Xhulio Isufi

Used PySpark for large-scale post-flight data analytics, supporting aviation projects with data-driven distribution strategies. Built a Markov Model anomaly detection system for post-flight analysis, improving aviation safety risk management. Conducted research to optimize RNN-based predictive models for flight trajectory forecasting, refining predictions from downlink messages using quantitative techniques. Researched LLMs to develop an integration API, enabling stakeholders to interact with the database. Developed CI/CD pipelines for ML model deployment with DevOps principles. Set up training and testing environments and tools to streamline development. Delivered documentation and weekly progress reports, ensuring alignment with project goals.