Re:[ML-news] Digest for ml-news@googlegroups.com - 6 Messages in 6 Topics

[Derry Fitzgerald]

ml-...@googlegroups.com wrote:

>=============================================================================
>Today's Topic Summary
>=============================================================================
>
>Group: ml-...@googlegroups.com
>Url: http://groups.google.com/group/ml-news/topics
>
>  - ILP 2013 Call For Papers [1 Update]
>    http://groups.google.com/group/ml-news/t/82fef5ce9ed3f8ac
>  - Call for Papers ISACS 2013: 6th International Symposium on Attention in Cognitive Systems [1 Update]
>    http://groups.google.com/group/ml-news/t/e963180af6e24c5b
>  - CFP: ICML 2013 Workshop on Inferning: Interactions between Inference and Learning [1 Update]
>    http://groups.google.com/group/ml-news/t/d3fa9a29ec9c6f1e
>  - CFP: ICML 2013 Workshop on Prediction with Sequential Models [1 Update]
>    http://groups.google.com/group/ml-news/t/8569ffe783ba314e
>  - Call for BPDM Social Media Coordinator [1 Update]
>    http://groups.google.com/group/ml-news/t/182b00b93d6e5ba2
>  - Engineering for Health International Summer School Paris-Saclay [1 Update]
>    http://groups.google.com/group/ml-news/t/554050d6fec2037d
>
>
>=============================================================================
>Topic: ILP 2013 Call For Papers
>Url: http://groups.google.com/group/ml-news/t/82fef5ce9ed3f8ac
>=============================================================================
>
>---------- 1 of 1 ----------
>From: gerson zaverucha <ger...@cos.ufrj.br>
>Date: Feb 26 05:38PM -0300
>Url: http://groups.google.com/group/ml-news/msg/aacb3bf8c338c76a
>
>CFP - ILP 2013 - The 23rd International Conference on Inductive Logic
>Programming
>
>Rio de Janeiro, Brazil, August 28-30, 2013
>
> ilp13.cos.ufrj.br
>
>KEY DATES:
>
>25/04 : Abstracts of long papers due
>
>30/04 : Long papers due
>
>02/06 : Notification for long papers
>
>21/06: Short/published papers due
>
>28/06 : Notification for short/published papers
>
>
>INVITED SPEAKERS:
>
>Hendrik Blockeel -Katolieke Universiteit Leuven, Belgium
>
>Willian Cohen - Carnegie Mellon University, USA
>
>Jure Leskovesc - Stanford University, USA
>
>
>CALL FOR PAPERS
>
>The ILP conference series, started in 1991, is the premier international
>forum on learning from structured relational data. Originally focused on
>the induction of logic programs, it has broadened its scope and attracted a
>lot of attention and interest in recent years. Authors are invited to
>submit papers presenting original results on all aspects of learning in
>logic, multi-relational learning and data mining, statistical relational
>learning, graph and tree mining, relational reinforcement learning,
>and learning
>in other (non-propositional) logic-based knowledge representation
>frameworks.
>
>Typical, but not exclusive, topics of interest for submissions include:
>
>- Theoretical aspects: learning scenarios, data/model representation
>frameworks, their computational and/or statistical properties, etc.
>
>- Algorithms: probabilistic and statistical approaches, distance and
>kernel-based methods, learning with (semi)structured data, supervised,
>unsupervised, and semi-supervised relational learning, relational
>reinforcement learning, inductive databases, link discovery, new
>propositionalization approaches, multi-instance learning, predicate
>invention, logical and probabilistic inference, uncertainty reasoning.
>
>- Representations and languages for logic-based learning: including
>datalog, first-order logic, description logics and ontologies, higher-order
>logic, probabilistic logical representations, mapping between alternative
>representations.
>
>-  Systems: systems that implement inductive logic programming algorithms
>with special emphasis on issues like optimization, parallelism, efficiency
>and scalability.
