Graduate School of Informatics

Accurate identification of splice-altering genetic variants is critical for understanding disease mechanisms and improving clinical variant interpretation. Although deep learning–based splice prediction tools perform well for canonical splice-site variants, their ability to detect exonic splice-altering variants remains limited. This limitation is primarily due to the scarcity of experimentally validated exonic variants and model architectures optimized for canonical splice motifs rather than regulatory exonic regions. Overall, this study provides a comprehensive evaluation of current splice prediction tools, demonstrates the benefit of targeted retraining for exonic variant detection, and establishes a foundation for developing more accurate and clinically relevant splice-altering variant prediction models.

Date: 15.01.2026 / 11:00 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

Predicting surgical case durations accurately is essential for operating room scheduling and resource management of healthcare facilities. While everything relies on technology recently, enabling a system that predicts surgical durations using machine learning enhances accuracy, decreases workload of the healthcare personnel and increases patient satisfaction eventually. Using RFID-derived data to develop this machine learning model is more precise because the data source is not manually recorded. By dividing the available surgical data into three as short, medium and long, predictions are made by giving an estimation interval and the results are coherent with the literature according to the error rates.

Date: 13.01.2026 / 10:00 Place: A-212