Team Company: SUPSI

Charly Empereur-mot is a bioinformatics scientist specializing in the design and development of new molecular modeling and machine learning techniques for the characterization and understanding of complex (bio)chemical systems. He holds a Ph. D. in Bioinformatics from the National Conservatory of Arts and Crafts of Paris. He is a permanent researcher in the Computational Materials Science laboratory of the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). Dr. Empereur-mot has over 10 years of research experience in biophysics, nanomaterials design, drug discovery and computational chemistry, with expertise in machine learning and a history of collaborating with experimental groups in academic and industry settings.

Annalisa Cardellini is a computational materialsscientist with a Ph.D. in Energy Engineeringfrom Politecnico di Torino. Her researchintegrates multiscale molecular modelling,enhanced sampling and data-driven techniquesto unravel the dynamic behavior of complex softmatter systems. Over the years, Dr. Cardellinihas investigated the aggregation and interfacialproperties of coated nanoparticles, thearchitecture of supramolecular polymers, andthe assembly of cyclic peptides into functionalmonolayers. Currently based at SUPSI(University of Applied Sciences and Arts ofSouthern Switzerland), she applies herexpertise to drive the materials design for energy, environmental, and biomedicaltechnologies.

Daniela Polino is a chemical engineer specializing in molecular modeling of complex chemical systems for energy and industrial applications. She holds a Ph.D. in Industrial Chemistry and Chemical Engineering from Politecnico di Milano. Her doctoral research focused on developing methodologies for accurate estimation of reaction rate coefficients.

Currently, she serves as the head of the Computational Materials Science Laboratory at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI) in Lugano. Her research integrates ab initio methods, molecular dynamics, and machine learning to study catalytic processes, hydrogen production, and sustainable fuels.