>
>- applications including, but not restricted to multi-relational learning
>from structured (e.g., labeled graphs, tree patterns) and semi-structured
>data (e.g., XML documents), learning from relational data in areas of
>science (bioinformatics, cheminformatics, medical informatics, etc.),
>natural language processing (computational linguistics, text and web mining
>etc.), engineering, games, semantic web, the arts, etc.
>
>We solicit three kinds of papers:
>
>1) Long papers describing original mature work containing appropriate
>experimental evaluation and/or representing a self-contained theoretical
>contribution. Long papers will be reviewed by at least 3 members of the
>program committee. Authors will be notified prior to the conference on
>acceptance/rejection for the Springer LNAI post-conference proceedings.
>Authors of accepted papers will be assigned a standard time slot for
>presentation.
>
>2) Short papers describing original work in progress, brief accounts of
>original ideas without conclusive experimental evaluation, and other
>relevant work of potentially high scientific interest but not yet
>qualifying for the long paper category. The PC chairs will accept/ reject
>short papers on the grounds of relevance. Authors of accepted short
>paperswill be assigned a reduced time slot for presentation. Each
>short
>paper will be reviewed by at least 3 members of the program committee on
>the basis of both the manuscript and its presentation, and the authors of
>selected papers will be invited to submit a long version for the Springer
>LNAI post-conference proceedings; in this case, the long paper will be
>reviewed again by the assigned PC members of the short paper and be finally
>accepted if satisfactorily addressing the reviewer's
>
>requirements.
>
>3) Papers relevant to the conference topics and recently published or
>accepted for publication by a first-class conference such as ECML/ PKDD,
>ICML, KDD, ICDM, AAAI, IJCAI,  etc. or journal such as MLJ, DMKD, JMLR etc.
>The PC chairs will accept/reject such  papers on the grounds of relevance
>and quality of the original publication venue. Authors of accepted
>paperswill be assigned a reduced time slot for presentation. These
>papers will not appear in the Springer LNAI post-conference proceedings.
>
>Submissions in category 1 or 2 must not have been published or be under
>review for a journal or for another conference with published proceedings.
>They should be submitted in the Springer LNCS format. Long (short)
>papersmust not exceed 12 (6) pages.
>Papers in category 3 should be submitted in their original format and the
>authors should indicate the original publication venue.
>
>A special issue of the Machine Learning journal is planned following the
>conference, with papers selected by the PC from all the three categories
>above, significantly revised and extended to meet the MLJ criteria, and
>re-reviewed by the PC.
>
>Program Chairs
>
>Gerson Zaverucha, UFRJ, Brazil
>
>Vítor Santos Costa, UP, Portugal
>
>Local Chair
>
>Aline Paes, UFRJ, Brazil
>
>
>
>=============================================================================
>Topic: Call for Papers ISACS 2013: 6th International Symposium on Attention in Cognitive Systems
>Url: http://groups.google.com/group/ml-news/t/e963180af6e24c5b
>=============================================================================
>
>---------- 1 of 1 ----------
>From: Bjoern Schuller <schu...@tum.de>
>Date: Feb 27 11:57PM
>Url: http://groups.google.com/group/ml-news/msg/88062e53c8d4f2f8
>
>Dear List,
>
>Please find below a
>
>
>Call for Papers
>_________________________________________________________
>
>ISACS 2013
>6th International Symposium on Attention in Cognitive Systems
>
>In conjunction with the
>International Joint Conference on Artificial Intelligence (IJCAI 2013)
>
>Beijing, China
>August 3-5, 2013
>
>http://isacs2013.joanneum.at
>_________________________________________________________
>
>
>
>Dates
>_________________________________________________________
>
>Full Paper Submission: April 20, 2013
>Acceptance Notification: May 20, 2013
>Final Paper Submission: May 30, 2013
>Workshop Day:  August 3-5, 2013, TBD
>
>
>The Symposium
>_________________________________________________________
>
>The capacity to attend to the relevant has been part of Artificial Intelligence (AI)
>systems since the early days of the discipline. Currently, with respect to the design and
>computational modelling of artificial cognitive systems, selective attention has again
>become a focus of research, and one sees it important for the organization of
>behaviours, for control and interfacing between sensory and cognitive information
>processing, and for the understanding of individual and social cognition in humanoid
>artefacts. One may consider selective attention as part of the core of artificial cognitive
>systems. Within the context of the engineering domain, the development of enabling
>technologies such as autonomous robotic systems, miniaturized mobile - even wearable
>- sensors, and ambient intelligence systems involves the real-time analysis of enormous
>quantities of data. These data have to be processed in an intelligent way to provide "on
>time delivery" of the required relevant information. Knowledge has to be applied about
>what needs to be attended to, and when, and what to do in a meaningful sequence, in
>correspondence with visual feedback.
