Olivier SchneiderAprès une thèse en physique des particules à l'Université de Lausanne, soutenue en 1989, Olivier Schneider rejoint le LBL, Lawrence Berkeley Laboratory (Californie), pour travailler sur l'expérience CDF au Tevatron de Fermilab (Illinois), d'abord au bénéfice d'une bourse de chercher débutant du Fonds National Suisse pour la Recherche Scientifique, puis comme post-doc au LBL. Il participe à la construction et à la mise en service du premier détecteur de vertex au silicium fontionnant avec succès auprès d'un collisionneur hadronique, détecteur qui a permis la découverte du sixième quark, appelé "top". Dès 1994, il revient en Europe et participe à l'expérience ALEPH au grand collisionneur électron-positon du CERN (Genève), comme boursier puis comme titulaire d'un poste de chercheur au CERN. Il se spécialise en physique des saveurs lourdes. En 1998, il est nommé professeur associé à l'Université de Lausanne, puis professeur extraordinaire à l'EPFL en 2003, et enfin professeur ordinaire à l'EPFL en 2010. Ayant participé depuis 1997 à la préparation de l'expérience LHCb au collisionneur LHC du CERN, entrée en fonction à fin 2009, il en analyse maintenant les données. Il contribue aussi depuis 2001 à l'exploitation des données enregistrées par l'expérience Belle au laboratoire KEK (Tsukuba, Japon). Ces deux expériences étudient principalement les désintégrations de hadrons contenant un quark b, ainsi que la violation de CP, c'est-à-dire le non-respect de la symétrie entre matière et antimatière.
Anthony Christopher DavisonAnthony Davison has published on a wide range of topics in statistical theory and methods, and on environmental, biological and financial applications. His main research interests are statistics of extremes, likelihood asymptotics, bootstrap and other resampling methods, and statistical modelling, with a particular focus on the first currently. Statistics of extremes concerns rare events such as storms, high winds and tides, extreme pollution episodes, sporting records, and the like. The subject has a long history, but under the impact of engineering and environmental problems has been an area of intense development in the past 20 years. Davison''s PhD work was in this area, in a project joint between the Departments of Mathematics and Mechanical Engineering at Imperial College, with the aim of modelling potential high exposures to radioactivity due to releases from nuclear installations. The key tools developed, joint with Richard Smith, were regression models for exceedances over high thresholds, which generalized earlier work by hydrologists, and formed the basis of some important later developments. This has led to an ongoing interest in extremes, and in particular their application to environmental and financial data. A major current interest is the development of suitable methods for modelling rare spatio-temporal events, particularly but not only in the context of climate change. Likelihood asymptotics too have undergone very substantial development since 1980. Key tools here have been saddlepoint and related approximations, which can give remarkably accurate approximate distribution and density functions even for very small sample sizes. These approximations can be used for wide classes of parametric models, but also for certain bootstrap and resampling problems. The literature on these methods can seem arcane, but they are potentially widely applicable, and Davison wrote a book joint with Nancy Reid and Alessandra Brazzale intended to promote their use in applications. Bootstrap methods are now used in many areas of application, where they can provide a researcher with accurate inferences tailor-made to the data available, rather than relying on large-sample or other approximations of doubtful validity. The key idea is to replace analytical calculations of biases, variances, confidence and prediction intervals, and other measures of uncertainty with computer simulation from a suitable statistical model. In a nonparametric situation this model consists of the data themselves, and the simulation simply involves resampling from the existing data, while in a parametric case it involves simulation from a suitable parametric model. There is a wide range of possibilities between these extremes, and the book by Davison and Hinkley explores these for many data examples, with the aim of showing how and when resampling methods succeed and why they can fail. He was Editor of Biometrika (2008-2017), Joint Editor of Journal of the Royal Statistical Society, series B (2000-2003), editor of the IMS Lecture Notes Monograph Series (2007), Associate Editor of Biometrika (1987-1999), and Associate Editor of the Brazilian Journal of Probability and Statistics (1987 2006). Currently he on the editorial board of Annual Reviews of Statistics and its Applications. He has served on committees of Royal Statistical Society and of the Institute of Mathematical Statistics. He is an elected Fellow of the American Statistical Assocation and of the Institute of Mathematical Statistics, an elected member of the International Statistical Institute, and a Chartered Statistician. In 2009 he was awarded a laurea honoris causa in Statistical Science by the University of Padova, in 2011 he held a Francqui Chair at Hasselt University, and in 2012 he was Mitchell Lecturer at the University of Glasgow. In 2015 he received the Guy Medal in Silver of the Royal Statistical Society and in 2018 was a Medallion Lecturer of the Institute of Mathematical Statistics.
Aurelio BayAurelio Bay graduated in physics at the University of Lausanne (UNIL) in 1980 and got his PhD degree from the same institution in 1986 for a work on the determination of the axial form factor of the ? meson.
He then went to Lawrence Berkeley Laboratories (LBL), USA as a post doc for two years, where he worked on the TPC/2? Electromagnetic Calorimeter and the SSC/LHC detector. He then came back to Europe and was named Maître Assistant at University of Geneva till 1994, where he started working at the L3 experiment of LEP at CERN.
He was appointed Assistant Professor at the University of Lausanne in 1994 and Full Professor in 1998, continuing working at LEP, LEP2 and LHCb at CERN , and starting a collaboration at BELLE experiment at KEK, Tsukuba (Japan).
At the University of Lausanne he was Director of the Institute of High Energy Physics, Deputy Director of the Physics Department and Deputy of the Dean of the Faculty of Sciences.
In 2003, following the merge of UNIL physics department into the EPFL School of Basic Sciences, he was appointed Full Professor at Ecole Polytechnique Fédérale de Lausanne (EPFL), and Director of the EPFL Laboratory of High Energy Physics.
Ali H. SayedAli H. Sayed est doyen de la Faculté des sciences et techniques de l’ingénieur (STI) de l'EPFL, en Suisse, où il dirige également le laboratoire de systèmes adaptatifs. Il a également été professeur émérite et président du département d'ingénierie électrique de l'UCLA. Il est reconnu comme un chercheur hautement cité et est membre de la US National Academy of Engineering. Il est également membre de l'Académie mondiale des sciences et a été président de l'IEEE Signal Processing Society en 2018 et 2019.
Le professeur Sayed est auteur et co-auteur de plus de 570 publications et de six monographies. Ses recherches portent sur plusieurs domaines, dont les théories d'adaptation et d'apprentissage, les sciences des données et des réseaux, l'inférence statistique et les systèmes multi-agents, entre autres.
Ses travaux ont été récompensés par plusieurs prix importants, notamment le prix Fourier de l'IEEE (2022), le prix de la société Norbert Wiener (2020) et le prix de l'éducation (2015) de la société de traitement des signaux de l'IEEE, le prix Papoulis (2014) de l'Association européenne de traitement des signaux, le Meritorious Service Award (2013) et le prix de la réalisation technique (2012) de la société de traitement des signaux de l'IEEE, le prix Terman (2005) de la société américaine de formation des ingénieurs, le prix de conférencier émérite (2005) de la société de traitement des signaux de l'IEEE, le prix Koweït (2003) et le prix Donald G. Fink (1996) de l'IEEE. Ses publications ont été récompensées par plusieurs prix du meilleur article de l'IEEE (2002, 2005, 2012, 2014) et de l'EURASIP (2015). Pour finir, Ali H. Sayed est aussi membre de l'IEEE, d'EURASIP et de l'American Association for the Advancement of Science (AAAS), l'éditeur de la revue Science.
