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Continuous production environments (refineries, power grids) generate vast multivariate sensor data, yet remain stuck in fragmented “Narrow AI” while foundation models transformed language and vision. This thesis introduces Prometheus, a compact 1.6M-parameter bidirectional Transformer encoder that reads a plant’s sensors as a language and learns its coupled physics through self-supervised “Four-Teacher” geometric masking. On a crude distillation unit, Prometheus wins all 38 channels on every metric, surpasses zero-shot Time-Series Foundation Models up to ~300× larger, reconstructs entirely missing sensors, and beats a deployed industrial soft sensor, evidence that domain specialization, not generalized scale, is the path toward Industrial Foundation Models.
Date: 25.06.2026 / 14:30 Place: A-212
This thesis presents a conversational analytic system that enables users to query complex data environments through natural language instead of writing SQL. The system combines Data Mesh and Data Fabric principles on a lakehouse architecture and uses LLM-based agents to discover datasets, enrich metadata, and infer relationships between tables. The goal is to reduce the manual effort required for data discovery and schema exploration, while keeping the underlying metadata transparent, reusable, and reproducible. The approach is evaluated through a multi-domain benchmark designed to measure how relationship metadata affects query correctness and reproducibility.
Date: 15.06.2026 /15:00 Place: A-212
This thesis presents a conversational analytic system that enables users to query complex data environments through natural language instead of writing SQL. The system combines Data Mesh and Data Fabric principles on a lakehouse architecture and uses LLM-based agents to discover datasets, enrich metadata, and infer relationships between tables. The goal is to reduce the manual effort required for data discovery and schema exploration, while keeping the underlying metadata transparent, reusable, and reproducible. The approach is evaluated through a multi-domain benchmark designed to measure how relationship metadata affects query correctness and reproducibility.
Date: 18.06.2026 /15:00 Place: A-212
This study identifies and investigates the critical barriers to adopting Agile methodologies within Turkey’s defense industry. Utilizing an extensive literature review and expert consultations, the research identifies key challenges in transitioning to flexible and collaborative methodologies. By applying Interpretive Structural Modeling (ISM) and MICMAC approaches to questionnaire data, the study maps the causal relationships and interdependencies among these barriers, establishing a layered, hierarchical framework. Ultimately, this research provides defense industry managers with a systematic understanding and a comprehensive framework to enhance awareness, mitigate adoption impediments, and lay a solid groundwork for successful Agile transformation.
Date: 17.06.2026 Place: A-212
This study takes a quantitative look at how protein-coding genes replicate across different cell lines using SigProfilerTopography, where we score earlier-replicating genes higher. We originally set out to build a model that could predict replication timing directly, but when that didn't pan out as expected, we shifted our focus to mapping out the actual timing differences between cell lines.
Date: 23.06.2026 / 14:30 Place: A-212