>
>Suggested symposium topics include, but are not limited to:
>
>* Computational architectures for attention
>* Modelling of visual and auditory attention
>* Biologically inspired attention
>* Attention in robotic / mobile / wearable systems
>* Aspects of attention in cognitive psychology, neuroscience, and philosophy
>* Attention and control of machine vision processes
>* Performance measures for attention enabled artificial systems
>* Applications of machine attention
>
>
>Objectives
>_________________________________________________________
>
>The goal of this symposium is to provide an international forum to examine
>computational methods of attention in cognitive systems from an interdisciplinary
>viewpoint, with the focus on computer vision in relation to robotics, psychology, and
>neuroscience.
>
>
>Organisers
>_________________________________________________________
>
>Lucas Paletta  Joanneum Research, Graz, Austria  lucas....@joanneum.at
>Laurent Itti  University of Southern California, CA, USA it...@pollux.usc.edu
>Björn Schuller  Technische Universität München, Germany  schu...@tum.de
>Fang Fang    Peking University, China  xr...@pku.edu.cn<mailto:xr...@pku.edu.cn>
>
>
>Program Committee
>_________________________________________________________
>
>Minoru Asada  University of Osaka, Japan
>Christian Balkenius  Lund University, Sweden
>Anna Belardinelli  University of Tübingen, Germany
>Ali Borji  University of Southern California, CA, USA
>James J. Clark  McGill University, Toronto, Canada
>Ralf Engbert  University of Potsdam, Germany
>Fang Fang    Peking University, China
>Simone Frintrop  University of Bonn, Germany
>Horst-Michael Gross  Tech. Univ. Ilmenau, Germany
>Dietmar Heinke  University of Birmingham, UK
>Laurent Itti University of Southern California, CA; USA
>Ilona Kovacs  Université Paris Descartes, France
>Eileen Kowler   Rutgers University, NJ, USA
>Minho Lee   Kyungpook National Univ., South Korea
>Michael Lindenbaum   Technion, IIT, Israel
>David Melcher  University of Trento, Italy
>Giorgio Metta  Italian Institute of Technology, Italy
>Lucas Paletta   Joanneum Research, Austria
>Fiora Pirri  University of Rome, La Sapienza, Italy
>Ron Rensink  Univ. of British Columbia, BC, Canada
>Erich Rome   Fraunhofer IAIS, Germany
>Albert Rothenstein   York University, Toronto, Canada
>Björn Schuller   Techn. Univ. München, Germany
>Jochen Triesch   Frankfurt IAS, Germany
>Yizhou Wang   Peking University, China
>Hezy Yeshurun  University of Tel Aviv, Israel
>Chen Yu  University of Indiana, IN, USA
>
>
>Authors
>_________________________________________________________
>
>Between  10  (short) and  14  (long) pages Springer  -
>style blind paper submission is handled via easychair.
>Springer LNAI publication in terms of post-conference
>proceedings of selected, revised and invited papers.
>The ISACS Best Paper Award, funded by SMI, will be
>given to the best paper submission.
>
>
>
>Thank you for excusing cross-postings.
