• Geo-Sci-Info


    The ICTRP Search Portal
    Important information related to the COVID-19 outbreak!

    Due to heavy traffic generated by the COVID-19 outbreak, the ICTRP Search Portal is not accessible from outside WHO temporarily. Please subscribe to the ICTRP listserv if you wish to be notified when the search portal is working again. Information on how to subscribe can be found on the same page below.

    Click here to download all COVID-19 trials from the ICTRP database
    Click here to download all new/updated records from the ICTRP database

    The ICTRP Search Portal aims to provide a single point of access to information about ongoing and completed clinical trials. It provides a searchable database containing the trial registration data sets made available by data providers around the world meeting criteria for content and quality control.

    Trial registration data sets are available on the ICTRP Search Portal in English only. Some data providers, however, may also store trial registration data sets in other languages. If available, these may be searched by going directly to the web sites of the individual registries. It is possible to search for trials registered in certain Primary Registries in the following languages:

    Chinese - Chinese Clinical Trial Registry (ChiCTR)
    Dutch - The Netherlands National Trial Register (NTR)
    German – German Clinical Trials Register (DRKS)
    Japanese – Japan Primary Registries Network (JPRN)
    Korean – Clinical Research Information Service (CRiS), Republic of Korea
    Persian – Iranian Registry of Clinical Trials (IRCT)
    Portuguese – Brazilian Registry of Clinical Trials (ReBec)
    Spanish – Cuban Public Registry of Clinical Trials(RPCEC) and the Peruvian Clinical Trial Registry (REPEC)

    To facilitate the unambiguous identification of trials, the Search Portal bridges (groups together) multiple records about the same trial.

    The ICTRP Search Portal has been developed to make it easier for users to search for clinical trials. Please contact us if you have any comments or suggestions.
    Subscribe to the ICTRP mailing list for technical updates

    To receive the latest news and events concerning the WHO ICTRP technical updates and notifications. Simply send us an email by clicking the link below.

    Subscribe to the ICTRP Technical updates mailing list

    If you have any difficulties with the link above, please send us an email following these instructions:

    To: listserv [at] who.int
    Subject: subscription: WHO ICTRP Tech updates
    Body: subscribe ictrptech Firstname Lastname (Please replace Firstname and Lastname in this command line with your own.)

    posted in COVID19 open Database ressources and challenges read more
  • Geo-Sci-Info

    Capture du 2020-04-05 12-13-44.png


    by Max Roser, Hannah Ritchie and Esteban Ortiz-Ospina
    We thank Bernadeta Dadonaite, Jason Hendry, and Moritz Kraemer for helpful comments and suggestions on earlier versions of this work.Tom Chivers we would like to thank for editorial review and feedback.

    And we would like to thank the many hundreds of you who give us feedback on this work every day.
    Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write.
    Even if we can’t respond to every message we receive, we do read all feedback and take it all into account.

    Note: To inform yourself and understand the risk to the public we recommend to rely on your government body responsible for health and the World Health Organization – their site is here.

    The mission of Our World in Data is to make data and research on the world’s largest problems understandable and accessible.

    Read more about our mission →

    Only on the basis of clearly presented and well-documented data can governments, organizations and individuals hope to respond appropriately to the COVID-19 pandemic. The goal of our work here is to present the best available data and clarify what can – and can not be said – based on this data.

    We list all our visualizations – more than 40 in total – on the pandemic on this page.

    This article covers a developing situation and the Our World in Data team is updating it daily: The last update was made on April 4, 2020 (11:30, London time).

    Even the best existing research and data is preliminary and will be revised as the pandemic progresses.
    We reviewed existing global data sources and decided to rely on the global statistics published by the European Center for Disease Prevention and Control (ECDC).

    During a disease outbreak it is the growth rate that deserves attention. We present a table that lists how fast the number of deaths is doubling.
    In interactive charts we present the data on confirmed deaths in all countries in the world.

    Without widespread testing for COVID-19 we can neither know how the pandemic is spreading nor appropriately respond to it.

    The total number of COVID-19 cases is not known. It is however certain that the total number of COVID-19 cases is higher than the number of known confirmed cases. This is mainly due to limited testing.
    Just as we do for deaths, we focus on the growth rate of confirmed cases. We present a sortable table that lists how fast the number of confirmed cases is doubling.
    In interactive charts we present the data on confirmed cases over time in all countries in the world.

    The case fatality rate (CFR) – the ratio between confirmed deaths and cases – is widely discussed, but during the outbreak of a pandemic with large unknowns it is important to know what can and cannot be said based on currently available statistics.

    We rely on data from the European CDC

    In this document and the many embedded and linked charts we report and visualize the data from the European Center for Disease Prevention and Control (ECDC).

    The European CDC publishes daily statistics on the COVID-19 pandemic. Not just for Europe, but for the entire world.

    The ECDC makes all their data available in a daily updated clean downloadable file. This gets updated daily reflecting data collected up to 6:00 and 10:00 CET. The data made public via the downloadable data file is published at 1pm CET, and is used to produce a page that gets updated daily under the name Situation Update Worldwide.

    We discuss our data sources and why we rely on the data from the ECDC rather than other institutions at the end of this article here.
    Reported new cases on a particular day do not necessarily represent new cases on that day

    The number of confirmed cases or deaths reported by any institution – including the WHO, the ECDC, Johns Hopkins and others – on a given day does not represent the actual number of new cases or deaths on that date. This is because of the long reporting chain that exists between a new case or death, and its inclusion in national or international statistics.

    The steps in this chain are different across countries, but for many countries the reporting chain includes most of the following steps:1

    Doctor or laboratory diagnoses a COVID-19 case based on testing or combination of symptoms and epidemiological probability (such as a close family member testing positive).
    Doctor or laboratory submits report to health department of the city or local district.
    Health department receives the report and records each individual case in the reporting system, including patient information.
    The ministry or another governmental organization brings this data together and publishes the latest figures.
    International data bodies such as the WHO or the ECDC can then collate statistics from hundreds of such national accounts.

