Person

Maren Tabea Meinhard

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

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).

Yiming Li

Xin Chen

Maren Tabea Meinhard

Graph Chatbot

Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

Search for Higgs boson pairs decaying to WWWW, WW*ττ, and ττττ in proton-proton collisions at √s=13 TeV

Measurements of Higgs boson production in the decay channel with a pair of τ leptons in proton-proton collisions at √s=13 TeV

Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

Search for Higgs boson pairs decaying to WWWW, WW*ττ, and ττττ in proton-proton collisions at √s=13 TeV

Measurements of Higgs boson production in the decay channel with a pair of τ leptons in proton-proton collisions at √s=13 TeV

Maren Tabea Meinhard

Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

Search for Higgs boson pairs decaying to WW*WW*, WW*ττ, and ττττ in proton-proton collisions at √s=13 TeV

Measurements of Higgs boson production in the decay channel with a pair of τ leptons in proton-proton collisions at √s=13 TeV

Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

Search for Higgs boson pairs decaying to WW*WW*, WW*ττ, and ττττ in proton-proton collisions at √s=13 TeV

Measurements of Higgs boson production in the decay channel with a pair of τ leptons in proton-proton collisions at √s=13 TeV

Search for Higgs boson pairs decaying to WWWW, WW*ττ, and ττττ in proton-proton collisions at √s=13 TeV

Search for Higgs boson pairs decaying to WWWW, WW*ττ, and ττττ in proton-proton collisions at √s=13 TeV