Person

Mathieu Ribordy

This person is no longer with EPFL

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Related publications (79)

Please note that this is not a complete list of this person’s publications. It includes only semantically relevant works. For a full list, please refer to Infoscience.

The IceProd framework: Distributed data processing for the IceCube neutrino observatory

Xiaoxia Bai, Mathieu Ribordy, Shirit Cohen, Mark Tobin, Matthias Wolf, Sofia Vallecorsa

IceCube is a one-gigaton instrument located at the geographic South Pole, designed to detect cosmic neutrinos, identify the particle nature of dark matter, and study high-energy neutrinos themselves. Simulation of the IceCube detector and processing of dat ...

Academic Press Inc Elsevier Science2015

Search for neutrino-induced particle showers with IceCube-40

Xiaoxia Bai, Mathieu Ribordy, Shirit Cohen, Mark Tobin, Matthias Wolf, Sofia Vallecorsa

We report on the search for neutrino-induced particle showers, so-called cascades, in the IceCube-40 detector. The data for this search were collected between April 2008 and May 2009 when the first 40 IceCube strings were deployed and operational. Three co ...

American Physical Society2014

IceCube sensitivity for low-energy neutrinos from nearby supernovae (vol 535, pg A109, 2011)

Xiaoxia Bai, Mathieu Ribordy, Shirit Cohen, Matthias Wolf

Edp Sciences S A2014

People doing similar research (32)

Roberto 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).

Alessandro Degano

Xin Chen