Computational vision is one of the most challenging research domains in engineering sciences. The aim is to reproduce human visual perception through intelligent processing of visual data. The application domains span from computer aided diagnosis to industrial automation & robotics. The most common mathematical formulation to address such a challenge is through mathematical modeling. In such a context, first the solution of the desired vision task is expressed in the form of a parameterized mathematical model. Given such a model, the next task consists of associating the model parameters with the available observations, which is often called the model-to-data association. The aim of this task is to determine the impact of a parameter choice to the observations and eventually maximize/minimize the adequacy of these parameters with the visual observations. In simple words, the better the solution is, the better it will be able to express and fit the data. This is often achieved through the definition of an objective function on the parametric space of the model. Last, but not least given the definition of the objective function, visual perception is addressed through its optimization with respect to the model parameters. To summarize, computation visual perception involves three aspects, a task-specific definition of a parametric model, a data-specific association of this model with the available observations and last the optimization of the model parameters given the objective and the observations.

Such a chain processing inherits important shortcomings. The curse of dimensionality is often used to express the importance of the model complexity. In simple words, the higher the complexity of the model is, the better its expressive power will be with counter effect the increase of the difficulty of the inference process. Non-linearity is another issue to be addressed which simply states that the association between the model and the data is a (highly) non-linear function and therefore direct inference is almost infeasible. The impact of this aspect is enforced from the curse of non-convexity that characterizes the objective function. Often it lives in high-dimensional spaces and is ill posed making exact inference problematic (in many cases not possible) and computationally expensive. Last, but not least modularity and scalability is another important concern to be addressed in the context of computational vision. The use of task-specific modeling and algorithmic solutions make their portability infeasible and therefore transfer of knowledge from one task to another is not straightforward while the methods do not always scale well with respect either to the dimensionality of the representation or the data.

GALENaims at proposing innovative techniques towards automatic structuring, interpretation and longitudinal modeling of visual data. In order to address these fundamental problems of computational perception, GALEN investigates the use of discrete models of varying complexity. These methods exhibit an important number of strengths such as their ability to be modular with respect to the input measurements (clinical data), the nature of the model (certain constraints are imposed from computational perspective in terms of the level of interactions), and the model-to-data association while being computational efficient.

**ICCV Participation**: GALEN has participated in the 2011 International Conference in Computer Vision (ICCV'11) conference, the most selective conference in the field of computer vision
and medical image analysis with five papers (acceptance rate %20).

**CVPR Participation**: GALEN has participated in the 2011 annual IEEE Conference in Computer Vision and Pattern Recognition (CVPR'11) conference, the leading event in the field of
computer vision and medical image analysis with five papers (double blind full submissions, acceptance rate %25) including one oral presentation (out of a 60).

**MICCAI Participation**: GALEN has participated in the 2011 annual Medical Image Computing and Computer Assisted Intervention (MICCAI'11) conference one of the leading events in the
field of medical image analysis with four (double blind full submissions, acceptance rate %30).

**ISBI Participation**: GALEN has participated in the 2011 International Symposium of Biomedical Imaging (ISBI'11) conference, one of the notable events in the field of medical image
analysis with four papers (acceptance rate %40) including three oral presentations.

**IEEE Fellow & BMVC Plenary Speaker**: N. Paragios was promoted to the IEEE Fellow grade and was one of the plenary speakers of the 22
^{nd}edition of the British Machine Vision Conference.

A wide variety of tasks in medical image analysis can be formulated as discrete labeling problems. In very simple terms, a discrete optimization problem can be stated as follows: we are
given a discrete set of variables
*singleton*potential function
*pairwise*potential function

Our goal is then to choose a labeling which will allow us to pay the smallest total price. In other words, based on what we have mentioned above, we want to choose a labeling that minimizes the sum of all the MRF potentials, or equivalently the MRF energy. This amounts to solving the following optimization problem:

The use of such a model can describe a number of challenging problems in medical image analysis. However these simplistic models can only account for simple interactions
between variables, a rather constrained scenario for high-level medical imaging perception tasks. One can augment the expression power of this model through higher order interactions between
variables, or a number of cliques

where

The foundation of statistical inference is to learn a function that minimizes the expected loss of a prediction with respect to some unknown distribution

where

A key problem is that the distribution

which makes an implicit assumption that the training samples

where

Equation (
) is very well studied in classical statistics for the case that the output,

