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.
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.
Bernard MoretBernard M.E. Moret was born in Vevey, Switzerland, received baccalauréats in Latin-Greek and Latin-Mathematics, then did a Diploma in Electrical Engineering at EPFL. After working for 2 years for Omega and Swiss Timing on the development of real-time OS for sports applications, he left for the US. He received his PhD in Electrical Engineering from the U. of Tennessee in 1980 and joined the Department of Computer Science at the University of New Mexico (UNM) that fall. He served as Chairman of the department from 1991 till 1993 and eventually retired in summer 2006 to join the School of Computer and Communication Sciences at EPFL. (You can read about his work at UNM on his (archived) personal and laboratory web pages at UNM.) He was appointed group leader for phylogenetics at the Swiss Institute for Bioinformatics (SIB). From 2009 until his retirement, he was also in charge of the BS and MS programs in Computer Science and Associate Dean for Education. He founded the ACM Journal of Experimental Algorithmics (JEA) and served as its Editor-in-Chief for 7 years; he also helped found the IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), where he served as Associate Editor until 2008. He founded the annual Workshop on Algorithms in Bioinformatics (WABI) and chairs its steering committee, and he serves on the steering committee of the Workshop on Algorithm Engineering and Experiments (ALENEX). Until summer 2008, he chaired the Biodata Management and Analysis (BDMA) study section of the US National Institutes of Health (NIH); now he is a charter member of the NIH College of Reviewers. He led a team of over 50 biologists, computer scientists, and mathematicians in the CIPRES (Cyber Infrastructure for Phylogenetic Research) project, funded by the US National Science Foundation (NSF) for US$ 12 million over 5 years. He has published nearly 150 papers in computational biology, under funding from the US NSF, the Alfred P. Sloan foundation, the IBM Corporation, the US NIH, the Swiss NSF, and SystemsX.ch. He is a Fellow of the ISCB (International Society for Computational Biology). His Erdös number is 2 and (as of 2020) his h-index is 48.
Volkan CevherVolkan Cevher received the B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara, Turkey, in 1999 and the Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in Atlanta, GA in 2005. He was a Research Scientist with the University of Maryland, College Park from 2006-2007 and also with Rice University in Houston, TX, from 2008-2009. Currently, he is an Associate Professor at the Swiss Federal Institute of Technology Lausanne and a Faculty Fellow in the Electrical and Computer Engineering Department at Rice University. His research interests include machine learning, signal processing theory, optimization theory and methods, and information theory. Dr. Cevher is an ELLIS fellow and was the recipient of the Google Faculty Research award in 2018, the IEEE Signal Processing Society Best Paper Award in 2016, a Best Paper Award at CAMSAP in 2015, a Best Paper Award at SPARS in 2009, and an ERC CG in 2016 as well as an ERC StG in 2011.
Jean-Pierre HubauxJean-Pierre Hubaux is a full professor at EPFL and head of the Laboratory for Data Security. Through his research, he contributes to laying the foundations and developing the tools for protecting privacy in today’s hyper-connected world. He has pioneered the areas of privacy and security in mobile/wireless networks and in personalized health. He is the academic director of the Center for Digital Trust (C4DT). He leads the Data Protection in Personalized Health (DPPH) project funded by the ETH Council and is a co-chair of the Data Security Work Stream of the Global Alliance for Genomics and Health (GA4GH). From 2008 to 2019 he was one of the seven commissioners of the Swiss FCC. He is a Fellow of both IEEE (2008) and ACM (2010). Recent awards: two of his papers obtained distinctions at the IEEE Symposium on Security and Privacy in 2015 and 2018. He is among the most cited researchers in privacy protection and in information security. Spoken languages: French, English, German, Italian