    This reporting chain can take several days. This is why the figures reported on any given date do not necessarily reflect the number of new cases or deaths on that specific date.

    posted in COVID19 open Database ressources and challenges read more
  • Geo-Sci-Info

    Polymath proposal: clearinghouse for crowdsourcing COVID-19 data and data cleaning requests

    25 March, 2020 in math.ST, polymath | Tags: Chris Strohmeier, coronavirus

    After some discussion with the applied math research groups here at UCLA (in particular the groups led by Andrea Bertozzi and Deanna Needell), one of the members of these groups, Chris Strohmeier, has produced a proposal for a Polymath project to crowdsource in a single repository (a) a collection of public data sets relating to the COVID-19 pandemic, (b) requests for such data sets, (c) requests for data cleaning of such sets, and (d) submissions of cleaned data sets. (The proposal can be viewed as a PDF, and is also available on Overleaf). As mentioned in the proposal, this database would be slightly different in focus than existing data sets such as the COVID-19 data sets hosted on Kaggle, with a focus on producing high quality cleaned data sets. (Another relevant data set that I am aware of is the SafeGraph aggregated foot traffic data, although this data set, while open, is not quite public as it requires a non-commercial agreement to execute. Feel free to mention further relevant data sets in the comments.)

    This seems like a very interesting and timely proposal to me and I would like to open it up for discussion, for instance by proposing some seed requests for data and data cleaning and to discuss possible platforms that such a repository could be built on. In the spirit of “building the plane while flying it”, one could begin by creating a basic github repository as a prototype and use the comments in this blog post to handle requests, and then migrate to a more high quality platform once it becomes clear what direction this project might move in. (For instance one might eventually move beyond data cleaning to more sophisticated types of data analysis.)

    UPDATE, Mar 25: a prototype page for such a clearinghouse is now up at this wiki page.

    UPDATE, Mar 27: the data cleaning aspect of this project largely duplicates the existing efforts at the United against COVID-19 project, so we are redirecting requests of this type to that project (and specifically to their data discourse page). The polymath proposal will now refocus on crowdsourcing a list of public data sets relating to the COVID-19 pandemic.

    posted in COVID19 open Database ressources and challenges read more
  • Geo-Sci-Info

    Capture du 2020-04-05 11-29-15.png

    COVID-19 Open Research Dataset Challenge (CORD-19) An AI challenge with AI2, CZI, MSR, Georgetown, NIH & The White House

    Dataset Description

    In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19). CORD-19 is a resource of over 47,000 scholarly articles, including over 36,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. This freely available dataset is provided to the global research community to apply recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease. There is a growing urgency for these approaches because of the rapid acceleration in new coronavirus literature, making it difficult for the medical research community to keep up.

    Call to Action

    We are issuing a call to action to the world's artificial intelligence experts to develop text and data mining tools that can help the medical community develop answers to high priority scientific questions. The CORD-19 dataset represents the most extensive machine-readable coronavirus literature collection available for data mining to date. This allows the worldwide AI research community the opportunity to apply text and data mining approaches to find answers to questions within, and connect insights across, this content in support of the ongoing COVID-19 response efforts worldwide. There is a growing urgency for these approaches because of the rapid increase in coronavirus literature, making it difficult for the medical community to keep up.

    A list of our initial key questions can be found under the Tasks section of this dataset. These key scientific questions are drawn from the NASEM’s SCIED (National Academies of Sciences, Engineering, and Medicine’s Standing Committee on Emerging Infectious Diseases and 21st Century Health Threats) research topics and the World Health Organization’s R&D Blueprint for COVID-19.

    Many of these questions are suitable for text mining, and we encourage researchers to develop text mining tools to provide insights on these questions.

    We are maintaining a summary of the community's contributions. For guidance on how to make your contributions useful, we're maintaining a forum thread with the feedback we're getting from the medical and health policy communities.

    Kaggle is sponsoring a $1,000 per task award to the winner whose submission is identified as best meeting the evaluation criteria. The winner may elect to receive this award as a charitable donation to COVID-19 relief/research efforts or as a monetary payment. More details on the prizes and timeline can be found on the discussion post.

    Accessing the Dataset

    We have made this dataset available on Kaggle. Watch out for periodic updates.

    The dataset is also hosted on AI2's Semantic Scholar. And you can search the dataset using AI2's new COVID-19 explorer.

    The licenses for each dataset can be found in the all _ sources _ metadata csv file.


    Capture du 2020-04-05 11-33-51.png

    This dataset was created by the Allen Institute for AI in partnership with the Chan Zuckerberg Initiative, Georgetown University’s Center for Security and Emerging Technology, Microsoft Research, and the National Library of Medicine - National Institutes of Health, in coordination with The White House Office of Science and Technology Policy.

    posted in COVID19 open Database ressources and challenges read more
  • Geo-Sci-Info


    Through the "2019 Novel Coronavirus Resource" database, you can gain hands-on access to the latest most comprehensive novel coronavirus data and research results. Developed and operated by the China National Center for Bioinformation, Beijing Institute of Genomics, CAS, the database was launched on January 22. It provides a one-stop browse, search and share service for the scientific community.

    To date, the database has included worldwide information on 2,533 non-redundant novel coronavirus genome sequences. At the same time, it has categorized 3,099 international articles related to the novel coronavirus and welcomed more than 83,000 visitors from 155 countries and regions, providing over 4.91 million downloads. About 70% of those visits were from international visitors.

    If you have not checked out the database yet, click the link below!


    posted in COVID19 open Database ressources and challenges read more
  • Geo-Sci-Info

    Capture du 2020-03-09 02-09-50.png
    Appel à Candidatures
    Prix de Thèse Systèmes Complexes 2020

    Depuis 2017, l'Institut des Systèmes Complexes de Paris IdF (ISC-PIF) et ses partenaires décernent annuellement un prix de thèse dont l'objectif est de mettre à l'honneur la recherche dans le domaine des systèmes complexes et de distinguer les travaux de jeunes chercheur·euse.s particulièrement prometteur·euse.s.

    Ce prix est organisé en collaboration avec l'Académie d'Excellence "Systèmes Complexes" de l'Université Côte d'Azur et l’Institut Rhônalpin des Systèmes Complexes (IXXI), avec le soutien du CNRS et de la région Île-de-France.

    Nous vous invitons cordialement à diffuser largement cet appel ou à nominer un.e ou plusieurs candidat.e.s ayant soutenu leurs thèses entre janvier et décembre 2019, dans une école doctorale française.

