Complex microbial Ecosystems MUltiScale modElling:
mechanistic and data driven approaches integration
"In my case, the E-MUSE project is the perfect mix between two of my biggest interests, such as machine learning/artificial intelligence and biology."
I am Gabriele Tazza and I am a PhD student at University of Szeged (USZ). I hold a Bachelor Degree in Clinical Engineering and I recently graduated in Data Science at La Sapienza Università di Roma with a thesis on deep-learning models applied to the field of medical imaging.
During all my studies I've always had a bias towards life sciences and for this reason, I am really happy to be part of the E-MUSE project as the ESR8. In my case, the E-MUSE project is the perfect mix between two of my biggest interests, such as machine learning/artificial intelligence and biology.
My program focuses mainly on exploring machine learning (ML) techniques in the context of multi-omics data. The two main fields of interest are ML at genome-scale modeling (eg. fluxomic analysis) in order to integrate/support knowledge-driven models, and feature selection used to help biomarker discovery and increase the performance of predictive models.
E-MUSE project represents an incredible opportunity to work in an international and interdisciplinary environment and to fulfill my passion for research.