Chaire Georges Lemaître 2017 in CP3
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Europe/Brussels
E/3rd floor-E.349 - Seminar room (E.349) (Marc de Hemptinne (chemin du Cyclotron, 2, Louvain-la-Neuve))
E/3rd floor-E.349 - Seminar room (E.349)
Marc de Hemptinne (chemin du Cyclotron, 2, Louvain-la-Neuve)
30
Description
Physics, Statistics, and machine learning by Kyle Cranmer
Prof. Kyle Cranmer is the recipient of the 2017 Georges Lemaitre Chaire in CP3 .
He will give a inaugural lecture on Monday 27th of Nov. at 16:15 (CYCL01) and then a series of four 2h lectures (starting at 10:45, Tue-Fri, E.361)
CV (EN)
Kyle Cranmer is Associate Professor of Physics at New York University and affiliated with NYU’s Center for Data Science and CILVR Machine Lab. He is an experimental particle physicist working, primarily, on the Large Hadron Collider. Professor Cranmer obtained his Ph.D. in Physics from the University of Wisconsin-Madison in 2005 and his B.A. in Mathematics and Physics from Rice University. He was awarded the Presidential Early Career Award for Science and Engineering in 2007 and the National Science Foundation’s Career Award in 2009. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in July 2012. His current interests are at the intersection of physics, statistics, and machine learning.
CV (FR)
Kyle Cranmer est Professeur de physique à la New York University et membre affilié aux Center for Data Science et CILVR Machine Lab de la New York University. Il est expérimentateur en physique des particules, essentiellement auprès du grand collisionneur de hadrons. Le professeur Cranmer a obtenu son doctorat en physique à l’Université de Wisconsin-Madison en 2005 et son B.A. en Mathématique et Physique à l’Université Rice. Il a reçu le « Presidential Early Career Award» en sciences et ingénierie en 2007 et le « National Science Foundation’s Career Award» en 2009. Le professeur Cranmer a développé un environnement qui permet une modélisation statistique collaborative, utilisé largement dans la découverte du boson de Higgs en juillet 2012. Ses centres d’intérêts actuels sont à la frontière entre la physique, la statistique et le machine learning.
Prof. Kyle Cranmer is the recipient of the 2017 Georges Lemaitre Chaire in CP3 .
He will give a inaugural lecture on Monday 27th of Nov. at 16:15 (CYCL01) and then a series of four 2h lectures (starting at 10:45, Tue-Fri, E.361)
CV (EN)
Kyle Cranmer is Associate Professor of Physics at New York University and affiliated with NYU’s Center for Data Science and CILVR Machine Lab. He is an experimental particle physicist working, primarily, on the Large Hadron Collider. Professor Cranmer obtained his Ph.D. in Physics from the University of Wisconsin-Madison in 2005 and his B.A. in Mathematics and Physics from Rice University. He was awarded the Presidential Early Career Award for Science and Engineering in 2007 and the National Science Foundation’s Career Award in 2009. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in July 2012. His current interests are at the intersection of physics, statistics, and machine learning.
CV (FR)
Kyle Cranmer est Professeur de physique à la New York University et membre affilié aux Center for Data Science et CILVR Machine Lab de la New York University. Il est expérimentateur en physique des particules, essentiellement auprès du grand collisionneur de hadrons. Le professeur Cranmer a obtenu son doctorat en physique à l’Université de Wisconsin-Madison en 2005 et son B.A. en Mathématique et Physique à l’Université Rice. Il a reçu le « Presidential Early Career Award» en sciences et ingénierie en 2007 et le « National Science Foundation’s Career Award» en 2009. Le professeur Cranmer a développé un environnement qui permet une modélisation statistique collaborative, utilisé largement dans la découverte du boson de Higgs en juillet 2012. Ses centres d’intérêts actuels sont à la frontière entre la physique, la statistique et le machine learning.
Participants
Alessia Saggio
Alessio Magitteri
Ambresh Shivaji
Andrea Giammanco
Andres M. Belaza
Anronio Carlos O. Santos
Bram Verbeek
Chiara Arina
Christophe Delaere
Claude Duhr
Claudio Caputo
David Gordo
Denis FAVART
Eduardo Cortina Gil
Efthymios Karangelos
Elisa Minucci
Enrico Vitale
Fabio Maltoni
Federico Ambrogi
Florian Bury
Giacomo Bruno
Jan Govaerts
Jan Hajer
Jean Liénardy
Jerome de Favereau
Joeri Hermans
Johan Suykens
Jorgen D'Hondt
Kaoru Hagiwara
Ken Mimasu
Leonardo leoguti85
Liam Moore
Louis Wehenkel
Louppe Gilles
Luca Mantani
Manoj Mandal
Marco Drewes
Marco Musich
Martin Michel
Michael Fanuel
Michel Herquet
Michele Lucente
Michele Mastropietro
Miguel Vidal
Olivier Mattelaer
Oswaldo Gressani
p lambrechts
Pascal Vanlaer
patrick guerin
Pavel Demin
Philippe Ruelle
Picot Thomas
Pierre Dupont
Pierre Geurts
Pieter David
Pragati Mitra
Roberto Franceschini
Robin Marzucca
Seth Moortgat
Shivangee Rathi
Sophia Borowka
Sophie Mathieu
stijn de baerdemacker
Sébastien Wertz
Tim Van der Linden
Vincent Lemaître
Xiaoran Zhao
Zhengwen Liu