    Veuillez noter que les jeunes docteurs de nationalité étrangère peuvent participer à cette sélection.

    Pour nominer un.e ou plusieurs candidat.e.s, merci de nous transmettre leurs coordonnées en réponse de ce mail, en les mettant éventuellement en copie.

    Ci dessous, les détails de l'appel à candidature que nous vous invitons cordialement à partager.
    Télécharger le pdf de l'appel


    Les thèses éligibles auront été soutenues entre janvier et décembre 2019, dans une école doctorale française. Les jeunes docteurs de nationalité étrangère peuvent participer à cette sélection. Les thèses rédigées en anglais sont acceptées par le jury.

    Pour pouvoir concourir à ce prix, les candidat.e.s doivent faire la preuve de la pertinence de leurs travaux par rapport à l'approche systèmes complexes et doivent aborder un ou plusieurs objets et questions propres à la recherche Systèmes Complexes : voir tous les détails du Prix de Thèse 2020 sur notre site.


    Les candidats et candidates devront remplir leur candidature en ligne avant le 22 mars 2020 à minuit.

    Documents demandés : CV et lettre de motivation, rapport du jury de thèse et pré-rapports des rapporteurs, résumé de la thèse.


    Les candidat.e.s sélectionnés seront invités à présenter leur travaux devant un jury interdisciplinaire composé de personnalités reconnues dans le domaine. Les auditions auront lieu à l'ISC-PIF (Paris 13e), le 22 mars 2020.

    Les critères pris en compte par le jury pour sélectionner les lauréat.e.s sont l’importance et l’originalité des contributions dans le domaine des systèmes complexes, la qualité du manuscrit et, éventuellement, l’interdisciplinarité du travail.


    • 22 mars 2020 : Date limite des dépôts de candidatures
    • 15 mai 2020 : Annonce des finalistes
    • 17 juin 2020 : Audition des finalistes et remise des prix
      + d'infos sur le site


    • Annick Vignes, CAMS (CNRS EHESS), LISIS.
    • Julien Randon-Furling, SAMM (Univ. Paris 1 Panthéon Sorbonne), Fédération Parisienne de Modélisation Mathématique (CNRS).


    Capture du 2020-03-09 02-07-36.png

    posted in Complex System PhD Prize read more
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    Welcome from the Chair
    The concept of entropy emerges initially from the scope of physics, but it is now clear that entropy is deeply related to information theory and the process of inference. Today, entropic techniques have found a broad spectrum of applications in all branches of science.

    The conference will be organized into six sessions, which reflect the inter-disciplinary nature of entropy and its applications:

    • Statistical Physics
    • Information Theory, Probability and Statistics
    • Thermodynamics
    • Quantum Information and Foundations
    • Complex Systems
    • Entropy in Multidisciplinary Applications

    The inter-disciplinary and multi-disciplinary nature of contributions from both theoretical and applied perspectives are welcome, including papers addressing conceptual and methodological developments, as well as new applications of entropy and information theory.

    All accepted abstracts will be published in the proceedings of the conference. Moreover, participants are cordially invited to contribute with a full manuscript to Entropy Special Issue "Entropy: The Scientific Tool of the 21st Century".

    Special Issue Submission deadline: 30 October 2020
    Papers presented at the conference will be granted a 30% discount!

    The conference is sponsored by MDPI, the publisher of the open-access journal Entropy and follows the very successful meeting Entropy 2018: From Physics to Information Sciences and Geometry held in May 2018 in Barcelona, Spain.

    We very much look forward to seeing you at this exciting meeting in Porto.

    Please feel free to download our Conference Poster.

    Conference Secretariat

    • Ms. Yuejiao Hu
    • Ms. Connie Xiong
    • Ms. Stefanie Li
      Email: entropy2020 [at] mdpi.com

    Conference Chairs

    • Professor J. A. Tenreiro Machado, Institute of Engineering (ISEP), Department of Electrical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal, Website

    Conference Committee

    • Professor Walter Lacarbonara, Department of Structural and Geotechnical Engineering , Sapienza University of Rome, Rome, Italy
    • Professor Jose C. Principe, Computational NeuroEngineering Lab, University of Florida, Gainesville, FL, USA
    • Prof. Dr. Gian Paolo Beretta, Department of Mechanical and Industrial Engineering, University of Brescia, Brescia, Italy
    • Professor Geert Verdoolaege, Research Unit Nuclear Fusion, Department of Applied Physics, Ghent University, Ghent, Belgium
    • Prof. Dr. William B. Sherwin, Evolution & Ecology Research Centre, School of Biological Earth and Environmental Science, UNSW Sydney, Sydney, Australia
    • Prof. Dr. Ali Mohammad-Djafari, Former Research Director, CNRS, Paris, France
    • Dr. Ginestra Bianconi, Queen Mary University of London, London, UK

    Invited Speakers

    • Prof. Dr. Kevin H. Knuth, University at Albany (SUNY), Albany, NY, USA
    • Prof. Dr. Miguel Rubi, University of Barcelona, Barcelona, Spain
    • Prof. Dr. Philip Broadbridge, La Trobe University, Melbourne, Australia
    • Prof. Dr. Marcel Ausloos, University of Leicester, Leicester, UK
    • Dr. Remo Garattini, University of Bergamo, Bergamo, Italy
    • Prof. Dr. Nihat Ay, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
    • Prof. Dr. Luca Gammaitoni, Università degli Studi di Perugia, Perugia, Italy
    • Prof. Dr. Abraham Marmur, Technion-Israel Institute of Technology, Haifa, Israel
    • Dr. Rosario Lo Franco, Università di Palermo, Italy
    • Prof. Dr. Miguel A. F. Sanjuán, Universidad Rey Juan Carlos, Madrid, Spain

    Conference Organizers

    • Nídia Caetano, Institute of Engineering (ISEP), Department of Chemical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal
    • Carlos Felgueiras, Institute of Engineering (ISEP), Department of Electrical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal
    • Ana Maria Madureira, Instituto Superior de Engenharia do Porto (ISEP), Porto, Portugal
    • Luís M. Afonso, ISRC (Interdisciplinary Studies Research Center) , Instituto Superior de Engenharia do Porto, Porto, Portugal
    • Alexandra M.F. Galhano, Institute of Engineering (ISEP), Department of Electrical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal


