Olivier SchneiderAfter his thesis defense in particle physics in 1989 at University of Lausanne, Olivier Schneider joins LBL, the Lawrence Berkeley Laboratory (California), to work on the CDF experiment at the Tevatron in Fermilab (Illinois), first as a research fellow supported by the Swiss National Science Foundation, and later as a post-doc at LBL. He participates in the construction and commissioning of the first silicon vertex detector to operate successfully at a hadron collider; this detector enabled the discovery of the sixth quark, named "top". Since 1994, he comes back to Europe and participates in the ALEPH experiment at CERN's Large Electron-Positron Collider, as CERN fellow and then as CERN scientific staff. He specializes in heavy flavour physics. In 1998, he becomes associate professor at University of Lausanne, then extraordinary professor at the Swiss Institute of Technology Lausanne (EPFL) in 2003, and finally full professor at EPFL in 2010. Having worked since 1997 on the preparation of the LHCb experiment at CERN's Large Hadron Collider, which started operation in 2009, he is now analyzing the first data. He also contributes since 2001 to the exploitation of the data recorded at the Belle experiment (KEK laboratory, Tsukuba, Japan). These two experiments study mainly the decays of hadrons containing a b quark, as well CP violation, i.e. the non-invariance under the symmetry between matter and antimatter.
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 is Dean of Engineering at EPFL, Switzerland, where he also leads the Adaptive Systems Laboratory. He has also served as Distinguished Professor and Chairman of Electrical Engineering at UCLA. He is recognized as a Highly Cited Researcher and is a member of the US National Academy of Engineering. He is also a member of the World Academy of Sciences and served as President of the IEEE Signal Processing Society during 2018 and 2019.
Dr. Sayed is an author/co-author of over 570 scholarly publications and six books. His research involves several areas
including adaptation and learning theories, data and network sciences, statistical inference, and multiagent systems.
His work has been recognized with several major awards including the 2022 IEEE Fourier Award, the 2020 Norbert Wiener Society Award and the 2015 Education Award from the IEEE Signal Processing Society, the 2014 Papoulis Award from the European Association for Signal Processing, the 2013 Meritorious Service Award and the 2012 Technical Achievement Award from the IEEE Signal Processing Society, the 2005 Terman Award from the American Society for Engineering Education, the 2005 Distinguished Lecturer from the IEEE Signal Processing Society, the 2003 Kuwait Prize, and the 1996 IEEE Donald G. Fink Prize. His publications have been awarded several Best Paper Awards from the IEEE (2002, 2005, 2012, 2014) and EURASIP (2015). He is a Fellow of IEEE, EURASIP, and the American Association for the Advancement of Science (AAAS); the publisher of the journal Science.
