This project studies the robustness of country-wide railway networks using network science methods. The networks are built from GTFS data and modeled as graphs capturing stations and rail connections. Network efficiency and connectivity are tracked as stations are progressively removed under different disruption strategies to assess the impact on overall system performance.

EnergyAgent is an LLM-powered conversational assistant for household energy monitoring and efficiency. It enables natural-language queries over smart-meter data and provides strictly factual responses derived from recorded consumption. User questions are translated into explicit calculation steps processed by an analytics engine, ensuring numerical accuracy. The system then converts these results into clear, actionable insights to help users better understand and optimize their energy usage.