    • SESSION 1. Statistical Physics
      classical, quantum, and relativistic statistical mechanics; kinetics theory; dynamical processes and relaxation phenomena; geometric applications to statistical physics; classical and quantum stochastic processes; combinatorial aspects of statistical physics and quantum field theory; quantum information and entangled states. Statistical physics of complex and disordered systems: biophysics, genomics, ecological and evolutionary systems, climate and earth models (including seismology); traffic flow; nonlinear time series analysis; big data analysis and algorithm problems; networks and graphs; random and fractal systems; pattern formation; collective phenomena in economic and social systems. Show all accepted abstracts (12)
    • SESSION 2. Information Theory, Probability and Statistics
      Shannon entropy; Kullback-Leibler divergence; channel capacity; source coding; channel coding; algorithmic complexity theory; algorithmic information theory; information–theoretic security; information geometry; Bayesian inference; information theoretic learning; deep learning. Show all accepted abstracts (18)
    • SESSION 3. Thermodynamics
      heat transfer; Carnot cycle; heat engine; thermodynamic temperature; entropy generation; Clausius equality/inequality; equilibrium/non-equilibrium thermodynamics; work availability; exergy; laws of thermodynamics. Show all accepted abstracts (26)
    • SESSION 4. Quantum Information and Foundations
      quantum foundations; quantum probability and non-Kolmogorov models; entanglement entropy; Eigenstate thermalization; quantum phase transitions; topological order; black hole information paradox; the holographic principle; quantum coherent transport; quantum coherence in biological phenomena. Show all accepted abstracts (9)
    • SESSION 5. Complex Systems
      complexity; nonlinearity; fractionality; nonlinear dynamics; fractional calculus; chaos; big data; networks; cybernetics; biology. Show all accepted abstracts (18)
    • SESSION 6. Entropy in Multidisciplinary Applications
      signal processing and data analysis; entropy and complexity in biology; geosciences; environment; social network; economy and finance. Show all accepted abstracts (40)


    posted in Entropy 2020: The Scientific Tool of the 21st Century read more
  • Geo-Sci-Info

    The division of science at New York University in Abu Dhabi is currently advertising two-years postdoctoral positions to work with me on subjects in high dimensional probability and related fields.
    More information can be found at the following link: https://apply.interfolio.com/74217 where the applications should be submitted. Please do not hesitate to share the message with potential candidates and feel free to contact me directly for further information.

    The anticipated start date is September 1, 2020. Applications should be received before March 15 to be guaranteed full consideration. (Candidates should please ask their referees to send their letters of reference by that date.)

    posted in Jobs offers - Call for projects read more
  • Geo-Sci-Info

    Open GPU Data Science | RAPIDS


    The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs.
    Learn more

    Seamlessly scale from GPU workstations to multi-GPU servers and multi-node clusters with Dask.
    Learn more about Dask

    Accelerate your Python data science toolchain with minimal code changes and no new tools to learn.

    Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.

    Drastically improve your productivity with more interactive data science.
    Learn more about XGBoost

    RAPIDS is an open source project. Supported by NVIDIA, it also relies on numba, apache arrow, and many more open source projects.
    Learn more

    posted in GSI FORGE read more
  • Geo-Sci-Info

    Capture du 2020-02-16 13-55-41.png
    This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.

    It provides the following solvers:

    • OT Network Flow solver for the linear program/ Earth Movers Distance [1].
    • Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional * GPU implementation (requires cupy).
    • Sinkhorn divergence [23] and entropic regularization OT from empirical data.
    • Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17].
    • Non regularized Wasserstein barycenters [16] with LP solver (only small scale).
    • Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4].
    • Optimal transport for domain adaptation with group lasso regularization [5]
    • Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
    • Linear OT [14] and Joint OT matrix and mapping estimation [8].
    • Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt).
    • Gromov-Wasserstein distances and barycenters ([13] and regularized [12])
    • Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
    • Non regularized free support Wasserstein barycenters [20].
    • Unbalanced OT with KL relaxation distance and barycenter [10, 25].

    Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.

    Using and citing the toolbox
    If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference:

    title={POT Python Optimal Transport library},
    author={Flamary, R{'e}mi and Courty, Nicolas},

    posted in GSI FORGE read more
  • Geo-Sci-Info

    lease find below the announcement of three positions (two M2
    internships and one post-doc position) opened in Nancy :





    PhD opening at the University of Groningen

    The group of Probability and Statistics at the University of Groningen invites applications for a

    4-year PhD position

    During your PhD you will work at the forefront of research in topological data analysis and spatial random networks. While the previous years have brought forth outstanding progress in the description of large combinatorial random graphs, the understanding of random structures with spatial dependencies is still in its infancy. A key component of your research will be to identify regimes that are amenable to a rigorous mathematical description in terms of central limit theorems and large deviation principles.

    Modern materials science is a major driving force behind the interest in topological data analysis and spatial random networks, since the insights gained from appropriate mathematical models make it possible to reduce the number of time-consuming experiments. With the newly established CogniGron, the University of Groningen has created an interdisciplinary research center with the goal of making progress towards cognitive computing. Hence, you will have the unique opportunity to interact broadly with colleagues outside mathematics. You will also be expected to contribute to the teaching at the faculty.

    Hence, if you have graduated with an outstanding Master's degree (or are about to do so soon) and are committed to working on fundamental research questions, we are very much looking forward to your application. Any background knowledge in large deviation theory, random networks or central limit theorems will help you to get a smoother start.

    We offer a full-time PhD position for 4 years. The salary and working hours are determined according to the Collective Labour Agreement for Dutch Universities. In 2019, PhD candidates started with € 2,325 gross per month; for a candidate in the fourth year, this figure rises to € 2,972. In addition to that, the salary scheme comprised 8% of the yearly salary as holiday bonus and 8.3% as end of your bonus.

    We are an equal opportunity employer that values diversity. We have adopted an active policy to increase the number of female scientists across all disciplines of the university.

    We envision that you join us on April 1, 2020 or later. Please send us until January 27, 2020 your cover letter, CV, Master's thesis (or a preliminary version) and transcript of records. All documents should be sent to dr. Christian Hirsch: c.p.hirsch@rug.nl, who will also be happy to answer your questions on the position.



