EuroPython 2019

Date: July 8-14 2019

Venue: Basel, CH



Twitter: @europython

Conference App will be announced on the blog.

EuroPython is an annual conference hosting ~1200 participants from academia and companies, interested in development and applications of python programming language. It's also a good opportunity for students and postdocs who wish to find a job outside academia.

For more info, contact: Valeria Pettorino


A Distributed Learning Architecture for Scientific Imaging Problems


Authors: A. Panousopoulou, S. Farrens, K. Fotiadou, A. Woiselle, G. Tsagkatakis, J-L. Starck,  P. Tsakalides
Journal: arXiv
Year: 2018
Download: ADS | arXiv


Current trends in scientific imaging are challenged by the emerging need of integrating sophisticated machine learning with Big Data analytics platforms. This work proposes an in-memory distributed learning architecture for enabling sophisticated learning and optimization techniques on scientific imaging problems, which are characterized by the combination of variant information from different origins. We apply the resulting, Spark-compliant, architecture on two emerging use cases from the scientific imaging domain, namely: (a) the space variant deconvolution of galaxy imaging surveys (astrophysics), (b) the super-resolution based on coupled dictionary training (remote sensing). We conduct evaluation studies considering relevant datasets, and the results report at least 60\% improvement in time response against the conventional computing solutions. Ultimately, the offered discussion provides useful practical insights on the impact of key Spark tuning parameters on the speedup achieved, and the memory/disk footprint.

Cosmostat Day on Machine Learning in Astrophysics

Date: January the 24th, 2019

Organizer:  Joana Frontera-Pons  <>


Local information

CEA Saclay is around 23 km South of Paris. The astrophysics division (DAp) is located at the CEA site at Orme des Merisiers, which is around 1 km South of the main CEA campus. See  for detailed information on how to arrive.

On January the 24th, 2019, we organize the fourth day on machine learning in astrophysics at DAp, CEA Saclay. 


All talks are taking place at DAp, Salle Galilée (Building 713)

14:00 - 14:30h. Machine Learning in High Energy Physics : trends and successes -  David Rousseau (LAL)                             
14:30 - 15:00h. Learning recurring patterns in large signals with convolutional dictionary learning - Thomas Moreau (Parietal team - INRIA Saclay)
15:00 - 15:30h. Distinguishing standard and modified gravity cosmologies with machine learning -  Austin Peel (CEA Saclay - CosmoStat)

15:30 - 16:00h. Coffee break

16:00 - 16:30h.  The ASAP algorithm for nonsmooth nonconvex optimization. Applications in imagery - Pauline Tan (LJLL - Sorbonne Université)                                      16:30 - 17:00h. Deep Learning for Blended Source Identification in Galaxy Survey Data - Samuel Farrens (CEA Saclay - CosmoStat)

Machine Learning in High Energy Physics : trends and successes

David Rousseau (LAL)

Machine Learning has been used somewhat in HEP in the nighties, then at the Tevatron and recently at the LHC (mostly Boosted Decision Tree). However with the birth of internet giants at the turn of the century, there has been an explosion of Machine Learning tools in the industry.. A collective effort has been started for the last few years to bring state-of-the-art Machine Learning tools to High Energy Physics.
This talk will give a tour d’horizon of Machine Learning in HEP : review of tools ; example of applications, some used currently, some in a (possibly distant) future (e.g. deep learning, image vision, GAN) ; recent and future HEP ML Kaggle competitions. I’ll conclude on the key points to set up frameworks for High Energy Physics and Machine Learning collaborations.

Learning recurring patterns in large signals with convolutional dictionary learning

Thomas Moreau (Parietal team - INRIA Saclay)

Convolutional dictionary learning has become a popular tool in image processing for denoising or inpainting. This technique extends dictionary learning to learn adapted basis that are shift invariant. This talk will discuss how this technique can also be used in the context of large multivariate signals to learn and localize recurring patterns. I will discuss both computational aspects, with efficient iterative and distributed convolutional sparse coding algorithms, as well as a novel rank 1 constraint for the learned atoms. This constraint, inspired from the underlying physical model for neurological signals, is then used to highlight relevant structure in MEG signals.

