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).
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.
Radhakrishna AchantaExpertise
Image Processing, Computer Vision, and Machine Learning
Radhakrishna Achanta has a PhD in computer science from EPFL, an MSc from NUS, Singapore, and a BEng from India. He has over 20 years of industrial and academic experience in the capacities of an individual contributor as well as a manager. His domains of expertise are Computer Vision, Image Processing, and Machine Learning. He has authored over 50 peer reviewed papers and patents, which have more than 23’000 citations. He has co-founded 4 st
Alessandro PauTeaching & PhD PhD Students Peizheng Zhang, Jean-Pierre Thomas Svantner, Cristina Venturini, Paul Véronique Guillaume Devianne Courses Control and Operation of Tokamaks PHYS-748 This course treats the main issues in operation and control of a tokamak. Control-oriented models are derived and controllers are designed using techniques from modern control theory. Operational limits are discussed as well as state-of-the-art research questions for future reactors.
Olivier SauterTeaching & PhD PhD Students Cassandre Ekta Contré, Antonia Frank, Peizheng Zhang, Jean-Pierre Thomas Svantner, Cristina Venturini, Cosmas Quirin Lorenz Heiss, Daniel Perales Rios, Viacheslav Bochkarev, Jan Jakub Frybes, Paul Véronique Guillaume Devianne Past EPFL PhD Students Clemente Angioni, Petri Nikkola, Alberto Bottino, Andrei Martynov, Emiliano Fable, Costanza Zucca, Federico Felici, Dávid Wágner, Doohyun Kim, Anna Teplukhina, Mengdi Kong, Francesco Carpanese, Simon Van Mulders, Stefano Marchioni, F
Jean-Louis ScartezziniDirector of EPFL Solar Energy and Building Physics Laboratory (1994-present); Founder & Director of ENAC Institute of Infrastructures, Resources and Environment (2002-2009); Founder & Director of EPFL Doctoral Program in Environment (2002-2009); Co-Director of EPFL Institute of Building Technology (1994-1997); Associate Professor of Building Physics at EPFL (1994-1997); Associate Professor of Building Physics at University of Geneva (1990-1997); Group Leader & Research Fellow at the EPFL Solar Energy Research Group (1981-1989); Research Fellow at the Applied Geophysics Institute of University of Lausanne (1980-1981).
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.