    Applications are invited for a StatLab–CANSSI–CRM Postdoctoral Fellowship in Statistics. To be eligible, candidates should have received their PhD after June 30, 2016, and must have fulfilled all PhD requirements by the time of taking up the award. The successful candidate must be supervised by at least one Regular Member of StatLab and be based in a member’s institution for the tenure of the award. A list of StatLab members can be found here: http://www.crm.umontreal.ca/labo/stat/membres/

    The fellowship is worth $45 000. A successful candidate will be designated a StatLab–CANSSI–CRM Postdoctoral Fellow for a period of one year, starting any time between June 1 and September 30, 2020.

    An application includes: (i) a presentation letter including in particular a description of research interests and research program; (ii) the candidate’s CV; (iii) electronic copies of articles, theses, research reports, etc. Candidates must prepare the application using the guidelines presented here: http://www.crm.umontreal.ca/labo/stat/en/postdoctoral-fellows/

    and send it to coeurjolly.jean-francois@uqam.ca by January 31, 2020. Candidates should arrange as well to have at least two letters of recommendation sent independently.

    PDF announcement is enclosed to this email.


    Un appel à candidatures est lancé pour une bourse postdoctorale StatLab–INCASS–CRM en statistique. Pour être éligible, un candidat doit avoir obtenu son doctorat après le 30 juin 2016 et avoir complété toutes les exigences du programme de doctorat au moment de débuter le stage. Il ou elle doit être supervisé par au moins un membre régulier de StatLab et être inscrit dans l’établissement d’un tel membre pendant la période de validité de la bourse. Pour une liste complète des membres de StatLab, voir http://www.crm.umontreal.ca/labo/stat/membres/

    La bourse est d’une valeur de 45 000$. Le boursier portera le titre de stagiaire postdoctoral StatLab–INCASS–CRM pendant une période d’un an pouvant commencer à tout moment entre le 1er juin et le 30 septembre 2020.

    Un dossier de candidature comprend: (i) une lettre de présentation incluant notamment une description des intérêts de recherche, un programme de recherche; (ii) le CV du candidat; (iii) copies électroniques d'articles, thèse, rapport de recherche, etc. Les candidats doivent préparer leur dossier en suivant les indications disponibles ici: http://www.crm.umontreal.ca/labo/stat/stagiaires-postdoctoraux/

    et envoyer leur dossier à coeurjolly.jean-francois@uqam.ca avant le 31 janvier 2020. Les candidats doivent également s'arranger pour transmettre au moins deux lettres de recommandation, envoyées indépendamment.

    L'annonce officielle au format pdf est jointe à ce courriel.

    posted in Jobs offers - Call for projects read more
  • Geo-Sci-Info

    Lors de la prochaine campagne synchronisée, un poste MCF 26/27
    "Statistique pour la science des données" sera publié à l'IUT
    d'Avignon, département STID. La personne recrutée intègrera le
    département STID et l'équipe de Statistique du Laboratoire de
    Mathématiques d'Avignon (LMA). Le profil détaillé est disponible via le
    lien :


    Je vous remercie par avance de faire suivre cette annonce autour de
    vous. Nous invitons toutes les personnes intéressées à contacter
    Delphine Blanke (responsable STID & équipe de Statistique) et Céline
    Lacaux (directrice du LMA).

    Amitiés, Céline.

    PS : Je vous prie de nous excuser pour les réceptions multiples de cette

    posted in Jobs offers - Call for projects read more
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    The Faculty of Electrical Engineering, Mathematics and Computer Science
    at the University of Twente, The Netherlands, invites applications for a
    fully funded

    PhD studentship in spatial statistics

    in the project `Data driven risk management for fire services' funded
    by the Dutch Research Council.

    This project aims to develop a flexible hierarchical statistical
    framework for the analysis and prediction of various types of fires
    and will be carried out in close cooperation with the Twente Fire
    Brigade. It will deliver a model for spatio-temporal inference
    (including monitoring, filtering and prediction) that is able to
    recognize patterns in historic data and convert these to a prediction.
    The results can be used for both repression and prevention purposes,
    including improving vehicle and personnel planning in the fire stations,
    informing the right people at the right time and evaluating the
    effectivity of public awareness campaigns.

    Candidates are required to have a Masterʼs degree (or an equivalent one)
    in mathematics or a related discipline and a solid background in
    probability and statistics. They should have excellent grades, research
    talent (as proven by their thesis), an excellent command of English and
    good academic writing and presentation skills.

    The terms of employment and salary are in accordance with the Dutch
    Collective Labour Agreement for universities.

    To apply, please send a letter of motivation, a list of your BSc and
    MSc courses with grades and either a copy of your Master's thesis if
    available or a copy of your Bachelor's thesis accompanied by an outline
    of your Master's thesis to

    Dr.ir. M. de Graaf (m.degraaf [at] utwente.nl) or
    Prof.dr. M.N.M. van Lieshout (colette [at] cwi.nl).

    from whom further information can also be obtained.

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    W3. Complex Networks: Hidden Geometry and Dynamics
    Workshop organized by: B. Tadic and N. Gupteo

    The influence of network structure on dynamics in many complex systems has been demonstrated in numerous studies with a detailed analysis of empirical data and theoretical approaches. Recent studies of networks representing various complex systems from the brain to large-scale social dynamics have revealed their higher-order architecture, which can be described as aggregates of simplexes (triangles, tetrahedrons, and higher cliques). Beyond the standard graph theory, these hidden structures are quantified by algebraic topology methods. This Special Session will bring together presentations exploring different complex systems concerning a) the structure of simplicial complexes, including the properties of the underlying topological graph (network); b) dynamic processes in a particular or co-evolving network structure; c) information topology and graph representations of time series. We expect research results based on empirical data analysis and theoretical and numerical modeling. The aim is to cover recent developments in these areas of research and to stimulate discussion towards an in-depth understanding of the role of higher-order connectivity in the emergence of functional properties of complex systems.

    posted in Sigmaphi 2020 read more
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    S3. Information Geometry and Deformed Statistics
    Special Session organized by J. Zhang and H. Matsuzoe

    Information geometry provides a suite of differential geometric tools for studying statistical inference, information theory, and machine learning models. Key notions such as statistical manifolds (with Fisher information as to its Riemannian metric), Hessian geometry, biorthogonal coordinates have links to statistical mechanics, thermodynamics, geometric mechanics, etc. As sequel to the special session (organized by Johnston, Matsuzoe, Ruppeiner, and Wada) at SigmaPhi2017, this Session at SigmaPhi2020 will explore differential geometric characterizations of probabilistic models arising from a variety of setting including stochastic thermodynamics, condensed matter physics, cosmology and high-energy physics, etc. Of particular interest are deformed statistical models, models which deviate from exponential family through parameterization (e.g., kappa-exponential model, q-exponential model, Renyi model) and their associated deformed entropy, cross-entropy, and divergences. The Session will also welcome contributions from related disciplines of statistical machine learning, dynamics and control, optimal transport and Wasserstein geometry, etc.

