As a Graduate Teaching Assistant, I supported multiple courses across various semesters, including:- Algorithms and Complexity (7th semester): Prepared theoretical exercises, graded assignments, and provided additional support to students on complex algorithmic concepts.- Introduction to Programming (1st semester): Led the laboratory sessions, gave presentations, and provided hands-on guidance to students in their first programming experience.- Discrete Mathematics (4th semester): Assisted with grading assignments and assessments.

Worked on the Fair Preference Aggregation Problem and studied the compatibility of various fairness notions under specific assumptions.

As a researcher on the Horizon Europe AutoFAIR project, my primary focus is on exploring fairness and explainability within machine learning (ML) systems. AutoFAIR is dedicated to developing trusted AI and user-in-the-loop systems for industry applications.

As an Engineer at Software Competitiveness International, I contributed to several impactful development and ML projects, including:- Developed and trained machine learning models for anomaly detection in ESOC ground systems logs using Elastic Stack and Python as part of the ProPML project (ESA-funded). This involved creating standardized datasets and implementing supervised and unsupervised learning techniques to predict anomalies in both space and non-space systems.- Contributed to the design of a recommender system for automation testing (ESA-funded).- Built back-end features for an e-commerce platform using Java, Spring Boot, PostgreSQL, and ElasticSearch, with a focus on search optimization and complex filtering processes.- Handled front-end bug fixing and improvements using Angular, Bootstrap, and HTML5, ensuring a seamless user experience.- Assisted in the design, implementation, and deployment of an Eclipse RCP decision-making application with a Business Rules Engine on the back-end, utilizing JavaFX 8 and JBoss Drools (ESA-funded).