Muhammad Ammar

I am an AI researcher and M.Sc. student in Electromobility-ACES at FAU, working at Siemens on GenAI-enabled software ecosystem health monitoring. My work sits between applied machine learning and software/data systems: turning messy platform, research, and KPI data into ontology-backed knowledge graphs, then making those graphs useful through LLM interfaces, Cypher generation, dashboards, and graph analytics.

I am especially interested in practical AI systems that connect language models with structured knowledge. Recently, I have worked on ontology-driven ETL pipelines, Neo4j-based metric knowledge graphs, text-to-Cypher query translation, prompt-to-visualization workflows, and systematic literature reviews for software ecosystem health. My thesis work extends this direction toward agentic AI workflows, with a focus on how agents are structured, controlled, and evaluated as complete systems rather than isolated demos.

Research Interests

• LLM interfaces for structured data: text-to-Cypher, schema-aware prompting, prompt-to-visualization, and natural-language access to knowledge graphs.
• Knowledge graph engineering: ontology-driven ETL, Neo4j graph construction, KPI trees, metric modeling, and graph algorithms for analytical workflows.
• Software ecosystem health monitoring: data-driven approaches for measuring platform performance, ecosystem quality, and real-time decision support.
• Agentic AI and multi-agent systems: workflow-level architectures, control mechanisms, evaluation methods, and applications in intelligent manufacturing systems.

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