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    13-17 July 2020 - Chania, Crete, Greece

    The Conference is organized in the following three Areas to cover all the Topics of Statistical Physics:

    Area A: Foundations and Theoretical aspects of classical, quantum and relativistic statistical physics and thermodynamics. Mathematical aspects and methods, formalism, rigorous results, exact solutions, connections with the methods of high energy physics, string theory, mathematical statistics and information theory, information geometry, classical, quantum and relativistic transport theory, Boltzmann and Fokker-Planck kinetics, nonlinear kinetics, dynamical systems, relaxation phenomena, random systems, pattern formation, fractal systems, solitons, chaotic systems, strongly correlated electrons, soft quantum matter, mesoscopic quantum phenomena, fractional quantum Hall effect, low dimensional quantum field theory, quantum phase transitions, quantum information and entanglement, power laws, stochastic optimal controletc.

    Area B: Applications to Physical Systems: quantum systems, soft condensed matter, liquid crystals, plasmas, fluids, surfaces and interfaces, disordered and glassy systems, percolation, spin glasses, structural glasses, jamming, critical phenomena and phase transitions,fluids and interfacial phenomena, molecular and ionic fluids, metastable liquids, hydrodynamic instabilities, turbulence, growth processes, wetting, surface effects, films, crystals, confined systems, surfaces and interfaces, chemical reactions, cold atoms, etc.

    Area C: Applications to non-Physical Systems: Interdisciplinary applications of statistical physics, networks and graphs, applied networks, biophysics, genomics, environments, climate and earth models, seismology, linguistics, econophysics, social systems, traffic flow, algorithmic problems, complex systems, nonlinear time-series analysis, novel data analysis tools, extreme events, tipping points, prediction, classification, etc.

    Any area include several Sessions dealing with general aspects and applications.

    Special sessions: Some special sessions within the thematic Area A will cluster talks dedicated to the following theoretical topics:

    • S1: Spin glass theory and far beyond;
    • S2: Quantum long-range interactions;
    • S3: Information geometry and deformed statistics;
    • S4: Entropies and correlations in complex systems.

    Workshops: Some workshops will cluster talks dedicated to special applications of statistical physics in physical and non-physical systems and will be organized as parallel events.

    Poster Sessions: The Conference will also include some poster sessions.

    Conference Chairman

    • Kaniadakis G. (Politecnico di Torino, Italy)

    Organizing Committee

    • Argyrakis P. (Aristotle University of Thesaloniki, Greece)
    • Carbone A. (Politecnico di Torino, Italy)
    • Constantoudis V. (NCSR Demokritos, Athens, Greece)
    • Ellinas D. (Technical University of Crete, Chania, Greece)
    • Gervino G. (University of Torino, Italy)
    • Hristopulos D. (Technical University of Crete, Chania, Greece)
    • Lapenta G. (Katholieke University of Leuven, Belgium)
    • Lissia M. (INFN, University of Cagliari, Italy)
    • Maizza G. (Politecnico di Torino, Italy)
    • Scarfone A.M. (ISC-CNR, Politecnico di Torino, Italy) - Chair
    • Spagnolo B. (University of Palermo, Italy)
    • Valenti D. (Università degli studi di Palermo, Italy)

    EPS-SNLP, European Physical Society Statistical and Nonlinear Physics Division

    • Beck C. (Queen Mary, University of London, UK) - Chair
    • Caldarelli G. (IMT School for Advanced Studies Lucca, Italy)
    • Cugliandolo L.F. (Université Pierre et Marie Curie, Paris, France)
    • Gudowska-Nowak E. (Jagiellonian University, Krakow, Poland)
    • Kantz H. (Max Planck Institute, Dresden, Germany)
    • Manneville P. (Ecole Polytechnique, Palaiseau, France)
    • Ruffo S. (SISSA, Trieste, Italy)
    • Toral R. (Inst. de Física Interdisc. y Sist. Compl., Palma de Mallorca, Spain)

    Abstract Submisision

    Registration and Payment
    Early registration (from 17 April to 13 May)
    Registration fee: 500€ (VAT included)
    EPS member registration fee: 450€ (VAT included)
    PhD Students: 250€ (VAT included)
    Regular registration (after 13 May)
    Registration fee: 600€ (VAT included)
    EPS member registration fee: 500€ (VAT included)
    PhD Students: 400€ (VAT included)
    The registration fee for the Participants includes: Admission to all scientific sessions, Conference Kit, Abstract Booklet, Coffee Breaks.

    Selected contributions (invited, oral and poster) can be invited to be submitted for publication in topical volumes of ISI quoted journals, devoted to the SigmaPhi2020 Conference.
    All the papers will undergo the standard referral process of the journals. The submission of a paper implies that it represents original work not previously published and not considered for publication elsewhere. Further details concerning journals, manuscript length and format will be given during the Conference.


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    Capture du 2020-02-16 12-48-33.png

    The 9th conference of Stochastic Geometry Days 2020, associated with the French GDR project GeoSto, will take place in Dunkerque from March 30th to April 03th. This conference provides the opportunity to explore the presentations on all probabilistic and statistical aspects of Stochastic Geometry and applications in various fields including: random geometric graphs, percolation, statistical mechanics, computational geometry, random walks, random fields, point processes and spatial statistics.