Many tasks in artificial intelligence are solved by building a model whose parameters encode the prior domain knowledge and the likelihood of the observed data. In order to use such models
in practice, we need to estimate its parameters automatically using training data. The most prevalent paradigm of parameter estimation is supervised learning, which requires the collection of
the inputs

In order to address the deficiencies of supervised learning, researchers have started to focus on the problem of parameter estimation with data that contains hidden variables. The hidden
variables model the missing information in the annotations. Obtaining such data is practically more feasible: image-level labels (`contains car',`does not contain person') instead of tight
bounding boxes; partial segmentation of medical images. Formally, the parameters
**w**of the model are learned by minimizing the following objective:

Here,

Previous attempts at minimizing the above objective function treat all the training samples equally. This is in stark contrast to how a child learns: first focus on easy samples (`learn to
add two natural numbers') before moving on to more complex samples (`learn to add two complex numbers'). In our work, we capture this intuition using a novel, iterative algorithm called
self-paced learning (
spl). At an iteration

Here, samples with

Here,
**w**. The use of a more general loss function will allow us to better exploit the freely available data with missing information. For example, consider the case where

**Large Scale Urban Modeling**: The use of satellite imaging along with range data towards large scale image-driven reconstruction. The aim is to produce scalable representations of 3D
models that are compact, modular and able to provide realistic 3D representations of real visual data.

**Objet Recognition**: The use annotated data-bases towards learning class-specific visual and geometric object characteristics to perform recognition.

**MR & Muscular Diseases**: The use of MR and Diffusion Tensor Imaging are investigated in collaboration with the Henri Mondor University Hospital and Institut of Myology towards
automatic quantification of muscular mass loss and non-invassive biopsy. The aim is to provide tools that could be used to automatically analyze MR imaging and extract useful clinical
measurements (Insitut of Myology), and assess the potential impact of diffusion tensor imaging towards automatic quantification either of muscular diseases progression.

**MR Brain Imaging towards Low-Gliomas Tumor Brain Understanding**: The use of contrast enhanced imaging is investigated in collaboration with the Montpellier University Hospital towards
better understanding of low-gliomas positioning, automatic tumor segmentation/identification and longitudinal (tumor) growth modeling.

Dropis a deformable registration platform in C++ for the medical imaging community (publicly available at
http://

FastPDis an optimization platform in C++ for the computer vision and medical imaging community (publicly available at
http://

GraPeSis a generic image parsing library based on re-inforcement learning. It can handle grammars (binary-split, four-color, Hausmannian) and image-based rewards (Gaussian mixtures, Randomized Forests) of varying complexity while being modular and computationally efficient both in terms of grammar and image rewards. The platform is used from approximately 500 users worldwide.

TeXMeGis a front-end for texture analysis and edge detection platform in Matlab that relies on Gabor filtering and image demodulation (publicly
available at
http://

**Image-based Procedural Modeling of Urban environments**: In
we develop a multiple hypotheses testing algorithm for
image-based/grammar-driven building modeling. Shape grammars are used to express the variation of the observed architecture. Such a model is coupled with the observations through a maximum
likelihood principle where the aim is to maximize the posterior segmentation probability in the image plane given the partition being determined from the grammar derivation. The unknown
parameters of the process involve the grammar derivation tree and the associated parameters. Such a mixed continuous/discrete problem is solved through a hill climbing approach that
involves joint perturbations in the derivation and parameter space. Promising results demonstrated the potentials of such a formulation for complex Parisian architectures. This idea was
further extended in
where reinforcement learning was used as optimization principle. 2D
Image-based grammar parsing was expressed as a Markov decision process where an agent ought to take actions in an environment so as to maximize some notion of cumulative reward. Performance
in particular computational gain over
demonstrated the extreme potentials of such a formulation. In order
to cope with multi-view geometry, the grammar was further derived to include 3D components and the optimization process was amended to deal with multiple views. An evolutionary computation
process (based on consistent mutation and recombination of partial grammar trees) was proposed to fuse image and depth-based information. The use of the Pareto frontier between the two
concurrent components of the objective function provides a principle way to determine the optimal solution of the designed objective function.

