Name: ESCAPE SUMMER SCHOOL
Start: 2021-06-07T08:30:00+02:00
End: 2021-06-18T14:00:00+02:00
Location: LAPP

ESCAPE SUMMER SCHOOL

de lundi 7 juin 2021 (08:30) à vendredi 18 juin 2021 (14:00)

lundi 7 juin 2021
09:00 Welcome address - Thomas Vuillaume (LAPP, CNRS)
Welcome address
- Thomas Vuillaume (LAPP, CNRS)
09:00 - 09:10
https://www.youtube.com/channel/UC05braEQdP2rCSUamHm9I_Q/live
09:10 ESCAPE - Giovanni LAMANNA (LAPP - IN2P3/CNRS)
ESCAPE
- Giovanni LAMANNA (LAPP - IN2P3/CNRS)
09:10 - 09:30
https://www.youtube.com/channel/UC05braEQdP2rCSUamHm9I_Q/live
09:30 School organisation - Thomas Vuillaume (LAPP, CNRS)
School organisation
- Thomas Vuillaume (LAPP, CNRS)
09:30 - 10:30
https://www.youtube.com/channel/UC05braEQdP2rCSUamHm9I_Q/live
10:30 Coffee break
Coffee break
10:30 - 11:00
11:00 Reproducible science
Reproducible science
11:00 - 12:30
Contributions

11:00 Reproducible science - Rachael Ainsworth
12:30 Lunch break
Lunch break
12:30 - 14:00
14:00 Environment setup
Environment setup
14:00 - 14:30
Contributions

14:00 Environment setup - Enrique Garcia Garcia
14:30 Python and Notebooks
Python and Notebooks
14:30 - 16:00
Contributions

14:30 Python and Notebooks - Enrique Garcia Garcia
mardi 8 juin 2021

09:00 Git
Git
09:00 - 10:30

10:30 Coffee break
Coffee break
10:30 - 11:00

11:00 Git
Git
11:00 - 12:30

12:30 Lunch break
Lunch break
12:30 - 14:00
mercredi 9 juin 2021
09:00 Python introduction, useful packages and libraries for data scientists
Python introduction, useful packages and libraries for data scientists
09:00 - 10:30
Contributions

09:00 Library overview - Tamas Gal
10:30 Coffee break
Coffee break
10:30 - 11:00
11:00 Numpy
Numpy
11:00 - 12:30
Contributions

11:00 Numpy
12:30 Lunch break
Lunch break
12:30 - 14:00
14:00 Pandas
Pandas
14:00 - 15:30
Contributions

14:00 Pandas - Tamas Gal
15:30 Matplotlib
Matplotlib
15:30 - 16:30
Contributions

15:30 Matplotlib - Tamas Gal
jeudi 10 juin 2021
09:00 Reproducible science in practice
Reproducible science in practice
09:00 - 10:30
Contributions

09:00 Reproducible science in practice - Arturo Sanchez Pineda (LAPP)
10:30 Coffee break
Coffee break
10:30 - 11:00
11:00 Reproducible science in practice
Reproducible science in practice
11:00 - 12:30
Contributions

11:00 Reproducible science in practice - Arturo Sanchez Pineda (LAPP)
12:30 Lunch break
Lunch break
12:30 - 14:00
14:00 Test driven devs - unit tests and continuous integration
Test driven devs - unit tests and continuous integration
14:00 - 15:30
Contributions

14:00 Test driven developments and continuous integration - Maximilian Nöthe
15:30 Coffee break
Coffee break
15:30 - 16:00
16:00 Packaging
Packaging
16:00 - 17:00
Contributions

