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.
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.
Roberto CastelloRoberto Castello is a senior scientist and group leader at the EPFL Laboratory of Solar Energy and Building Physics. Physicist by training, he has extensive experience in collecting, classifying and interpreting large datasets using advanced data mining techniques and statistical methods. He received his MSc (2007) in Particle Physics and PhD (2010) in Physics and Astrophysics from the University of Torino. He worked as a postdoctoral researcher at the Belgian National Research Fund (2011-2014) and at the CERN Experimental Physics Department (2015-2017) as a research fellow and data scientist. He is primary author of more than 20 peer-reviewed publications and he presented at major international conferences in the high energy physics domain.
In 2018 he joined the Solar Energy and Building Physics Laboratory (LESO-PB) to work on data mining and Machine Learning techniques for the built environment and renewable energy. His main research interests are: spatio-temporal modeling of renewable energy potential, energy consumption forecasting techniques, anomaly detection, and computer vision techniques for automated classification in the built environment.
He leads the group of Urban Data Mining, Intelligence and Simulation at LESO-PB and he is a member of the NRP75 Big Data project (HyEnergy) of the Swiss National Science Foundation. He is a member of the Swiss Competence Centre for Energy Research (SCCER) and deputy leader of the working group on Leveraging Ubiquitous Energy Data. He has served as a scientific committee member, workshop organizer and speaker at international conferences (ICAE 2020, Applied Machine Learning Days 2019 and 2020, CISBAT 2019 and 2021 and SDS2020).
Since 2017 he is member of the Geneva 2030 Ecosystem network, promoting the United Nations agenda towards the realization of the Sustainable Development Goals (SDGs).