**Compressed Sensing Digital Subtraction Rotational Angiography**: in
we develop an extension of iterative filtered backprojection method
for reconstruction of three-dimensional vascular structures from two spins. Our contribution refers to an approach that improves the reconstruction quality of non-sparse volumes when there
exists a sparse combination of these volumes. This is achieved through a joint reconstruction of the mask and contrast volumes via

**Segmentation with Deformable Graph-based Priors**: in
we have introduced a novel formulation to address deformable
segmentation using graph-based priors while being able to handle partial-correspondences. Segmentation was formulated as a matching task, where candidate correspondences were determined
using boosting, and the assignment problem was solved using MAP inference constrained by a graph-based deformable prior. The notion of missing/erroneous correspondences was introduced in
the process leading to state-of-the art results once compared with prior art in the field. The same prior was used in the context of the segmentation of tagging MR heart images
. The main contribution of this paper was the exact estimation of
the region-based probability likelihood within a pair-wise MRF through the use of Stokes theorem and integral images.

**Pose-invariant Higher Order Graph-based Priors**: in
we have introduced a novel method for 3D model inference from 2D
images in the absence of camera pose parameters. The method exploits higher (fourth) order priors, which alleviate the need of the estimation of the camera parameters. Furthermore, the
proposed formulation couples 3D model inference with 2D correspondences and results on a single shot solution for both problems in the absence of knowledge of the observer internal and
external parameters.

**Quasi-real Time Registration**: in
we proposed a novel message-passing based optimization method to
for pair-wise Markov Random Fields models and their applications in medical imaging and computer vision. Such a method was integrated to the deformable registration paradigm introduced in
. Such an optimization framework was combined with efficient use
of modern architectures (Graphics Processing Units) leading to a speed up of at least one order of magnitude with respect to
making quasi real-time deformable registration feasible.

**Deformable registration of gene expression data**: in
the combined iconic/geometric registration framework introduced in
was extended to deal with gene expression data. Similarity
Sensitive Hashing was used to establish costs for landmark correspondences, and a graph-based formulations with unknowns the deformation vectors was adopted for the objective function. Such
an idea was extended to deal with combined segmentation/registration approach through an atlas in
where subdivision surfaces were considered to represent the
deformation grid.

**Coupled Iconic/Geometric Spatio-temporal Segmentation**: in
we have introduced a combined elongated structures
segmentation/tracking approach that was based on a two-layer graphical model. The image layer was exploiting the visual space and was seeking to minimize a data-driven cost while the
geometric layers was seeking to establish temporal correspondences of the deforming structure. These two layers were coupled through a common set of variables acting on the deformation of
the control points representing the elongated structure. Guide-wire segmentation
and tracking in low signal-to-noise ratio interventional images
demonstrated the extreme potentials of our approach.

**Intrasene**: spatio-temporal modeling of low gliomas brain tumors [PhD thesis S. Parisot: 2010-2013]

**General Electric HealthCare**

Compressed Sensing Digital Subtraction Rotational Angiography [PhD thesis H. Langet: 2009-2012]

Guide-wire Segmentation and Tracking of in interventional Imaging [PhD thesis N. Honnorat: 2008-2011]

**Microsoft**: Image-based Procedural Modeling of Large Scale Urban Environments [PhD thesis O. Teboul: 2008-2011]

**Siemens**: Muscle Segmentation in MR Imaging [PhD thesis P-Y. Baudin: 2009-2012]

**SubSample:**A chair proposal was submitted to DIGITEO in collaboration with the PARIETAL group (B. Thirion) from Pr. Dimitris Samaras (StonyBrook) aiming understanding correlations
between imaging and gene expressions data. The proposal was accepted and Pr. Samaras will be spending for the next four years, three months per year at Ecole Centrale. In parallel a PhD
student will be co-supervised between B. Thirion and D. Samaras.

