Guillaume Romain ObozinskiGuillaume Obozinski is Deputy Chief Data Scientist at the Swiss Data Science Center. He graduated with a PhD in Statistics from UC Berkeley in 2009. He did his postdoc and held until 2012 a researcher position in the Willow and Sierra teams at INRIA and Ecole Normale Supérieure in Paris. He was then Research Faculty at Ecole des Ponts ParisTech until 2018. Guillaume has broad interests in statistics and machine learning and worked over time on sparse modeling, optimization for large scale learning, graphical models, relational learning and semantic embeddings, with applications in various domains from computational biology to computer vision.
Mathieu SalzmannI am a Senior Researcher at EPFL-CVLab, and, since May 2020, an Artificial Intelligence Engineer at ClearSpace (50%). Previously, I was a Senior Researcher and Research Leader in NICTA's computer vision research group. Prior to this, from Sept. 2010 to Jan 2012, I was a Research Assistant Professor at TTI-Chicago, and, from Feb. 2009 to Aug. 2010, a postdoctoral fellow at ICSI and EECS at UC Berkeley under the supervision of Prof. Trevor Darrell. I obtained my PhD in Jan. 2009 from EPFL under the supervision of Prof. Pascal Fua.
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.
Silvia Alessandra QuarteroniSilvia holds an M.Sc. in Computer Science from EPFL and a Ph.D. in Computer Science from the University of York, UK. She has been a senior research fellow at Politecnico di Milano and later at the University of Trento, Italy, where she had the chance to work on Marie Curie and ERC projects relating to natural language processing. From 2012 to 2019, she was a Senior Manager and NLP expert at ELCA Informatique Switzerland, whose AI department she helped to create and expand. Silvia has joined the Swiss Data Science Center in 2019 as a Principal Data Scientist for industry collaborations with the aim of helping the industry benefit from the latest innovation in data science.