    As in the previous editions, the Stochastic Geometry Days consists of two days of mini-courses and three days of talks given by invited speakers (five contributed talks will be also given by participants who wish to give a talk).

    Speakers of the mini courses (30-31th of March, 6h per speaker)

    • François Baccelli (ENS Paris, INRIA) on the theme “Unimodularity”
    • Clément Dombry (Université Franche-Comté) on the theme “Max-stable processes”

    Invited speakers of the conference (01-03 April, 45min per speaker)

    • Rémi Bardenet (CNRS, Université de Lille)
    • Quentin Berger (Sorbonne Université)
    • Emilie Chautru (Mines ParisTech)
    • Nicolas Curien (Université Paris-Saclay)
    • Olivier Devillers (INRIA Nancy)
    • Matthieu Fradelizi (Université Paris-Est)
    • Alexandre Gaudillière (Université Aix-Marseille)
    • Jean-Baptiste Gouéré (Université de Tours)
    • Tom Hutchcroft (University of Cambridge)
    • Benedikt Jahnel (Weierstrass Institute for Applied Analysis and Stochastics, Berlin)
    • Sabine Jansen (Mathematical Institute of the University of Munich)
    • Wolfgang König (Weierstrass Institute for Applied Analysis and Stochastics, Berlin)
    • Irène Marcovici (Université de Lorraine)
    • Elena Pulvirenti (Institute for Applied Mathematics, Bonn)
    • Eliza O’Reilly (California Institute of Technology)
    • Thierry de la Rue (Université de Rouen)

    Some practical information
    Informations about the conference room, hotels, travel and more are available on the page “Useful Informations”.
    Registrations (free of charge for all participants) are open from January 15th to March 15th, 2020 (registration before February 28th for participants who wish to give a contributed talk).

    Social activities
    Walk in the wild sand dune on Tuesday, March 31;
    Coktail on Wednesday, April 1;
    Gala dinner offered to all the participants on Thursday, April 2.

    (under the coordination of Nicolas Chenavier).

    • Nicolas Chenavier (Université du Littoral Côte d’Opale)
    • David Coupier (Université Polytechnique Hauts-de-France)
    • Ahmad Darwiche (Université du Littoral Côte d’Opale)
    • David Dereudre (Université de Lille)
    • Romuald Ernst (Université du Littoral Côte d’Opale)
    • Lucas Flammant (Université Polytechnique Hauts-de-France)
    • Dominique Schneider (Université du Littoral Côte d’Opale)
    • Thibaut Vasseur (Université de Lille)

    With the support of
    GDR GeoSto; Université du Littoral Côte d’Opale (ULCO); laboratoires LMPA J. Liouville, LPP, LAMAV, Labex CEMPI; ERC NEMO; ANR ASPAG et PPPP; CNRS; Fédération de Recherche Mathématique des Hauts-de-France (FMHF, FR2037 CNRS); Région Hauts-de-France.

    Contact: sgd2020@listes.math.cnrs.fr

    Capture du 2020-02-16 12-54-53.png

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    Joint Structures and Common Foundations of Statistical Physics, Information Geometry and Inference for Learning (SP+IG'20)
    Date: 26th July to 31st July 2020
    Location: Ecole de Physique des Houches
    Ecole de Physique des Houches https://houches.univ-grenoble-alpes.fr/
    149 Chemin de la Côte, F-74310 Les Houches, France
    (+33/0) 4 57 04 10 40
    Download the poster in pdf

    Affiche Houches.png

    To submit a short paper or poster, please use the Easychair conference system:

    Scientific rationale:
    In the middle of the last century, Léon Brillouin in "The Science and The Theory of Information" or André Blanc-Lapierre in "Statistical Mechanics" forged the first links between the Theory of Information and Statistical Physics as precursors. In the context of Artificial Intelligence, machine learning algorithms use more and more methodological tools coming from the Physics or the Statistical Mechanics. The laws and principles that underpin this Physics can shed new light on the conceptual basis of Artificial Intelligence. Thus, the principles of Maximum Entropy, Minimum of Free Energy, Gibbs-Duhem's Thermodynamic Potentials and the generalization of François Massieu's notions of characteristic functions enrich the variational formalism of machine learning. Conversely, the pitfalls encountered by Artificial Intelligence to extend its application domains, question the foundations of Statistical Physics, such as the construction of stochastic gradient in large dimension, the generalization of the notions of Gibbs densities in spaces of more elaborate representation like data on homogeneous differential or symplectic manifolds, Lie groups, graphs, tensors, .... Sophisticated statistical models were introduced very early to deal with unsupervised learning tasks related to Ising-Potts models (the Ising-Potts model defines the interaction of spins arranged on a graph) of Statistical Physics. and more generally the Markov fields. The Ising models are associated with the theory of Mean Fields (study of systems with complex interactions through simplified models in which the action of the complete network on an actor is summarized by a single mean interaction in the sense of the mean field). The porosity between the two disciplines has been established since the birth of Artificial Intelligence with the use of Boltzmann machines and the problem of robust methods for calculating partition function. More recently, gradient algorithms for neural network learning use large-scale robust extensions of the natural gradient of Fisher-based Information Geometry (to ensure reparameterization invariance), and stochastic gradient based on the Langevin equation (to ensure regularization), or their coupling called "Natural Langevin Dynamics". Concomitantly, during the last fifty years, Statistical Physics has been the object of new geometrical formalizations (contact or symplectic geometry, ...) to try to give a new covariant formalization to the thermodynamics of dynamic systems. We can mention the extension of the symplectic models of Geometric Mechanics to Statistical Mechanics, or other developments such as Random Mechanics, Geometric Mechanics in its Stochastic version, Lie Groups Thermodynamic, and geometric modeling of phase transition phenomena. Finally, we refer to Computational Statistical Physics, which uses efficient numerical methods for large-scale sampling and multimodal probability measurements (sampling of Boltzmann-Gibbs measurements and calculations of free energy, metastable dynamics and rare events, ...) and the study of geometric integrators (Hamiltonian dynamics, symplectic integrators, ...) with good properties of covariances and stability (use of symmetries, preservation of invariants, ...). Machine learning inference processes are just beginning to adapt these new integration schemes and their remarkable stability properties to increasingly abstract data representation spaces. Artificial Intelligence currently uses only a very limited portion of the conceptual and methodological tools of Statistical Physics. The purpose of this conference is to encourage constructive dialogue around a common foundation, to allow the establishment of new principles and laws governing the two disciplines in a unified approach. But, it is also about exploring new « chemins de traverse ».


