Zabihullah Azimy

- Master's Thesis: "Sovereign Federated Learning for Financial Applications" — Researched federated XGBoost training for financial use cases, evaluating aggregation strategies and integrating Eclipse Dataspace Components Connector (EDCC) to enforce client-defined policies governing federated learning training. - Improved the reliability of the Distributed Learning Framework (DLFi) by identifying and resolving bottlenecks across multiple system components. - Developed and enhanced framework components, in

- Contributed to research activities in digital signal processing (DSP) for coherent optical communication systems by implementing and modifying software components in C/C++ and Python to meet evolving project requirements. - Enhanced and maintained existing codebases supporting experimental investigations in optical transport networks and related signal processing applications. - Troubleshot, configured, and managed virtual machine environments to ensure reliable execution of laboratory experiments and eva

- Developed and maintained RESTful APIs using Django REST Framework (DRF), backed by SQLite databases, to support laboratory workflows and internal applications. - Integrated Django OAuth Toolkit to implement OAuth 2.0-based authentication and authorization mechanisms, improving the security of internal services. - Automated the ingestion of medical measurement data by developing parsers that extracted, transformed, and stored records in the database. - Reduced manual data processing efforts by automating t

- Developed dynamic frontend applications using Angular, enhancing user interaction across the website. - Collaborated closely with stakeholders to understand business requirements, ensuring alignment with project goals. - Integrated SQL database functionalities to support seamless data management and retrieval.