I have received a Bachelor's degree in Computer Engineering in 2016, and a Master's degree in Computer Engineering with honors in 2019.
From November 2019 to October 2020, I have been a Junior Research Fellow working on the project "Advancements in Empirical Model Learning with Application on Transprecision Computing". During this period, I worked on methods to inject knowledge into Deep Learning models to solve Constraints Satisfaction Problems.
Since November 2020, I am a Ph.D. student in Computer Science and Engineering. My research focuses on the integration of external and prior knowledge in Machine Learning algorithms (machines can be more intelligent if we teach them what we already know!).
As a Ph.D. student, I am currently involved in the KINeMA (Knowledge Integration in Neural networks for e-Maintenance) project of the BI-REX competence center. This project is allowing me to apply my research on Predictive Maintenance real-world use cases and to collaborate with industrial partners.
I am also interested in the intersection between learning and optimization. During my Master’s thesis, I applied Deep Reinforcement Learning algorithms to solve the Product Allocation Problem, which is an NP-hard combinatorial optimization problem. Currently, I am working on methods that allow for tighter integration between Deep Reinforcement Learning and combinatorial optimization. In my opinion, taking advantage of both worlds is the right path to follow to further improve this research area.
