18 - online
by Tiziano Vargiolu 09 Sep '20

09 Sep '20

Dear Friends, this is to invite you to participate in AMASES 2020, the annual conference of the Association for Mathematics Applied to Social and Economic Sciences, which will take place in remote mode on September 18, 2020. The event is free of charge, it will be streamed via the Zoom platform provided by the Department of Mathematics of the University of Padova. The Invited Speakers are: Andreas Tsanakas Faculty of Actuarial Science and Insurance, Cass Business School, City University of London ?Sensitivity Analysis: From Scenario Weights to Dependence Cascades? Piero Manfredi Department of Economics and Management, University of Pisa ?Mathematical modeling of infectious diseases: from Sir Ronald Ross to COVID-19? Salvatore Greco Department of Economics and Business, University of Catania ?The Robust Ordinal Regression and the constructive approach of Multiple Criteria Decision Aiding? You can find all the information on the webpage: https://www.amases.org/annual-conference-2020/ All the people interested in attending the meeting are kindly invited to fill in the registration form at the following address until September 16 https://tinyurl.com/amases2020 Best wishes, The organizers A. Buratto G. Callegaro R. Cambini C. Fontana M. Grasselli L. Grosset E. Sartori T. Vargiolu B. Viscolani Tiziano -------------------------------------------------------------------------- Tiziano Vargiolu Dipartimento di Matematica Phone: +39 049 8271383 Universita' di Padova Fax: +39 049 8271428 Via Trieste, 63 E-mail: vargiolu(a)math.unipd.it I-35121 Padova (Italy) WWW: http://www.math.unipd.it/~vargiolu --------------------------------------------------------------------------

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Special Issue "Stochastic Optimization Methods in Economics, Finance and Insurance"
by c.ceci＠unich.it 09 Sep '20

09 Sep '20

Dear all, we are glad to inform you that submissions for the Special Issue "Stochastic Optimization Methods in Economics, Finance and Insurance" on Mathematics (https://www.mdpi.com/journal/mathematics) are open. The following is the official website: https://www.mdpi.com/journal/mathematics/sections/financial_mathematics Research articles, reviews, communications and concept papers will be considered. The deadline is December 2020, but it will be postponed in the future. Since Mathematics is an open access journal, there is a fee (1200 CHF) to be paid at acceptance. Short description of the Special Issue: stochastic optimization finds numerous and various applications in economics, finance and insurance. Among these, we may cite optimal portfolio selection, optimal reinsurance and investment problems, utility maximization and application to valuation of financial and insurance derivatives, optimal management of pension fund and public debt, risk measures. This Special Issue aims at collecting original research papers or comprehensive reviews on the theory and applications of dynamic stochastic optimization in economics, finance and insurance. Advanced mathematical tools have been employed to handle with these problems including viscosity solutions approach, martingale methods, backward stochastic differential equations (BSDEs), partial differential equations (PDEs), convex duality, filtering techniques and various numerical methods. Applications different from stochastic optimization will be possibly considered. If you have any questions, please do not hesitate to contact us. Please apologize for cross-posting. Guest Editor: Prof. Claudia Ceci Co-Guest Editor: Dr. Matteo Brachetta

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JOB: Assistant Professor in Data Science/Statistics at the University of Nottingham (Permanent)
by Fabrizio Leisen 09 Sep '20

09 Sep '20

------------------------------------------------------------------------ **Assistant Professor in Data Science/Statistics** School of Mathematical Sciences, The University of Nottingham Closing Date: Wednesday, 7th October 2020 Job Type: Research & Teaching - permanent post available from 04 Jan 2021. Salary: £36914 to £49553 per annum (pro-rata if applicable) depending on skills and experience. Salary progression beyond this scale is subject to performance. ------------------------------------------------------------------------ We are looking for an Assistant Professor to deliver outstanding teaching and research in Data Science or Statistics. This post, jointly funded for the first four years by Horizon (horizon.ac.uk) and the School of Mathematical Sciences (http://www.nottingham.ac.uk/mathematics/index.aspx), provides a pathway to a permanent academic position for an early-career researcher. The role-holder will initially focus on research within Horizon (horizon.ac.uk), with minimal teaching, then transition by the fourth year to working evenly between research within Horizon and teaching in Mathematical Sciences. After four years, the role-holder will become a permanent member of staff within the School of Mathematical Sciences, complementing and enhancing activity in the Statistics and Probability Section. We are particularly seeking candidates with expertise in computational statistics, especially Bayesian methodology and applications, and machine learning, though we welcome applications from any candidates who can show how their expertise fits with the Horizon, the Statistics and Probability Section, and/or other research groups within the School of Mathematical Sciences. Importantly, candidates must be keen to bring their skills and experience into the interdisciplinary team within Horizon addressing the overall socio-technical challenges of the future Digital Economy. Applicants should hold (or shortly expect to receive) a PhD or equivalent in Statistics, Data Science or related area. We believe that talented and inclusive teams deliver the highest quality research and are seeking applications from excellent candidates who enhance the diversity of our existing team. The School is committed to creating opportunities for people traditionally under-represented in Mathematical Sciences. You will have a proven track record, or demonstrated potential, in publishing research of international quality in Statistics, Data Science or related area, and be able to evidence how you will: - Develop research proposals to attract funding from Research Councils, Industry, EU, and other sources - Contribute to disseminating research outputs at national/international conferences, workshops, and meetings - Plan and deliver high quality teaching on Statistics modules - Provide curriculum development within your own area of expertise and collaborate with colleagues to design modules and/or programmes - Demonstrate excellent communication, organisation, interpersonal and time management skills Informal enquiries may be addressed to Professor Theodore Kypraios (Head of Statistics and Probability Section) by email at theodore.kypraios(a)nottingham.ac.uk. Please note that applications sent directly to this email address will not be accepted. More info (role profile, additional information, supporting information fro EU/EEA/Overseas nationals etc) can be found here: https://www.nottingham.ac.uk/jobs/currentvacancies/ref/SCI245020 -- Fabrizio Leisen Professor of Statistics University of Nottingham School of Mathematical Sciences University Park, Nottingham, NG7 2RD, UK http://sites.google.com/site/fabrizioleisen/

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