Federico AmatoI have an MSc in Engineering and a Ph.D. in Sustainable Development and Innovation Engineering. After a three-year Postdoc in Environmental Data Mining, in 2021 I joined the Swiss Data Science Centre, a joint venture between the polytechnical schools of Lausanne (EPFL) and Zurich (ETH) having as mission the acceleration of the digital transformation of the academic community and the industrial sector, putting to work Artificial Intelligence and Machine Learning and facilitating the multidisciplinary exchange of data and knowledge.
Over the years I worked on the development of methodological tools to mine and model big spatiotemporal datasets. I extensively applied such methods to study the interaction between the spatial and temporal components of environmental and geographical phenomena, and their consequences in terms of spatial planning and urban geography. My academic activity has focused on the analysis of land-use dynamics and their relationship with climate, pollution, natural hazards, and other social/economic phenomena. I have deep competencies in applied statistics, machine learning, geocomputation, spatial statistics, urban modeling, and remote sensing. I am also an expert in European Project development and implementation.
Alessandro NestiI joined the Swiss Data Science Center at EPFL in March 2019 as a data scientist focused on industry collaborations. Our mission is to support corporates in leveraging the power of their data by adopting analytical approaches and data-centric solutions. My background is in biomedical engineering and I hold a PhD in neuroscience from the University of Tübingen. Before joining the center, I worked as a postdoc at the Max Planck Institute for Biological Cybernetics, at the EPFL Laboratory of Cognitive Neuroscience in Geneva, and as data scientist for a private ecommerce company.
Guillaume Romain ObozinskiGuillaume Obozinski est Chef Data Scientist Adjoint au Swiss Data Science Center. Il a reçu le diplôme de doctorat en Statistiques de l'Université de Californie à Berkeley en 2009. Post-doc puis chercheur dans les équipes Willow et Sierra à l'INRIA / Ecole Normale Supérieure à Paris jusqu'en 2012, il a été chercheur à l'Ecole des Ponts ParisTech à Paris jusqu'en 2018. Ses domaines d'expertise sont principalement en apprentissage automatique, en statistique et en optimisation. Il a travaillé au fil du temps sur les méthodes d'apprentissage parcimonieux, les techniques d'optimisation pour l'apprentissage, les modèles graphiques probabilistes, l'apprentissage relationnel et les représentations vectorielles sémantiques. Il est intéressé par l'application de ces techniques à tous les domaines des data sciences, depuis la biologie computationnelle à l'analyse automatique d'images.