16:00 Packaging - Maximilian Nöthe
vendredi 11 juin 2021

09:00 Scipy
Scipy
09:00 - 10:30

10:30 Coffee break
Coffee break
10:30 - 11:00

11:00 Astropy
Astropy
11:00 - 12:30

12:30 Lunch break
Lunch break
12:30 - 14:00
14:00 Webinar: AI in Cosmological Experiments - Ofer Lahav (University College London)
Webinar: AI in Cosmological Experiments
- Ofer Lahav (University College London)
14:00 - 15:00
Abstract: Detailed observations of the present contents of the Universe are consistent with the Lambda + Cold Dark Matter model, subject to some ‘tensions'. To probe these components further the Dark Energy Survey and larger galaxy surveys require new statistical approaches. In particular the role of Artificial Intelligence, or preferably Augmented Intelligence, is crucial for analysing forthcoming surveys (e.g. Rubin-LSST and Euclid) of billions of galaxies. This also requires new ways to train the next generation of scientists for the challenges ahead. Prof. Ofer Lahav is Perren Chair of Astronomy in the Astrophysics Group at University College London (UCL) and Vice-Dean (International) of the UCL Faculty of Mathematical and Physical Sciences (MAPS). He is also Co-Director of the STFC-funded Centre for Doctoral Training in Data Intensive Science at UCL. Ofer's research area is Observational Cosmology, in particular probing Dark Matter and Dark Energy. His work involves Machine Learning for Big Data. https://www.ucl.ac.uk/astrophysics/professor-ofer-lahav
15:00 Discussion with teachers
Discussion with teachers
15:00 - 16:00
samedi 12 juin 2021
dimanche 13 juin 2021

lundi 14 juin 2021
09:00 Debugging and profiling
Debugging and profiling
09:00 - 10:30
Contributions

09:00 Debugging and profiling - Karl KOSACK (CEA Saclay)
10:30 Coffee break
Coffee break
10:30 - 11:00
11:00 Optimisation and parallelism in Python
Optimisation and parallelism in Python
11:00 - 12:30
Contributions

11:00 Optimisation and parallelism in Python - Karl KOSACK (CEA Saclay)
12:30 Lunch break
Lunch break
12:30 - 14:00

14:00 Gammapy
Gammapy
14:00 - 15:00

15:00 Coffee break
Coffee break
15:00 - 15:30
15:30 Scikit-HEP
Scikit-HEP
15:30 - 17:30
Contributions

15:30 Community specific analysis: Scikit-HEP
mardi 15 juin 2021

10:00

10:00 - 16:00
mercredi 16 juin 2021

09:00 Spark
Spark
09:00 - 10:30

10:30 Coffee break
Coffee break
10:30 - 11:00

11:00 Spark
Spark
11:00 - 12:30

12:30 Lunch break
Lunch break
12:30 - 14:00
14:00 Introduction to Julia
Introduction to Julia
14:00 - 15:00
Contributions

14:00 Introduction to Julia - Tamas Gal
15:00 Analysis Workflow from the KM3NeT Open Data Center
Analysis Workflow from the KM3NeT Open Data Center
15:00 - 16:00
Room: Auditorium

16:00 An introduction to gravitational wave data analysis
An introduction to gravitational wave data analysis
16:00 - 17:00
jeudi 17 juin 2021
09:00 Introduction to machine learning
Introduction to machine learning
09:00 - 10:30
Contributions

09:00 Machine Learning - Claudia Beleites Martino Sorbaro
10:30 Coffee break
Coffee break
10:30 - 11:00
11:00 Introduction to machine learning
Introduction to machine learning
11:00 - 12:30
Contributions

11:00 Machine Learning - Martino Sorbaro
12:30 Lunch break
Lunch break
12:30 - 14:00
14:00 Introduction to machine learning
Introduction to machine learning
14:00 - 16:00
Contributions

14:00 Machine Learning - Martino Sorbaro
vendredi 18 juin 2021
09:00 Introduction to deep learning
Introduction to deep learning
09:00 - 10:30
Contributions

09:00 Introduction to Deep learning
10:30 Coffee break
Coffee break
10:30 - 11:00
11:00 Introduction to deep learning
Introduction to deep learning
11:00 - 12:30
Contributions

11:00 Introduction to Deep learning
12:30 Farewell - Thomas Vuillaume (LAPP, CNRS)
Farewell
- Thomas Vuillaume (LAPP, CNRS)
12:30 - 13:00
Room: Auditorium

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