Fields of expertise
Renewable energy, Experimental Physics, Statistics, Big Data, Applied Machine Learning, Geographic Information System
Roberto Castello is a scientific collaborator 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) and PhD (2010) in Physics 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 fellow scientist. He is primary author of more than 20 peer-reviewed publications and he presented at several international conferences in the high energy physics domain. Together with his collaborators, he was awarded the European Physical Society prize (2013) for the discovery of the Higgs boson.
In 2018 he joined the Solar Energy and Building Physics Laboratory (LESO-PB) to work on computational techniques for urban sustainability. His main research interests are: spatio-temporal modeling of renewable energy potential, energy efficiency and optimization methods, Machine Learning and data mining for monitoring and classification of the built environment.
He co-leads the group of Urban Data Mining and Intelligence 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 (Applied Machine Learning Days 2019 and 2020, CISBAT2019 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).
S. Roquette : Detecting solar rooftop photovoltaic panels in aerial images using neural networks: a transfer learning approach ; 2020-06-19.
A. Walch; R. Castello; N. Mohajeri; J.-L. Scartezzini : Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty; Applied Energy. 2020-01-28. DOI : 10.1016/j.apenergy.2019.114404.
A. Walch; R. Castello; N. Mohajeri; J.-L. Scartezzini : A fast machine learning model for large-scale estimation of annual solar irradiation on rooftops. 2020. SHC 2019/SWC 2019. ISES Solar World Congress, Santiago, Chile, November 3-7, 2019. DOI : 10.18086/swc.2019.45.12.
D. Mauree; R. Castello; G. Mancini; T. Nutta; T. Zhang et al. : Wind profile prediction in an urban canyon: a machine learning approach. 2019-11-20. CISBAT 2019 | Climate Resilient Cities – Energy Efficiency & Renewables in the Digital Era, Lausanne, Switzerland, September 4-6, 2019. DOI : 10.1088/1742-6596/1343/1/012047.
G. Mori; S. Coccolo; R. Castello; J.-L. Scartezzini : Geospatial analysis and optimization of the incoming and stored CO2 emissions within the EPFL campus. 2019-11-20. CISBAT 2019 | Climate Resilient Cities – Energy Efficiency & Renewables in the Digital Era, Lausanne, Switzerland, 4-6 September 2019. DOI : 10.1088/1742-6596/1343/1/012118.
R. Castello; S. Roquette; M. Esguerra; A. Guerra; J.-L. Scartezzini : Deep learning in the built environment: automatic detection of rooftop solar panels using Convolutional Neural Networks. 2019-11-20. CISBAT 2019 | Climate Resilient Cities – Energy Efficiency & Renewables in the Digital Era, Lausanne, Switzerland, 4–6 September 2019. DOI : 10.1088/1742-6596/1343/1/012034.
T. Q. Nguyen; D. Weitekamp; D. Anderson; R. Castello; O. Cerri et al. : Topology classification with deep learning to improve real-time event selection at the LHC; Computing and Software for Big Science. 2019-08-31. DOI : 10.1007/s41781-019-0028-1.
A. Walch; R. Castello; N. Mohajeri; F. Guignard; M. Kanevski et al. : Spatio-temporal modelling and uncertainty estimation of hourly global solar irradiance using Extreme Learning Machines. 2019-03-15. 10 th International Conference on Applied Energy ( ICAE2018), Hong Kong , China, August 22- 25, 2018. p. 6378-6383. DOI : 10.1016/j.egypro.2019.01.219.
E. Honeck; R. Castello; B. Chatenoux; J.-P. Richard; A. Lehmann et al. : From a Vegetation Index to a Sustainable Development Goal Indicator: Forest Trend Monitoring Using Three Decades of Earth Observations across Switzerland; ISPRS International Journal of Geo-Information. 2018-11-24. DOI : 10.3390/ijgi7120455.
- R. Castello and CMS collaborators, Search for new physics in events with two low momentum opposite-sign leptons and missing transverse energy at 13 TeV, Physics Letters B, 2018
- R. Castello and CMS collaborators, Search for narrow resonances in dilepton mass spectra in proton-proton collisions at 13 TeV and combination with 8 TeV data, Physics Letters B, 2017
- R. Castello and CMS collaborators, Search for neutral resonances decaying into a Z boson and a pair of b jets or tau leptons, Physics Letters B, 2016
- R. Castello and CMS collaborators, Alignment of the CMS tracker with LHC and cosmic ray data, Journal of Instrumentation, 2014
- R. Castello and CMS collaborators, Measurement of the production cross sections for a Z boson and one or more b jets in pp collisions at 7 TeV, Journal of High Energy Physics, 2014
- R. Castello and CMS collaborators, Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC, Physics Letters B, 2012 - Science Breakthrough of the Year 2012
- R. Castello and CMS collaborators, Performance of CMS muon reconstruction in pp collision events, Journal of Instrumentation, 2012
- R. Castello and CMS collaborators, Alignment of the CMS Silicon Tracker during Commissioning with Cosmic Rays, Journal of Instrumentation, 2010 (PhD thesis work)
Teaching & PhD
- AR-241 Building technology III - Module on Building Physics (teaching assistant)
- AR-242 Building technology IV - Module on Building Physics (teaching assistant)