**sterEOS+:**MEDICEN excellence cluster supported a regional imitative towards the creation of the new generation clinical orthopedic work-station. This was a collaborative project
consisting of EOS-Imaging (hardware provide/low dose X-ray Imaging), Global Imaging on Line (software provider - Picture archiving and communication system), the Arts et Métiers ParisTech
(image-based biomechanical modeling), the GALEN group (medical image processing) and the leading clinical and university hospitals in the greater Paris area

**ADOC:**MEDICEN excellence cluster supported a regional imitative towards an imaging scanner providing guided diagnosis for cancer surgery. This translational research project will be
conducted in collaboration between public partners (Inria, The Curie Institut and Hopital Tenon) and private companies (LLtech, Intrasense). A new imaging scanner allowing real time digital
histology will be developed to assist the surgeon. The digital images will be used to give an indication to the surgeon, after a pathologist’ validation, on whether the surgical procedure
shall be continued or stopped.

Program: European Research Council

Project acronym: DIOCLES

Project title: Discrete bIOimaging perCeption for Longitudinal Organ modEling and computEr-aided diagnosiS

Duration: mois année début - mois année fin 9/2011-8/2016

Coordinator: N. Paragios

Abstract: Recent hardware developments from the medical device manufacturers have made possible non-invasive/in-vivo acquisition of anatomical and physiological measurements. One can cite numerous emerging modalities (e.g. PET, fMRI, DTI). The nature (3D/multi-phase/vectorial) and the volume of this data make impossible in practice their interpretation from humans. On the other hand, these modalities can be used for early screening, therapeutic strategies evaluation as well as evaluating bio-markers for drugs development. Despite enormous progress made on the field of biomedical image analysis still a huge gap exists between clinical research and clinical use. The aim of this proposal is three-fold. First we would like to introduce a novel biomedical image perception framework for clinical use towards disease screening and drug evaluation. Such a framework is expected to be modular (can be used in various clinical settings), computationally efficient (would not require specialized hardware), and can provide a quantitative and qualitative anatomo-pathological indices. Second, leverage progress made on the field of machine learning along with novel, efficient, compact representation of measurements toward computer aided diagnosis. Last, using these emerging multi-dimensional signals, we would like to perform longitudinal modeling and understanding the effects of aging to a number of organs and diseases that do not present pre-disease indicators such as brain neurological diseases, muscular diseases, certain forms of cancer, etc. Such a challenging and pioneering effort lies on the interface of medicine (clinical context), biomedical imaging (choice of signals/modalities), machine learning (manifold representations of heterogeneous multivariate variables), discrete optimization (computationally efficient infer- ence of higher-order models), and bio-medical image inference (measurements extraction and multi-modal data fusion of heterogeneous information sources). The expected results of such an approach are societal and scientific. The societal impact can be tremendous since we aim to provide novel means of using emerging biomedical signals to help physicians diagnose, select, customize and follow up therapeutic strategies for life-threatening diseases. Concerning scientific impact, this framework could influence and introduce novel means of re-thinking old, unsolved problems in a number of areas such us bioinformatics, geometric modeling, robotics, computer vision, multimedia, etc.

Partner 1: Technical University of Munich, Chair for Computer Aided Medical Procedures & Augmented Reality - Computer Science Department (Germany)

Mono and Multi-modal image fusion using discrete optimization and efficient linear programming.

Partner 2: University of Crete, Computer Vision Group - Computer Science Department, (Greece)

Linear Programming, relaxations and efficient optimization of pair-wise and higher order Markov Random Fields.

Partner 3: Eidgenössische Technische Hochschule (ETH) - Zürich, Seminar für angewandte Mathematik - Mathematics Department, (Switzerland)

Sparse Representations and Optimal Linear Registration of Volumetric Medical Image Data.

Galen Team along with the Machine Learning Group (DAGS) of the Computer Science Department of Stanford University have proposed the creation of the SPLENDID — Self-Paced Learning for Exploiting Noisy, Diverse or Incomplete Data associate team. The proposal was among the ones accepted in the 2011 INRIA campaign.

**Department of Diagnostic Radiology, University of Pennsylvania:**The GALEN and the Section of Biomedical Image Analysis - SBIA group (Pr. C. Davatzikos) have an established
collaboration during the past three years in the area of deformable image fusion. In this context, PhD candidates of the GALEN group spend time visiting the SBIA group, while Pr. Paragios
participates at a Nantional Institute Health grant led by SBIA. Such a collaboration led to a number of outstanding rank journal and conference publications
.

