Pietro Vischia

Pietro Vischia is Ramón y Cajal senior researcher at the Universidad de Oviedo and ICTEA (Spain) and Adjunct Professor at IITM. Graduated in 2016 from IST. He is the coordinator of the MODE (Machine-Learning-Optimized Design of Experiments) Collaboration, and the Machine Learning Coordinador (since 2024) and a member of the Statistics advisory Committee (since 2015) of the CMS Experiment at CERN. His research focuses  on experiment design and robust algorithms for multiobjective optimization in high-dimensional spaces, assisted by automatic differentiation and neuromorphic and quantum algorithms. Within CMS, he focuses on plugging inductive bias in machine learning algorithms for particle physics (including the 2018 observation of the ttH process). He has recently published a brief report on good statistical practices for data analysis, including machine learning techniques. Vischia routinely teaches data analysis methods and AI from bachelor to postdoc level. More info on Pietro Vischia can be found here. 

Lara Lloret Iglesias

Lara Lloret is a tenured scientist at the Spanish Research Council. She completed her PhD in high-energy physics, contributing to the discovery of the Higgs boson within the CMS experiment at CERN. Her expertise in advanced computational methods, including deep learning, uncertainty quantification, and model interpretability, led her to expand her research into other domains, such as medicine, where she applies AI-driven techniques. She has presented her work at international conferences and published in high-impact journals. She is a member of the Spanish Research Council's committee responsible for shaping the institution's future in artificial intelligence. Additionally, she was responsible for CERN's data preservation portal for several years. She currently directs a master's program in data science, organized by the University of Cantabria and the Menéndez Pelayo International University. More info here. 

Miriam Cobo Cano

Miriam Cobo is a Physicist and 3rd year PhD candidate at the Institute of Physics of Cantabria. Her research focuses on learning more robust representations of data to increase generalizability and interpretability of deep learning models in medical imaging. Her work emphasizes faithful evaluation of deep learning as a key step towards trustworthy clinical translation. She has taught both experimental (Laboratory of nuclear physics, 3rd course) and theoretical (Astronomy, 1st course) courses in BSc of Physics at University of Cantabria, Spain. More info here.