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IN2P3 School of Statistics 2018
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Europe/Paris
Description
The sixth edition of the IN2P3 School Of Statistics gives an overview of the concepts and tools used in particle physics, astro-particle physics and cosmology when probabilities and statistics come to play.
This school is targeted towards PhD students and towards senior physicists, aiming at extending their knowledge and skills in the field of statistical tools and frameworks developed for their fields.
The school takes place in Les Océanides Residence at La Londe Les Maures from Monday 28 May to Friday 1 June 2018.
The school combines lectures and hands-on sessions. The lectures are subdivided into three parts: a part reminding the fundamental concepts used in Probabilities, Statistics and Hypothesis testing applied to physics analysis; a part focusing on the presentation of the concepts and basics of most popular multivariate techniques; and a part dedicated to actual multivariate tools and machine learning.
All lectures will be given in English. The School Of Statistics is supported by CNRS/IN2P3.
Financial support:
Main support comes from CNRS/IN2P3 however SOS 2018 get also substantial financial support from ENIGMASS, OCEVU and P2IO labex, and AMVA4NewPhysics ITN, as well as administrative and technical support from the CPPM laboratory.
Organizing Committee:
Johan Bregeon (LUPM, Montpellier), Nicolas Chanon (IPNL Lyon), Yann Coadou (CPPM, Marseille), Fabrice Couderc (IRFU-DPhP, Saclay), Sabine Crépé-Renaudin (LPSC, Grenoble), Laurent Derome (LPSC, Grenoble), Julien Donini (LPC, Clermont), Boris Hippolyte (IPHC, Strasbourg), David Rousseau (LAL Orsay).
Administrative Support:
Angélique Pèpe (CPPM, Marseille).
Preliminary Program:
- Courses
- Basics concepts: Julien Donini
- Classical interval estimation, limits, CLs, systematics: Tommaso Dorigo
- Introduction to Machine learning: Vincent Barra
- Boosted decision trees, TMVA: Yann Coadou
- Introduction to Deep Learning: Gilles Louppe
- Deep learning at colliders: Amir Farbin
- Optimal experiment design in astronomy: learning to adapt: Emille Ishida
- Hands on sessions
- Introduction to sklearn, BDT, gaussian process, generative model, etc: Gilles Louppe
- Deep learning: Amir Farbin
Participants
Abhilasha Singh
Adam James Hawken
Adam Morris
Aishik Ghosh
Alexandre Naud
Alexis Gamelin
Alina Kleimenova
Amir Farbin
Artur Lobanov
Arturo Nuñez-Castiñeyra
Bartholomé Cauchois
Batoul Diab
Bing LIU
Brieuc Voirin
Camille Camen
Cecilia Tosciri
Clément Buton
David Rousseau
Dawid Gerstel
Dimitri Misiak
Emery Nibigira
Emille Ishida
Fabrice Desse
Fabricio Jimenez Morales
Florian Benedetti
Gaël Touquet
Gilles Louppe
Giovanni Bartolini
Guillaume Bourgatte
Hayg Guler
Hoang Dai Nghia NGUYEN
Humberto Alonso Reyes González
Iro Koletsou
Johan Bregeon
Julien Donini
Julien Souchard
Konstantin Shchablo
Laura Ferraris-Bouchez
Loann Brahimi
Louis Portales
Marie Aubert
Merve Nazlim Agaras
Michal Zamkovsky
Michelle Tsirou
Nicolas Chanon
Nicolas Tonon
Nihal BRAHIMI
Olympia Dartsi
Pierre-Antoine Delsart
Qinhua Huang
reem rasheed
Robert Hankache
Rodrigo Gracia Ruiz
Romain Kukla
Sabine Crépé-Renaudin
Sanjana Sekhar
Sara Marcatili
Tasnuva Chowdhury
Thibaud Blondel
Tommaso Dorigo
Vincent Barra
XU HANLIN
Yann Coadou
Yinghao XI
Yvan Vallois
Ziyu GUO