**Department of Computer Science, StonyBrook, State University of New York:**The GALEN and the Image Analysis Lab - CBL (Pr. D. Samaras) have an established collaboration during the
past three years in the area of graph-based methods in medical imaging and computer vision. Pr. Samaras holds a research professor position (DIGITEO chair) at Ecole Centrale de Paris.
Such a collaboration led to a number of outstanding rank conference publications during the last year
,
.

**Department of Computer Science, University of Houston:**The GALEN and the Computational Biomedicine Lab - CBL (Pr. I. Kakadiaris) have an established collaboration during the past
three years in the area of medical image segmentation and gene expressions imaging processing. Pr. Paragios holds a research professor position at the Computer Science Department of the
University of Houston. Such a collaboration led to a number of outstanding rank conference publications
during the last year
,
.

**Chang Gung Memorial Hospital – Linkou, Taiwan:**In the context of France-Taiwan program sponsored from the French Science Foundation, GALEN (in collaboration with the department of
radiology of Henri Mondor University Hospital), a project (ADAMANTIUS) was initiated with the Chang Gung Memorial Hospital – Linkou that is the largest private hospital in Taiwan. The aim
of the project is to study the Automatic Detection And characterization of residual Masses in pAtients with lymphomas through fusioN of whole-body diffusion-weighTed mrI on 3T and
18F-flUorodeoxyglucoSe pet/ct.

**Rafeef Abugharbieh:**Jan-Jun. 2011, University of British Columbia - CA.

**Ghassan Hamarneh:**Jan-Jun. 2011, Simon Fraser University - CA.

**Dimitris Samaras:**Oct. 2011, State University of New York - StonyBrook, US.

**Avinash Singh Bagri:**Indian Institute of Technology - New Delhi, IN - Message Passing Methods on Graphics Processing Units towards Real-time Deformable Image Fusion .

**Krishna Nand Keshava Murthy:**University of British Columbia, CA - Iconic/Geometric Deformable Registration of Diffusion Tensor Images.

**Thanos Papadopoulos:**Technical University of Athens, GR - Iconic/Geometric Atlas-based Segmentation of Liver Volumetric Images.

**Jose Carlos Rubio:**Universitat Autònoma de Barcelona, ES - HyperGraph Representations and Matching towards Scene Understanding.

**Stavros Tsogkas:**Technical University of Athens, GR - Learning-based Symmetry Detection.

**Matthew Blaschko**

**Guest Editorships:**International Journal of Computer Vision: Special Issue on Structured Prediction and Inference

**Conference Committee:**IEEE International Conference on Computer Vision, IEEE Computer Vision and Pattern Recognition, Artificial Intelligence and Statistics (area chair), Neural
Information Processing Systems, Robotics: Science and Systems, International Conference on Robotics and Automation

**Workshop & Tutorials Organization:**British Machine Vision Conference Tutorial on Structured Prediction, Twentieth Annual Computational Neuroscience Meeting CNS*2011 Tutorial
on Machine Learning and Kernel Methods

**Journal Reviewing Services:**IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Machine Learning Research, PLoS ONE.

**Invited Seminars/Presentations:**Max Planck Institutes, Tübingen; Royal Academy of Engineering; Gatsby Computational Neuroscience Unit, University College London; University of
Sheffield; Radboud Universiteit Nijmegen; University of Birmingham; Toyota Technological Institute at Chicago; University of Illinois at Chicago; Winter Intelligence Conference, Future
of Humanity Institute, University of Oxford

**Distinctions:**Newton International Fellow, Best Reviewer Award IEEE International Conference on Computer Vision.

**Iasonas Kokkinos**

**Editorial Activities:**Image and Vision Computing Journal.

**Conference Committee:**IEEE International Conference on Computer Vision, IEEE Computer Vision and Pattern Recognition, Artificial Intelligence and Statistics, IEEE Workshop in
Stochastic Image Grammars Workshop, Energy Minimization Methods in Computer Vision and Pattern Recognition.

**Journal Reviewing Services:**IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, IEEE Transactions on Systems Man and
Cybernetics, Part B., Computer Vision and Image Understanding.

**PhD Committee Participation:**Olivier Teboul - Ecole Centrale de Paris - FR.

**Master Committee Participation:**Stavros Tsogkas - National Technical University of Athens -GR.

**Invited Seminars/Presentations:**Symmetry Detection in Real World Images Workshop, in conjunction with the IEEE Conference in Computer Vision and Pattern Recognition - US, Visual
Geometry Group, Oxford University - UK, Visual Computing Lunch, ETH Zurich - CH, Computer Science Department, Università della Svizzera Italiana - CH.

