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Guillermo Rodríguez Lázaro has a diverse work experience in the fields of informatics, biotech, and research. Guillermo began their career as a Graduate Teaching Assistant at Universitat de Barcelona, where they also pursued their PhD in Red Blood Cell mechanics. During this time, they developed a lattice-Boltzmann algorithm and membrane elasticity theory to study microfluidics of complex fluids.

Following their PhD, Guillermo worked as a Postdoctoral Fellow at Brandeis University - Graduate Professional Studies. Here, they conducted research on the self-assembly and interactions of viral capsids and biological membranes. Guillermo utilized molecular dynamics, markov state models, and other computational techniques to develop mechanistic models and gain insights into protein allosteric changes, self-assembly in biological systems, vesicle and nanoparticle assembly, and membrane elasticity.

Currently, Guillermo is working at Aplicaciones en Informática Avanzada, where they hold the position of Head of ML/Biotech Area. Guillermo is responsible for leading and managing projects in the field of machine learning and biotechnology. Prior to this role, they served as a Senior Data Scientist at the same company.

Overall, Guillermo's work experience demonstrates their expertise in computational techniques, model development, and research in various scientific domains, including informatics, biotech, and biophysics.

Guillermo Rodríguez Lázaro holds a Master's degree in Biophysics from Universitat de Barcelona, which they obtained from 2009 to 2010. Prior to that, they completed their Bachelor's degree in Physics from Universidad Complutense de Madrid between 2004 and 2009. In addition to their formal education, Guillermo also obtained several certifications, including the Scrum Foundation Professional Certificate (SFPC) from CertiProf in June 2020. Furthermore, they have completed various online courses in topics such as SQL for Data Science, Neural Networks and Deep Learning, Applied Machine Learning in Python, and Machine Learning, all offered by Coursera between 2017 and 2018.