João Luz is a biochemist whose interests revolve around untargeted metabolomics, high-resolution analytic techniques and computational data analysis using Machine Learning and statistical methods. Attending the Faculty of Sciences of the, University of Lisbon (FCUL), he obtained a Bachelor of Science degree in Biochemistry in July 2018, followed by a Master of Science degree in Biochemistry in February 2021. From October 2017 to July 2018, he conducted research as an intern at the FT-ICR and Structural Mass Spectrometry Laboratory of FCUL (FT-ICR-MS-Lisboa), where he acquired extensive scientific knowledge and practical skills important to the experimental practice of biochemistry, including experience in the growth and propagation of yeasts (wildtype and mutants), preparation of metabolite extracts and metabolomic analysis with fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). His masters' dissertation, entitled "Metabolomic effects of single gene deletions in Saccharomyces Cerevisae", also developed at the FT-ICR-MS-Lisboa laboratory, focused on the development of an FT-ICR-MS-based untargeted metabolomics approach for the differentiation of mutant yeast strains with the same genetic backgrounds. It was classified with a value of 19 out of 20. This same research later resulted in posters exposed at the X-omics festival 2021, the 2nd Chem & Biochem Students meeting at FCUL and the 14th European Fourier Transform Mass Spectrometry Workshop & 3rd EFTMS School, as well as in a Bruker application note (MRMS 75). João is currently a member of the team working on a project entitled "Wine yeasts typing: discrimination of non-conventional wine yeasts through untargeted chemical fingerprinting," as well as a recipient of a research fellowship from the "European Network of Fourier-Transform Ion-Cyclotron-Resonance Mass Spectrometry Centers," funded by the European Union. In addition to his experimental research skills, João is also knowledgeable about multivariate statistical analysis techniques (PCA, HCA, etc), as well as Machine Learning (PLS-DA, RFs), familiar with specific data analysis software for FT-ICR-MS (Metaboscape), and proficient in Python programming, with a specific focus on the scientific applications of the programming language. He co-authored two papers entitled "Binary simplification as a useful tool in metabolomics" and "Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics," where he proposed novel approaches for the treatment FT-ICR metabolomics data. This particular combination of skills in microbiology, high-resolution analytic techniques and computational data analysis allows him to autonomously develop scientific projects with a high degree of complexity and multidisciplinary character.