**Pawan Kumar**

**Conference Committee:**IEEE Conference on Computer Vision and Pattern Recognition, IEEE International Conference on Computer Vision, International Conference on Machine Learning,
Advances in Neural Information Processing Systems.

**Journal Reviewing Services:**IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Machine Learning Research.

**Workshop & Tutorials Organization:**IEEE International Conference in Computer Vision tutorial on
*Learning with Inference for Discrete Graphical Models*, IEEE Computer Vision and Pattern Recognition Workshop on
*Inference in Graphical Models with Structured Potentials*.

**Invited Seminars/Presentations:**Mysore Park Workshop on Computer Vision - IN, Ecole Normale Superieure - FR, Ecole Centrale de Paris - FR, Kungliga Techniska Hogskolan, SE.

**Distinctions:**Best Reviewer Award, IEEE Conference in Computer Vision and Pattern Recognition.

**Nikos Paragios**

**Editorial Activities:**IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, Medical Image Analysis, Computer Vision and Image
Understanding, Image and Vision Computing Journal, Machine Vision and Applications, SIAM Journal in Imaging Sciences.

**Guest Editorships:**Computer Vision and Image Understanding, Image and Vision Computing Journal, Special issue on Optimization for vision, graphics and medical imaging: Theory and
applications
.

**Conference Committee:**IEEE International Conference in Computer Vision, IEEE Computer Vision and Pattern Recognition, Medical Image Computing and Computer Assisted Intervention
(area chair), Information Processing in Medical Imaging, IEEE International Symposium on Biomedical Imaging, IEEE Mathematical Methods in Biomedical Image Analysis, International
Symposium on Visual Computing.

**Workshop & Tutorials Organization:**IEEE International Conference in Computer Vision tutorial on
*Learning with Inference for Discrete Graphical Models*.

**Journal Reviewing Services:**IEEE Transactions on Image Processing.

**PhD Committee Participation:**Daniel Pescia - Ecole Centrale de Paris - FR, Yangming Ou - University of Pennsylvania - US, Olivier Teboul - Ecole Centrale de Paris - FR, Benjamin
Glocker - Technical University of Munich - DE, Loic Simon - Ecole Centrale de Paris - FR, Chaohui Wang - Ecole Centrale de Paris - FR, Maélène Lohezic - University of Nancy - FR,
Aristeidis Sotiras - Ecole Centrale de Paris - FR, Hiep Hoang Vu - Ecole des Ponts-ParisTech - FR, Christophe Avenel - Univeristy of Rennes - FR.

**Invited Seminars/Presentations:**British Machine Vision Conference - UK, Isaac Newton Institute for Mathematical Sciences, Analytic and Geometric Methods in Medical Imaging -
UK.

**Distinctions:**IEEE Fellow.

Master : Introduction to Signal Processing, 36, M1, Ecole Centrale de Paris, France [I. Kokkinos]

Master : Introduction to Computer Vision, 36, M1, Ecole Centrale de Paris, France [I. Kokkinos]

Master : Pattern Recognition, 24, M2, Ecole Centrale de Paris/Ecole Normale Superieure-Cachan, France [I. Kokkinos]

Master : Advanced Mathematical Models in Computer Vision, 24, M2, Ecole Centrale de Paris/Ecole Normale Superieure-Cachan, France [N. Paragios]

N. Paragios is in charge of the option Medical Imaging, Machine Learning and Computer Vision at the Department of Applied Mathematics of Ecole Centrale de Paris. This option consists of 6 classes in the above mentioned fields, 180 hours of teaching and is associated with the M.Sc. (M2) program of the ENS-Cachan in Applied Mathematics, Machine Learning and Computer Vision.

PhD: Daniel Pescia , Segmentation des tumeurs du foie sur des images de scanner CT, Ecole Centrale de Paris, 15/01/2011, Nikos Paragios

PhD: Aristeidis Sotiras , Discrete Image Registration: a Hybrid Paradigm, Ecole Centrale de Paris, 6/11/2011, Nikos Paragios