    • Frédéric Barbaresco, THALES, KTD PCC, Palaiseau, France
    • Silvère Bonnabel, Mines ParisTech, CAOR, Paris, France
    • François Gay-Balmaz, Ecole Normale Supérieure Ulm, CNRS & LMD, Paris, France
    • Patrick Iglesias-Zemmour, Université de Marseille, I2M, Marseille, France
    • Bernhard Maschke, Université Claude Bernard, LAGEPP, Lyon, France
    • Eric Moulines, Ecole Polytechnique, CMAP, Palaiseau, France
    • Frank Nielsen, Sony Computer Science Laboratories, Tokyo, Japan and Ecole Polytechnique, France
    • Gery de Saxcé, Université de Lille, LAM3, Lille, France

    posted in Joint Structures and Common Foundations of Statistical Physics Information Geometry and Inference for Learning (SP+IG'20)read more
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    3 weeks of conferences, courses and seminars at the Fields Institute for Research in Mathematical Sciences (Toronto canada) on "New Geometric Methods in Neuroscience".
    Registration for each event in the program is now available online. Please register for each event you wish to attend by purchasing the appropriate tickets.

    Workshops and Conferences

    • Introduction to Geometric Modelling for Neuroscience February 24 - 28, 2020
    • New Mathematical Methods for Neuroscience March 2 - 6, 2020
    • Research Seminars: Exploring the Geometry of Neuroscience March 9 - 13, 2020

    Register online

    Organizing Committee

    • Jeremie Lefebvre - Krembil Research Institute
    • Matilde Marcolli - University of Toronto
    • Florence Roullet - McMaster University
    • Doris Tsao - Caltech
    • Don Weaver - Krembil Research Institute, University Health Network


    1. Introduction to Geometric Modelling for Neuroscience February 24 - 28, 2020, The Fields Institute

    Location: Fields Institute, Room 230
    The minicourses will cover introductory material about modelling of the brain, the use of topological and geometric methods in neuroscience, and new categorical models of probability and information and of computing with potential applications to neuroscience.
    Friday February 28: Special lecture by Eugenia Cheng
    Minicourses speakers:

    • Carina Curto (Pennsylvania State University)
    • Daniela Eg​as Santander (École Polytechnique Fédérale de Lausanne)
    • Lisbeth Fajstrup (Aalborg University)
    • Tobias Fritz (Perimeter Institute)
    • Sara Kalisnik (Wesleyan University)
    • Florence Roullet (McMaster University)
    • Giovanna Citti (Università di Bologna)
    • Lyle Muller (University of Western Ontario)

    Other participants

    • Britt Anderson (University of Waterloo)
    • Farzad Fathizadeh (Swansea University)
    • Lior Pachter (Caltech)
    • Sivabal Sivaloganathan (University of Waterloo)
    • Nora Youngs (Colby College)

    2. New Mathematical Methods for Neuroscience March 2 - 6, 2020, The Fields Institute

    Location: Fields Institute, Room 230
    The workshop will bring together neuroscientists and mathematicians from different disciplines, with the purpose of discussing the use of new mathematicalmethods based on geometry, algebraic topology, and category theory, to problems of modelling and of data analysis in neuroscience. These new mathematical approaches can complement and enrich the more traditional use of dynamical systems and differential equations, in modelling the brain at scaleslarger than single neurons, in terms of connectivity, functionality, neural coding and information flows.
    Workshop speakers:

    • Britt Anderson, University of Waterloo
    • Carina Curto, Pennsylvania State University
    • Jorn Diedrichsen, University of Western Ontario
    • Lisbeth Fajstrup, Aalborg University
    • Farzad Fathizadeh, Swansea University
    • Chad Giusti, University of Delaware
    • Vladimir Itskov, Pennsylvania State University
    • Matilde Marcolli, University of Toronto
    • Lyle Muller, University of Western Ontario
    • Tevin Rouse, Carnegie Mellon
    • Alexander Ruys de Perez, Texas A&M University
    • Doris Tsao, Caltech
    • Hugh Wilson, York University
    • Anqi Wu, Columbia University
    • Dan Yamins, Stanford University
    • Lai-Sang Young, Courant Institute NYU
    • Nora Youngs, Colby College

    3. Research Seminars: Exploring the Geometry of Neuroscience March 9 - 13, 2020, The Fields Institute

    Location: Fields Institute, Room 230
    Daily seminar and discussion sessions meetings will take place, organized around specific topics, including brain modelling of healthy and clinical brain, geometric modelling of the visual cortex with contact and symplectic geometry, information measures and their use in neuroscience modelling, persistent topology of neuroscience data. These seminars are structured more in the style of informal discussions and presentations that focus more on ideas than on polished details and already established results. They are aimed at developing new interactions between geometry and neuroscience and creating opportunity for new collaborations to develop among the participants, by identifying specific questions and projects.
    The activities of this week will be organized around daily "research seminars", accompaniedby one or two main lectures.
    The seminar activities will consist of shorter informal presentations, as well as discussion sessions, round tables and other such open interaction opportunities. The names listed under each daily seminar theme will be responsible for running the activity, while all people attending the event are strongly encouraged to participate in all of these seminars.
    Seminar speakers:

    • Britt Anderson, University of Waterloo
    • Pierre Baudot, Median Technologies
    • Florence Roullette, McMaster University
    • Alessandro Sarti, École des hautes études en sciences sociales
    • Sivabal Sivaloganathan, University of Waterloo
    • Ann Sizemore Blevins, University of Pennsylvania
    • Frances Skinner, Krembil Research Institute
    • Dan Yamins, Stanford University
    • Lai-Sang Young, Courant Institute NYU
    • Nora Youngs, Colby College

    posted in Focus Program on New Geometric Methods in Neuroscience read more
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    Keynote Speaker:

    • Jean-Baptiste Hiriart-Urruty: Pierre de FERMAT (ca. 1605-1665): lawyer, philologist and illustrious mathematician ... but enigmatic

    posted in GSI2019 read more
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