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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
This thesis proposes a self-supervised framework for detecting Primary (P) waves in strong motion accelerograms, an essential task for Earthquake Early Warning systems. Using Variational Autoencoders trained exclusively on P-wave segments sourced from 648 recordings of the Turkish National Strong Motion Network, the model detects P-waves through reconstruction-error-based detection without requiring labeled data. A systematic search across 492 configurations of four VAE architectures reveals that attention mechanisms achieve the best detection performance. The final Attention-VAE model achieves an AUC of 0.998, surpassing supervised baselines and demonstrating potential for real-time deployment.
Date: 12.06.2026 / 14:00 Place: A-212
