Daniela Paredes

Daniela Paredes is an experienced data scientist with a strong background in physics and data analysis. Currently at CERN since August 2015, Daniela has coordinated projects that significantly improved data extraction and analysis techniques. At The University of Hong Kong, Daniela has continued to apply expertise in machine learning, enhancing event classification accuracy in Large Hadron Collider (LHC) data. Prior experience at Yale University included developing machine learning models that increased sensitivity in event classification. Daniela's career began at Aristotle University of Thessaloniki, where foundational work on data preprocessing and hypothesis testing contributed to the search for new particles in LHC data. Educational accomplishments include a PhD in Physics from Université Blaise Pascal and a Master’s degree from Universitat de les Illes Balears, complemented by a Bachelor's degree from Universidad de los Andes (VE).