>
>
>
>___________________________________________
>PD Dr.-Ing. habil. DI
>Björn W. Schuller
>
>Visiting Professor
>Centre Interfacultaire en Sciences Affectives (CISA)
>Université de Genève
>7 rue des Battoirs - CH -1205 Genève
>Switzerland
>Head Machine Intelligence & Signal Processing Group
>Institute for Human-Machine Communication
>Technische Universität München
>Munich / Germany
>
>Visiting Professor
>School of Computer Science and Technology
>Harbin Institute of Technology
>Harbin / P.R. China
>schu...@tum.de
>www.mmk.ei.tum.de/~sch
>___________________________________________
>
>
>
>=============================================================================
>Topic: CFP: ICML 2013 Workshop on Inferning: Interactions between Inference and Learning
>Url: http://groups.google.com/group/ml-news/t/d3fa9a29ec9c6f1e
>=============================================================================
>
>---------- 1 of 1 ----------
>From: Ruslan Salakhutdinov <rsal...@cs.toronto.edu>
>Date: Feb 26 11:43PM -0500
>Url: http://groups.google.com/group/ml-news/msg/bd9fa5286cdc6ad1
>
>Call for Papers
>
>ICML 2013 Workshop on Inferning: Interactions between Inference and
>Learning
>
>http://inferning.cs.umass.edu
>infern...@gmail.com
>
>Important Dates:
>Submission Deadline: Mar 30th, 2013 (11:59pm PST)
>Author Notification: April 21st, 2013
>Workshop: June 20-21, 2013, Atlanta, GA
>
>
>There are strong interactions between learning algorithms which estimate
>the parameters of a model from data, and inference algorithms which use a
>model to make predictions about data. Understanding the intricacies of
>these interactions is crucial for advancing the state-of-the-art on
>real-world tasks in natural language processing, computer vision,
>computation biology, etc. Yet, many facets of these interactions remain
>unknown. In this workshop, we study the interactions between inference and
>learning using two reciprocating perspectives.
>
>Perspective one: how does inference affect learning? The first perspective
>studies the influence of the choice of inference technique during learning
>on the resulting model. When faced with models for which exact inference
>is intractable, efficient approximate inference techniques may be used,
>such as MCMC sampling, stochastic approximation, belief propagation,
>beam-search, dual decomposition, etc. The workshop will focus on work that
>evaluates the impact of the approximations on the resulting parameters, in
>terms of both the generalization of the model, the effect it has on the
>objective functions, and the convergence properties. We will also study
>approaches that attempt to correct for the approximations in inference by
>modifying the objective and/or the learning algorithm (for example,
>contrastive divergence for deep architectures), and approaches that
>minimize the dependence on the inference algorithms by exploring
>inference-free methods (e.g., piece-wise training, pseudo-max and
>decomposed learning).
>
>Perspective two: how does learning affect inference? Traditionally, the
>goal of learning has been to find a model for which prediction (i.e.,
>inference) accuracy is as high as possible. However, an increasing
>emphasis on modeling complexity has shifted the goal of learning: find
>models for which prediction (i.e., inference) is as efficient as possible.
>Thus, there has been recent interest in more unconventional approaches to
>learning that combine generalization accuracy with other desiderata such
>as faster inference. Some examples of this kind are: learning classifiers
>for greedy inference (e.g., Searn, Dagger); structured cascade models that
>learn a cost function to perform multiple runs of inference from coarse to
>fine level of abstraction by trading-off accuracy and efficiency at each
>level; learning cost function to search in the space of complete outputs
>(e.g., SampleRank, search in Limited Discrepancy Search space); learning
>structures that exhibit efficient exact inference etc. Similarly, there
>has been work that learns operators for efficient search-based inference,
>approaches that trade-off speed and accuracy by incorporating resource
>constraints such as run-time and memory into the learning objective.
