Summary
Fabio Bonassi is a postdoctoral researcher in Machine Learning for Control with 14 years of experience blending deep learning, system identification, and optimization for safety-critical dynamical systems. Based at Uppsala University and affiliated with the ELO‑X program, he develops robust neural architectures and MPC schemes that bridge data-driven models and control implementation for industrial applications such as brake-by-wire systems. His PhD work produced multiple journal and conference papers and a best young author award, reflecting a strong track record in recurrent neural models and time-series forecasting. Comfortable with PyTorch, TensorFlow, MATLAB/Simulink and optimization toolchains (CasADi, ipopt, CPLEX), he pairs theoretical rigor with hands-on validation and stakeholder collaboration. An unusual strength is his cross-domain fluency—from energy market-aware optimal control to automotive braking systems—which enables practical, safety-focused ML deployments.
14 years of coding experience
4 years of employment as a software developer
Athens Programme, Biomedical Signal Processing, Athens Programme, Biomedical Signal Processing at KU Leuven
Laurea triennale, Ingegneria dell'automazione, 110/110, with honors, Laurea triennale, Ingegneria dell'automazione, 110/110, with honors at Politecnico di Milano
Scientifico Tradizionale, 100/100, Scientifico Tradizionale, 100/100 at Liceo Scientifico Tradizionale
Accademia del freno, Accademia del freno at Brembo Accademy
Italian, English, svedese