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

This thesis presents an integrated framework for AI-assisted BIM analytics and digital twin development to support indoor navigation and emergency evacuation. Revit-based BIM data are transformed into a unified pipeline by separating geometric representations from structured spatial information and integrated into a Unity-based digital twin. Real-time indoor pathfinding is enabled using the A* algorithm while considering physical evacuation constraints. A large language model (LLM) provides natural-language navigation instructions and supports context-aware user interaction. The system enables interactive visualization and testing and is evaluated through user studies, demonstrating a scalable and human-centred approach for BIM-based digital twin applications.

Date: 14.01.2026 / 14:15 Place: II-06

The problem of distinguishing legitimate domains from DGAs is an ongoing challenge. CTAs use DGAs to bypass traditional detection mechanisms like blacklisting or static signature-based solutions. Since blacklisting all DGA-generated domains is not possible, the automatic detection of DGAs remains a critical necessity. Despite years of research, modern malware continues to evolve, and this makes detection increasingly difficult. This thesis employes a hybrid framework that integrates CNN based n-gram learning and LSTM for automatic feature extraction and Logistic Regression (LR) for final classification. The model input is further enriched by Top-Level Domain (TLD) information in order to improve DGA detection.

Date: 16.01.2026 / 11:00 Place: Cisco Lab

This thesis presents a comparative empirical evaluation of AI-generated front-end code quality. Three contemporary AI coding tools—Cursor, Gemini CLI, and Windsurf—are assessed through controlled experiments involving React and TypeScript applications of varying complexity. The study evaluates both single-prompt and incremental development approaches using industry-standard metrics, including Lighthouse, SonarQube, and ESLint. The findings demonstrate that while AI tools can generate functionally correct applications, code quality outcomes vary significantly depending on tool choice, development methodology, and application complexity. The results provide evidence-based guidance for selecting AI tools and workflows in modern front-end development.

Date: 20.01.2026 / 13:00 Place: A-212

This thesis introduces the Adaptive Reasoning Curriculum (ARC), a standardized framework that uses Large Language Models (LLM) for Automated Curriculum Learning (ACL). Designed to improve generalization in complex Open-Ended Learning (OEL) environments like Neural MMO 2, ARC facilitates efficient skill acquisition rather than environment memorization. Experiments demonstrate that ARC significantly outperforms expert-curricula and existing ACL methods across average return and sample efficiency. By providing open-source adapters and auxiliary tools, ARC establishes a reproducible, accessible standard for integrating LLMs into multi-agent RL, fostering broader innovation in open-ended research.

Date: 14.01.2026 / 13:00 Place: A-212

This study proposes a framework for investigating the alignment between institutional reviewer guidelines and peer review practice. A dataset was constructed from the ICLR 2024 venue on OpenReview. Large Language Model (LLM) pipelines were utilized to extract reviewer inquiries, perform topic modeling, and apply soft classification. These outputs were compared against the ICLR 2024 reviewer guideline to quantify the guideline-practice gap. Generated topics are also mapped to the guidelines from the top ten AI venues. The proposed framework provides a data-driven approach to identify implicit evaluation norms for venue organizers and gives authors and reviewers a data-driven roadmap of implicit evaluation norms.

Date: 14.01.2026 / 14:30 Place: A-212

This thesis proposes a data-driven framework to predict the surface roughness (Ra) of additively manufactured parts before printing. The most influential printing parameters were identified, and an experimental design was developed to generate a new, comprehensive dataset that contributes to the literature. Machine-learning and deep-learning models were implemented, with a multilayer perceptron (MLP) as the primary model to capture nonlinear effects. Data augmentation via a Conditional Generative Adversarial Network (CGAN) was investigated, and its impact on generalization was quantified. Finally, a custom web-based graphical user interface (GUI) was implemented for 3D model upload, interactive orientation and parameter adjustment, and real-time Ra visualization.

Date: 06.01.2026 / 13:00 Place: A-212

This thesis addresses the "tuning problem" in haptics by developing a human-in-the-loop Bayesian framework to estimate precise mechanical parameters from subjective preferences. The approach utilizes Gaussian Processes that learn from pairwise comparisons of different options to model latent utility functions. An Active Learning framework is adopted to intelligently select these comparison pairs. Experimental results across stiffness K, damping B, and distance D demonstrate that the model successfully converges to reference parameters. Furthermore, the study validates how coactive feedback accelerates convergence and helps the algorithm escape local optima.

Date: 22.01.2026 / 09:00 Place: A-212

This thesis develops a modular Value-at-Risk (VaR) framework tailored for the Borsa Istanbul (BIST) equity market. To overcome the failure of normality assumptions in emerging markets, we integrate volatility filtering with semi-parametric tail modeling. We introduce a dynamic scaling mechanism (kdyn​) that adjusts forecasts based on recent violations. Empirical analysis of 28 liquid stocks (2005–2025) shows that our adaptive Filtered Historical Simulation model achieves an 82.1% success rate in Conditional Coverage tests. These findings validate that decoupling volatility dynamics from tail shape significantly improves risk estimation stability during market stress.

Date: 12.01.2026 / 13:30 Place: A-212