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.
Johan AuwerxJohan Auwerx is Professor at the École Polytechnique Fédérale in Lausanne, Switzerland, where he occupies the Nestle Chair in Energy Metabolism. Dr. Auwerx has been using molecular physiology and systems genetics to understand metabolism in health, aging and disease. Much of his work focused on understanding how diet, exercise and hormones control metabolism through changing the expression of genes by altering the activity of transcription factors and their associated cofactors. His work was instrumental for the development of agonists of nuclear receptors - a particular class of transcription factors - into drugs, which now are used to treat high blood lipid levels, fatty liver, and type 2 diabetes. Dr. Auwerx was amongst the first to recognize that transcriptional cofactors, which fine-tune the activity of transcription factors, act as energy sensors/effectors that influence metabolic homeostasis. His research validated these cofactors as novel targets to treat metabolic diseases, and spurred the clinical use of natural compounds, such as resveratrol, as modulators of these cofactor pathways.
Johan Auwerx was elected as a member of EMBO in 2003 and is the recipient of a dozen of international scientific prizes, including the Danone International Nutrition Award, the Oskar Minkowski Prize, and the Morgagni Gold Medal. His work is highly cited by his peers with a h-factor of over 100. He is an editorial board member of several journals, including Cell Metabolism, Molecular Systems Biology, The EMBO Journal, Journal of Cell Biology, Cell, and Science. Dr. Auwerx co-founded a handful of biotech companies, including Carex, PhytoDia, and most recently Mitobridge, and has served on several scientific advisory boards.
Dr. Auwerx received both his MD and PhD in Molecular Endocrinology at the Katholieke Universiteit in Leuven, Belgium. He was a post-doctoral research fellow in the Departments of Medicine and Genetics of the University of Washington in Seattle.