Yves RevazYves Revaz est maître d'enseignement et de recherche au laboratoire d'astrophysique (LASTRO) de l'EPFL. Après des études à l'EPFL, il a soutenu une thèse de doctorat intitulée: "Dynamique des régions externes des galaxies spirales et contraintes sur la matière noire" à l'Observatoire de Genève, sous la direction du Prof. Daniel Pfenniger. Il a ensuite travaillé à l'Observatoire de Paris, au LERMA (Laboratoire d'Etudes du Rayonnement et de la Matière en Astrophysique et Atmosphères) sous la supervision de la Prof. Françoise Combes et s'est intéressé aux écoulement froids dans les amas de galaxies. Il a rejoint le LASTRO en 2007, où, en collaboration avec la Prof. Pascale Jablonka, il a développé un nouveau code TreePM/SPH chemo-dynamique appelé GEAR, dans le but d'étudier l'évolution chimique des galaxies. Sa recherche se focalise actuellement sur l'évolution des galaxies naines et leur lien avec la cosmologie. Yves Revaz et aussi l'auteur de pNbody, une librairie python parallélisée dédiée à l'analyse de larges systèmes N-corps.
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).
