IN2P3 School of Statistics 2018

Sabine Crepe-Renaudin (LPSC Grenoble; IN2P3-CNRS)

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
  • 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
  • Yann Coadou
  • Yinghao XI
  • Yvan Vallois
  • Ziyu GUO
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