>
>
>This workshop brings together practitioners from different fields
>(information extraction, machine vision, natural language processing,
>computational biology, etc.) in order to study a unified framework for
>understanding and formalizing the interactions between learning and
>inference. The following is a partial list of relevant keywords for the
>workshop:
>
>* learning with approximate inference
>* cost-aware learning
>* learning sparse structures
>* pseudo-likelihood, composite likelihood training
>* contrastive divergence
>* piece-wise and decomposed training
>* decomposed learning
>* coarse to fine learning and inference
>* score matching
>* stochastic approximation
>* incremental gradient methods
>* adaptive proposal distributions
>* learning for anytime inference
>* learning approaches that trade-off speed and accuracy
>* learning to speed up inference
>* learning structures that exhibit efficient exact inference
>* lifted inference for first-order models
>* more ...
>
>New benchmark problems: This line of research can hugely benefit from new
>challenge problems from various fields (e.g., computer vision, natural
>language processing, speech, computational biology, computational
>sustainability, etc.). Therefore, we especially request relevant papers
>describing such problems, main challenges, evaluations and public data
>sets.
>
>
>Invited Speakers:
>
>Dan Roth, University of Illinois, Urbana-Champaign
>Rina Dechter, University of California, Irvine
>Ben Taskar, University of Washington
>Hal Daume, University of Maryland, College Park
>Alan Fern, Oregon State University
>
>
>Important Dates:
>
>Submission Deadline: Mar 30th, 2013 (11:59pm PST)
>Author Notification: April 21st, 2013
>Workshop: June 20-21, 2013
>
>
>Author Guidelines:
>
>Submissions are encouraged as extended abstracts of ongoing research. The
>recommended page length is 4-6 pages. Additional supplementary content may
>be included, but may not be considered during the review process.
>Previously published or currently in submission papers are also encouraged
>(we will confirm with authors before publishing the papers online).
>
>The format of the submissions should follow the ICML 2013 style, available
>here:
>http://icml.cc/2013/wp-content/uploads/2012/12/icml2013stylefiles.tar.gz
>However, since the review process is not double-blind, submissions need
>not be anonymized and author names may be included.
>
>Submission site: https://www.easychair.org/conferences/?conf=inferning2013
>
>
>Organizers:
>
>Janardhan Rao (Jana) Doppa, Oregon State University
>Pawan Kumar, Ecole Centrale Paris
>Michael Wick, University of Massachusetts, Amherst
>Sameer Singh, University of Massachusetts, Amherst
>Ruslan Salakhutdinov, University of Toronto
>
>
>
>=============================================================================
>Topic: CFP: ICML 2013 Workshop on Prediction with Sequential Models
>Url: http://groups.google.com/group/ml-news/t/8569ffe783ba314e
>=============================================================================
>
>---------- 1 of 1 ----------
>From: Gabriel Dulac-Arnold <ga...@squirrelsoup.net>
>Date: Feb 28 12:11AM +0100
>Url: http://groups.google.com/group/ml-news/msg/b71f722fb49865de
>
>===========================================
>ICML '13 Workshop - Call for papers:
>*Prediction with Sequential Models *
>June 21st or 22nd, Atlanta, GA, USA
>*** Website: http://psm.lal.in2p3.fr/ ***
>
>===========================================
>
>Supervised and unsupervised function learning is a vast domain with a
>plethora of standard algorithmic solutions. Most of these methods learn
>a monolithic predictor function in the sense that each test instance is
>processed in a single-step, atomic process. In contrast, some recent
>studies have proposed a different paradigm in which **prediction is
>reformulated as a sequential decision process* *and**learning the
>predictor function corresponds to solving a dynamic control problem**.
>These new approaches bridge "classical" supervised and unsupervised
>learning problems with the fields of control theory and reinforcement
>learning (RL),**and *raise interesting questions on different domains
>ranging from reinforcement learning to representation learning*.
