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

This thesis develops a regime-aware day-ahead electricity consumption forecasting framework for Türkiye. It aims to predict the next day’s 24-hour national consumption profile using historical load, weather, and calendar variables from 2019–2025. Special attention is given to weekends, public holidays, Ramadan, and Eid periods, since these days create different demand patterns. Several statistical, machine learning, deep learning, hybrid, and meta-learning models are compared. The best result is achieved by a Ridge and LightGBM-based meta-learner, reaching 1.63% MAPE in 2025.

Date: 04.06.2026 / 10:00 Place: A-212

This thesis proposes an attention-enhanced Variational Autoencoder for generating station-specific strong ground motion records. The model encodes three-component PEER NGA-West2 seismic acceleration waveforms as six-channel STFT spectrograms and learns compact latent representations through a convolutional encoder with an attention-based bottleneck. A station-aware latent sampling strategy produces site-specific synthetic recordings from limited per-station data. A structured evaluation framework is introduced in the scope of the thesis. This framework combines time-domain metrics and pseudo-spectral acceleration analysis through intensity-shape binning. Generated records are benchmarked with the evaluation framework against original recordings and SCEC Broadband Platform simulations across Southern California stations.

Date: 30.04.2026 / 14:30 Place: A-212

This thesis presents a systematic evaluation of Static Application Security Testing (SAST) tools. Related studies mostly use synthetic codebases and per-vulnerability evaluation methods. In this study, both synthetic benchmarks and real-world intentionally vulnerable applications are tested against tools, along with per-issue evaluation. Conducted experiments measure various metrics and explain these results with reasons behind them. In addition to quantitative results, qualitative features and internal mechanisms of tools are examined to further explain results and observed performance differences. The results demonstrate the difference between evaluation models and tool effectivenes. Overall, thesis offers practical insights for SAST tool research and selection.

Date: 14.04.2026 / 11:00 Place: Cisco Lab

Somatic mutations drive the transformation of normal cells into cancer. However,distinguishing driver mutations, which confer a selective growth advantage, from the vast background of neutral passenger mutations remains a critical challenge. To address this, we introduce a novel graph-based framework that constructs mutation-centric networks by leveraging longrange genomic interaction data. Our method models genomic intervals as nodes and their long-range interactions or overlaps as edges. Starting from a ’seed’ mutation, the graph expands iteratively, finding overlaps and interacting intervals to capture both local and distal genomic context. This architecture allows us to quantify a mutation’s topological influence, identify complex structural patterns (such as graph cycles), and assess proximity to known driver genes across variable ranges. Furthermore, this approach naturally generates embeddings for individual mutations, enabling the clustering of samples based on mutation profile similarity. Ultimately, by providing a comprehensive, interaction-aware view of the genomic landscape, our framework facilitates more accurate driver identification and improved patient stratification for personalized treatment.

Date: 13.04.2026 / 14:00 Place: B-116

This thesis presents an empirical investigation into the impact of Large Language Model (LLM) assistance on the human factors of software practitioners. Through a controlled within-subject experiment conducted with 30 practitioners, the study compares conditions with and without LLM support using a software requirements specification task. The research utilizes validated psychometric scales and semi-structured interviews to analyze four key dimensions: perceived task complexity, motivation, sense of achievement, and creative self-efficacy. The study provides structured empirical evidence on how LLM tools shape cognitive and psychological processes beyond technical productivity.

Date: 03.04.2026 / 14:30 Place: A-212

This thesis aims to improve demand forecast accuracy through the implementation of machine learning/deep learning applications. SARIMA, LSTM, CNN, and Prophet models are implemented to forecast demand. The models are trained and validated using forward-chaining cross validation method. It applies time series forecasting on a seasonal, unbalanced and intermittent dataset. The effects of selected exogenous indicators and aggregation-disaggregation approaches are examined for further improvement.

Date: 20.01.2026 / 14:30 Place: A-212