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SALGINTR is a physician-based digital participatory surveillance system in which physicians self-report their own weekly influenza-like illness. Running at low cost (about USD 100 for the season), it was evaluated against Türkiye's national sentinel stream over the 2025/26 season (33 weeks). Of 304 registered physicians, 248 reported at least one week, contributing 4,729 person-weeks and 497 episodes. The participatory and sentinel series agreed before the epidemic (r=0.89) and discriminated epidemic weeks (AUC=0.77), with substantial alarm concordance (κ=0.835). A low-cost physician panel tracked the season in near-real time, supporting participatory reporting as a complement to laboratory-anchored surveillance.
Date: 02.09.2026 / 14:00 Place: A-212
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
