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This thesis presents CBC-Distilled, an efficient multimodal CNN–Transformer model for remote sensing semantic segmentation under missing-modality conditions. The model combines RGB, near-infrared (NIR), and shortwave infrared (SWIR) information and uses feature distillation, cross-band correlation, and random modality masking to improve robustness when one or more modalities are unavailable. The teacher and auxiliary branches are used only during training and removed during inference to reduce computational cost. Experiments show that the proposed model maintains competitive segmentation performance while requiring fewer parameters and lower computational cost than the baseline.
Date: 03.09.2026 / 13:30 Place: A-212
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