>
>
>
>This workshop aims at gathering the *various machine learning
>sub-communities* that have worked around the subject and discuss the
>aforementioned issues. The topics of interest include, but are not
>limited to:
>. Generic topics:
>*-- Classification, ranking,
>-- Budgeted and/or cost-sensitive classification
>-- Structured prediction
>-- Sparse coding with sequential models
>-- Feature selection. *
>. Reinforcement learning applied to learning sequential functions:
>*-- RL with many discrete actions
>-- RL in high-dimensional spaces
>-- Inverse RL *
>. Applications:
>*-- Real-time detection and classification **
>-- Text/image classification and information extraction
>-- Trigger design in high-energy particle physics
>-- Web-page ranking
>-- Medical diagnosis
>*
>
>
>      Program Committee
>
>  * Francis Bach -- Laboratoire d'Informatique de l'Ecole Normale
>    Superieure -- INRIA-Sierra - France
>  * Aaron Courville -- University of Montreal -- Canada
>  * Jason Eisner -- Johns Hopkins University -- USA
>  * Damien Ernst -- University of Liege -- Belgium
>  * Hugo Larochelle -- University of Sherbrooke -- Canada
>  * Francis Maes -- Catholic University of Leuven -- Belgium
>  * Rémi Munos -- INRIA Sequel -- France
>  * Philippe Preux -- University Lille 3 -- INRIA Sequel -- France
>  * Thomas Rückstieß -- Technische Universitat Munchen -- Germany
>  * Csaba Szepesvári -- University of Alberta -- Canada
>  * Kilian Weinberger -- Washington University -- USA
>
>
>
>*Organizers*
>
>  * Djalel Benbouzid (Paris Sud - CNRS)
>  * Ludovic Denoyer (UPMC - LIP6)
>  * Gabriel Dulac-Arnold (UPMC - LIP6)
>  * Patrick Gallinari (UPMC - LIP6)
>  * Balàzs Kégl (Paris Sud - CNRS)
>  * Michèle Sébag (Paris Sud - CNRS)
>
>
>
>=============================================================================
>Topic: Call for BPDM Social Media Coordinator
>Url: http://groups.google.com/group/ml-news/t/182b00b93d6e5ba2
>=============================================================================
>
>---------- 1 of 1 ----------
>From: Caio <soar...@gmail.com>
>Date: Feb 27 11:07PM -0800
>Url: http://groups.google.com/group/ml-news/msg/85e3f74011635179
>
>Please excuse duplicate postings and kindly distribute to department postings, listservs of underrepresented groups in STEM, and listservs of data mining, machine learning, pattern recognition, and information retrieval.
>
>Best,
>Caio Soares
>
>
>
>Greetings,
>
>Organization is well under way for the 2013 Broadening Participation in Data Mining program (BPDM).  This year many changes are taking place.  Most notably, 2013 will begin our new collaboration with the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), instead of SDM.  This year's KDD conference will be hosted in August in Chicago, IL and our program will span two days.  You can find more information at http://dataminingshop.com/.
>
>Another notable change will be our presence and use of social media to support the program.  So we are in search of our first Social Media Coordinator.  This individual will have the chance to greatly shape pre, post, and in-time social media interaction for the program.  Responsibilities are listed below.  The coordinator's program and conference expenses will also be covered.  If you're interested in becoming our Social Media Coordinator we ask you to apply by Mar. 1 to BPDMp...@gmail.com, including the requested application material.
>
>Application (submit in PDF format):
>-CV (including your Twitter handle and LinkedIn address, if available)
>-2-page max summary statement which answers the following:
>  *Describe your experience with social media (creation, development, and maintenance of accounts)
>  *How do you hope to shape social media for the BPDM program?
>  *What ideas do you hope to bring for integrating social media between BPDM and KDD/participants/sponsors/etc?
>
>Responsibilities:
>-Maintain existing BPDM Facebook, Twitter, and LinkedIn accounts (plus any other social media account you start)
>  *Ensure content posted by members is appropriate
>  *Post updates throughout the year
>-Take part in monthly or bi-monthly BPDM committee meetings
>-Attend the 2013 BPDM program and KDD Conference in Chicago, IL in August, 2013
>
>
>Email your complete application to BPDMp...@gmail.com, deadline is Mar. 1.
