Lars Kai Hansen has MSc and PhD degrees in physics from University of Copenhagen. Since 1990 he has been with the Technical University of Denmark, where he currently heads the Section for Cognitive Systems. He has published more than 300 contributions on machine learning, signal processing, and applications in AI and cognitive systems. His research has been generously funded by the Danish Research Councils and private foundations, the European Union, and the US National Institutes of Health. He has made seminal contributions to machine learning including the introduction of ensemble methods('90) and to functional neuroimaging including the first brain state decoding work based on PET('94) and fMRI('97). In the context of neuroimaging he has developed a suite of methods for visualizing machine learning models and quantification of uncertainty. In 2011 he was elected “Catedra de Excelencia” at UC3M Madrid, Spain.
Prof. Dr. Laura Leal-Taixé is a tenure-track professor (W2) at the Technical University of Munich, leading the Dynamic Vision and Learning group. Before that, she spent two years as a postdoctoral researcher at ETH Zurich, Switzerland, and a year as a senior postdoctoral researcher in the Computer Vision Group at the Technical University in Munich. She obtained her PhD from the Leibniz University of Hannover in Germany, spending a year as a visiting scholar at the University of Michigan, Ann Arbor, USA. She pursued B.Sc. and M.Sc. in Telecommunications Engineering at the Technical University of Catalonia (UPC) in her native city of Barcelona. She went to Boston, USA to do her Masters Thesis at Northeastern University with a fellowship from the Vodafone foundation. She is a recipient of the Sofja Kovalevskaja Award of 1.65 million euros for her project socialMaps.
Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit, and director of the Centre for Computational Statistics and Machine Learning (CSML) at UCL. He received degrees in Physics and Systems Engineering from the Australian National University, and a PhD with Microsoft Research and the Signal Processing and Communications Laboratory at the University of Cambridge. He previously worked at the MPI for Biological Cybernetics, and at the Machine Learning Department, Carnegie Mellon University.
Arthur's recent research interests in machine learning include the design and training of generative models, both implicit (e.g. GANs) and explicit (high/infinite dimensional exponential family models and energy-based models), nonparametric hypothesis testing, survival analysis, causality, and kernel methods.
Elsa D. Angelini is the co-lead of the Data Science Group in Institute of Translational Medicine and Therapeutics (ITMAT) within NIHR Imperial Biomedical Research Centre (BRC). She is also the co-director of the Heffner Biomedical Imaging Laboratory at Columbia University and is affiliated with the Department of Data-Signal-lmage at Telecom Paris (Associate Professor / on leave). She has co-authored over 140 peer-reviewed articles and has graduated 19 PhD students.
She is a Senior Member of IEEE and was the Vice-President for Technical Activities for IEEE EMBS (2017-19).
Roland Vollgraf is the Head of Zalando Research and obtained his Ph.D. at the Technical University of Berlin in Machine Learning and Statistical Signal Processing. Roland was integral to the establishment of Zalando Research and has been with Zalando since 2013. He previously worked as Head of Research for GA Financial Solutions GmbH and conducted the development of asset risk models and quantitative trading strategies. Current research interests include Deep Learning and Large Scale Bayesian Inference.