Distinguishing standard and modified gravity cosmologies with machine learning

Austin Peel (CEA Saclay - CosmoStat)

Modified gravity models that include massive neutrinos can mimic the standard concordance model in terms of weak-lensing observables. The inability to distinguish between these cases could limit our understanding of the origin of cosmic acceleration and of the fundamental nature of gravity. I will present a neural network we have designed to classify such cosmological scenarios based on the weak-lensing maps they generate. I will discuss the network's performance on both clean and noisy data, as well as how results compare to conventional statistical approaches.

The ASAP algorithm for nonsmooth nonconvex optimization. Applications in imagery

Pauline Tan (LJLL - Sorbonne Université)

In this talk, I will address the challenging problem of optimizing nonsmooth and nonconvex objective functions. Such problems are increasingly encountered in applications, especially when tackling joint estimation problems. I will propose a novel algorithm and demonstrate its convergence properties. Eventually, three actual applications in industrial imagery problems will be presented.

Deep Learning for Blended Source Identification in Galaxy Survey Data

Samuel Farrens (CEA Saclay - CosmoStat)

Weak gravitational lensing is a powerful probe of cosmology that will be employed by upcoming surveys such as Euclid and LSST to map the distribution of dark matter in the Universe. The technique, however, requires precise measurements of galaxy shapes over larges areas. The chance alignment of galaxies along the line of sight, i.e. blending of images, can lead to biased shape measurements that propagate
to parameter estimates. Machine learning techniques can provide an automated and robust way of dealing with these blended sources. In this talk I will discuss how machine learning can be used to classify sources identified in survey data as blended or not and show some preliminary results for CFIS simulations. I will then present some plans for future developments making use of multi-class classification and segmentation.

 Previous Cosmostat Days on Machine Learning in Astrophysics :

Euclid - France atelier/workshop gravitational lensing

Date: October 22, 2018

Organizer:  Martin Kilbinger & Karim Benabed

Venue: IAP,  98bis bd Arago, 75014 Paris. Salle Entresol

Local information


Martin Kilbinger
Karim Benabed
Sandrine Codis
Eric Jullo
Francis Bernardeau
Yohan Dubois
Santiago Casas
Raphael Gavazzi
Alain Blanchard
Patrick Hudelot
Calum Murray
Matteo Rizzato
Samuel Farrens
Alexandre Barthelemy
Austin Peel
Nicolas Martinet
Morgan Schmitz
Virginia Ajani
Henry McCracken
Peter Taylor
Bertrand Morin
Céline Gouin



10:00   Café
10:30   Martin Kilbinger                Welcome, introduction, goals of the meeting, resources
10:45   Matteo Rizzato                   Information content in the weak lensing bispectrum
11:15   Eric Jullo                                 WLSWG work package “Galaxy-galaxy lensing”
11:45   Alexandre Barthelemy    One-point statistics of weak lensing maps
12:15    Peter Taylor                         k-cut Cosmic Shear: Tunable Power Spectrum Sensitivity to Test Gravity
12:45   Henry Joy McCracken    Euclid VIS activities and weak lensing requirements
13:00   Lunch
14:15   Austin Peel                           Peak counts: breaking degeneracies & machine learning
14:45   Nicolas Martinet               WL peak/mass mapping/shear calibration
15:15   Céline Gouin                       The impact of baryons on WL statistics
15:45   Bertrand Morin                  COSEBIs - Implementation of cosmic shear E-/B- modes
16:15   Martin Kilbinger, all          WL projects in Euclid-France, discussion, future plans
17:15   End



French-Chinese Days on Weak Lensing

Date: October 4-5, 2018

Organizer:  Jean-Luc Starck and Martin Kilbinger


Local information

CEA Saclay is around 23 km South of Paris. The astrophysics division (DAp) is located at the CEA site at Orme des Merisiers, which is around 1 km South of the main CEA campus. See for detailed information on how to arrive.

