• Designed and implemented data pipelines to explore, clean, and analyze cardiovascular imaging data for patient prognosis using machine learning algorithms such as Scikit-learn and Pandas.• Trained and optimized tree-based gradient boosting algorithms (e.g. LightGBM, XGBoost, CatBoost) on radiologist-derived features, and utilized SHAP frameworks for model interpretability.• Conducted statistical analyses on retrospectively and prospectively collected clinical data for 15+ research papers published in peer-reviewed journals and presented at international conferences.

• Designed, implemented and maintained both the back-end and front-end of 3 dashboards (Python, Django and React.js) deployed on the Google Cloud Platform with 20k+ monthly visitors;• Realized and maintained Flask-based microservices for a tool that collected 10k+ daily javascript errors of 10+ businesses and for a tool that identified recurring visitors of 10+ websites based on their IPs;• Written RESTful APIs for performing CRUD operations on data stored on relational databases (MySQL);• Deployed a serverless solution (AWS Lambda) that fetched data from 10+ APIs and stored computation output on AWS S3;• Scheduled recurrent jobs for fetching, aggregating and storing data using CI practices (Jenkins).

• Carried out research activities on the Blockchain technology, with particular focus on decentralized energy markets. • Designed and implemented a decentralized application on the EnergyWeb blockchain for enabling peer-to-peer energy transactions between customers of the electric grid using a novel concept of rechargeable energy token.

• Conducted research on the Blockchain technology (mainly Ethereum);• Designed a decentralized application on the Ethereum Blockchain for application cooperation in the context of the public administration.