>
>Email any questions or concerns to the same address.  Notification will be made by March 17.  We hope you'll consider this opportunity and apply!
>
>Best,
>
>Caio Soares and Brandeis Marshall
>
>
>
>=============================================================================
>Topic: Engineering for Health International Summer School Paris-Saclay
>Url: http://groups.google.com/group/ml-news/t/554050d6fec2037d
>=============================================================================
>
>---------- 1 of 1 ----------
>From: CSIS Paris Saclay <l.mi...@gmail.com>
>Date: Feb 27 01:51AM -0800
>Url: http://groups.google.com/group/ml-news/msg/83a7a180e3372e94
>
>The new annual Engineering for Health International Summer School Paris-Saclay (ISS Paris-Saclay) will take place from May 24th to July 6th, 2013 in Paris-Saclay.
>http://www.summerschool-campus-paris-saclay.fr
>Please forward this information to all potentially interested people.
>
>The ISS Paris-Saclay is organized by:
>- Ecole Centrale Paris,
>- Ecole normale supérieure de Cachan,
>- Supélec,
>- Université Paris-Sud,
>in the framework of Université Paris-Saclay.
>
>It offers undergraduate students to spend 3 to 6 weeks in Paris.
>
>TOPICS COVERED:
>- Imaging
>- Reasoning
>- Sensing
>- Bioinformatics
>
>PROGRAM DATES:
>Session 1: From May 24th to June 14th, 2013
>Session 2: From June 14th to July 6th, 2013
>Session 3: From May 24th to July 6th, 2013
>
>The application deadline is March 31st.
>
>ACADEMIC COMMITTEE:
>Abdul BARAKAT - Ecole Polytechnique
>Alain DENISE (*) - Université Paris-Sud
>Olivier FRANCAIS (*) - Ecole Normale Supérieure Cachan
>Pierre-Yves JOUBERT – Université Paris-Sud
>Isabelle LEDOUX-RAK – Ecole Normale Supérieure Cachan
>Bruno LE PIOUFLE – Ecole Normale Supérieure Cachan
>Nikos PARAGIOS (*) - Ecole Centrale Paris
>Marie POIRIER-QUINOT (*) - Université Paris-Sud
>Thomas RODET – Supélec
>Arthur TENENHAUS - Supélec
>Joseph ZYSS – Ecole Normale Supérieure Cachan
>
>INSTRUCTORS:
>Charles BAROUD - Ecole Polytechnique
>Julie BERNAUER – École Polytechnique, INRIA Saclay
>Matthew BLASCHKO – Ecole Centrale Paris, INRIA Saclay
>Malcolm BUCKLE – Ecole Normale Supérieure Cachan, CNRS
>Alain DENISE (*) - Université Paris-Sud
>Olivier FRANCAIS (*) - Ecole Normale Supérieure Cachan
>Alexandre GRAMFORT – Telecom ParisTech
>Vincent GUILLEMOT – CEA Neurospin
>Akli HAMMADI – CEA INSTN
>Pawan KUMAR – Ecole Centrale Paris, INRIA Saclay
>Benoît LARRAT – CEA Neurospin
>Bruno LE PIOUFLE - Ecole Normale Supérieure Cachan
>Claude NOGUES – Ecole Normale Supérieure Cachan, CNRS
>Nikos PARAGIOS (*) - Ecole Centrale Paris
>Marie POIRIER-QUINOT (*) - Université Paris-Sud
>Monika REBISZ-POMORSKA – CEA INSTN
>Claire SMADJA – Université Paris-Sud
>Arthur TENENHAUS – Supélec
>Bertrand THIRION – CEA, INRIA Saclay
>Nicolas VAYATIS – Ecole Normale Supérieure Cachan
>
>(*)  Program leader
>
>http://www.summerschool-campus-paris-saclay.fr
>
>
>
>
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