On 4 and 5 October, 2018, we are organizing the first French-Chinese weak-lensing meeting at DAp, CEA Saclay. 


All talks are taking place at DAp, Salle Kepler (Building 709)

Thursday, October 4

9:30 - 10:00h.  Café

10:00 - 10:15h.    Welcome & introductions

10:15 - 10:45h.   Hu Zhan,   Overview of CSS-OS

10:45 - 11:15h.   Martin Kilbinger, Overview of CFIS Weak Lensing

11:15 - 11:45h.  Jun Zhang, Fourier_Quad,  a shear measurement method in Fourier Space

11:45 - 14:00h.  Lunch at the Rotonde

14:00 - 14:30h. Morgan Schmitz,  PSF Modeling using a Graph Manifold

14:30 - 15:00h. Chengliang Wei, A full sky WL simulation with semi-analytic galaxy formation 

15:00 - 15:30h. Jean-Luc Starck,  WL Mass Mapping

15:30 - 16:00h. Zuhui Fan,  WL peak statistics

16:00 - 16:30h. Austin Peel,  Cosmology with Mass Maps

Friday, October 5

9:30 - 10:00h.      Café

10:00 - 10:30h.   Sam Farrens,   The CFIS pipeline

10:30 - 11:00h.  Ran Li,  Lensing studies of sub-structures

11:00 - 11:30h.  Axel  Guinot,  Preliminary CFIS results

11:30 - 12:00h.  Liping Fu, Shear measurement from VOICE deep survey

12:00 - 14:00h. Lunch at Les Algorithmes

14:00 - 14:30h. Jean-Charles Cuillandre, The Euclid mission and ground-based observations

14:30 - 15:00h.  Huanyuan Shan: KiDS WL studies (via skype)

15:00 - 15:30h.  Alexandre Bruckert, Machine learning for blended objects separation

15:30 - 16:00h.   Rebeca Araripe Furtado Cunha,  Optimal Transport and PSF Modeling

16:00 -  17:00h. Discussion


École Euclid de cosmologie 2018

Date: August 20 - September 1, 2018

Venue: Roscoff, Bretagne, France


Lecture ``Weak gravitational lensing'' (Le lentillage gravitationnel), Martin Kilbinger.

Find here links to the lecture notes, TD exercises, "tables rondes" topics, and other information.

  • Resources.
    • A great and detailed introduction to (weak) gravitational lensing are the 2005 Saas Fee lecture notes by Peter Schneider. Download Part I (Introduction to lensing) and Part III (Weak lensing) from my homepage.
    • Check out Sarah Bridle's video lectures on WL from 2014.
  • TD cycle 1+2, Data analysis.
    1.  We will work on a rather large (150 MB) weak-lensing catalogues from the public CFHTLenS web page. During the TD I will show instructions how to create and download this catalogue. These catalogues will also be available on the virtual machine for the school.
      If you like, you can however download the catalogue on your laptop at home. Please have a look at the instructions in the TD slides.
    2. If you want to do the TD on your laptop, you'll need to download and install athena (the newest version 1.7). Available on the VM.
    3.  For one of the bonus TD you'll need a new version of (v 1.8beta). Download it here. Available on the VM.
  • Lecture notes and exercise classes.  You can already download the slides in one file (40 - 60 MB), but be ware that the content will still change slightly until the classes.
    • Part I (Cycle 1):    [all | day 1 (1/6)  |   day 2 (2/6) |  day 3 (3/6)]
    • Part II (Cycle 2):  [all | day 1 (4/6)   |   day 2 (5/6)  | day 3 (6/6)]
    • TD:                             [1/2 and 2/2]
    • Table Ronde sujet
  • Slack channel: