Roberto Castello
Data Scientist
EPFL SDSC
INN 218 (Bâtiment INN)
Station 14
CH-1015 Lausanne
+41 21 693 87 94
Office: INN 218
Office: INN 218
EPFL
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VPA
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VPA-AVP-CP
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SDSC
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SDSC-GE
Web site: Web site: https://www.datascience.ch
Fields of expertise
Applied Machine Learning and Big Data analytics for the built environment and renewable energy, Urban and building physics simulation, Statistical methods, Geographic Information System
Biography
Roberto Castello is a Principal Data Scientist at the EPFL Swiss Data Science Center (SDSC) since September 2021.
He received his MSc in Particle Physics and PhD in Physics and Astrophysics from the University of Torino. He worked as research fellow for the Belgian National Fund for Scientific Research (FNRS) and later at the CERN Experimental Physics Department as a senior research fellow and data scientist.
He received his MSc in Particle Physics and PhD in Physics and Astrophysics from the University of Torino. He worked as research fellow for the Belgian National Fund for Scientific Research (FNRS) and later at the CERN Experimental Physics Department as a senior research fellow and data scientist.
In 2018 he joined the EPFL Solar Energy and Building Physics Laboratory (LESO-PB) as a senior scientist and group leader to work on data mining and Machine Learning for the built environment and renewable energy. He has started and led several and diverse collaborations with Swiss industry partners, Federal Offices and startups.
He is member of the Geneva 2030 Ecosystem, a network of individuals from international organizations, NGOs, academia and private sector, working together towards the realization of the United Nations Sustainable Development Goals.
Current work
Currently working on developing algorithms for the estimation of renewables energy potential (solar, wind and geothermal) for the built environment using big data (NRP75 Big Data project of Swiss National Science Foundation).
Publications
Infoscience publications
A machine learning-assisted building electricity consumption profiling for anomaly detection
2020-12-14. International Conference on Applied Energy (ICAE 2020), Virtual Conference, December 1-10, 2020.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.Spatio-Temporal Relationship between Land Cover and Land Surface Temperature in Urban Areas: A Case Study in Geneva and Paris
ISPRS International Journal of Geo-Information. 2020. DOI : 10.3390/ijgi9100593.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.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.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.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.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.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.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.Other publications
Selected publications
- 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
Supervised students
- Mr Lorenzo Donadio (EPFL and Eindhoven University of Technology), Temporal hierarchical forecasting of energy consumption in office buildings
- Mr Romain Sibuet (HES-SO Geneva), Optimization of a hybrid solar-geothermal energy system potential at building scale
- Ms Xu Ge (Technical University of Denmark - DTU), Spatio-temporal relationship between land cover and Land Surface Temperature in urban areas
- Ms Dan Chai (EPFL), Automatic detection of rooftop solar installations in the built environment and its validation on the EPFL campus
- Mr Giovanni Mori (University of Bolzano), Geospatial analysis and optimization of the incoming and stored CO2 emissions within the EPFL campus