This research addresses a persistent paradox in the digital economy: While data is increasingly recognized as a strategic asset, data science projects designed to leverage its potential impact continue to suffer from high failure rates. As established in management theory, measurement is the prerequisite for improvement; without the ability to objectively assess success, organizations cannot effectively detect risks or optimize their initiatives. However, the current literature lacks a formalized, operationalizable success assessment model that accounts for distinct characteristics of data science projects and is applicable across diverse project types and contexts. To bridge this gap, this thesis develops the Data Science Projects Success Assessment Model (DS PRO-S). Adopting a Design Science Research (DSR) approach, the study constructs a holistic solution that functions as a meta-model, an instantiation toolkit, and a methodology to make project success explicit, measurable, and comparable. This architecture is supported by a rigorous mathematical formalization of measurement and evaluation, aligned with the ISO/IEC 15939 standard. By introducing evaluations at both project and phase levels and decoupling success (the achievement of objectives) from health (establishing the enabling conditions for success), DS PRO-S offers a modular and asynchronous assessment capability with operational flexibility. The applicability and usefulness of DS PRO-S were validated through expert interviews and multiple case studies.

Date: 21.01.2026 / 13:30 Place: A-212

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 dissertation presents ID-SDM, a computational framework utilizing Influence Diagrams to model Shared Decision-Making (SDM) based on the Three-Talk Model. By representing clinicians and patients through separate IDs, the model simulates information flow via three node operations: decision alternative transfer, chance node transfer, and preference transfer. Applied to Graves’ Disease, results show that SDM achieves the perfect-information-sharing model’s optimal decision.more efficiently than other decision models. The SDM process reaches consensus in less time with upfront information sharing from both sides. When the clinician attributes greater importance to the patient's utility criteria, the clinician's decision shifts to the perfect-information-sharing model’s optimal decision.

Date: 13.01.2026 / 13:30 Place: A-212

This thesis proposes the Data Governance Capability Maturity Model (DG-CMM), a structured assessment model based on ISO/IEC 330xx standards for evaluating organizational data governance maturity. The model examines maturity across four core process areas: Data, Organization, Strategy, and Technology. DG-CMM was developed using a Design Science Research methodology in line with Becker et al. (2009), incorporating an extensive literature review, a Modified Delphi approach with domain experts, and empirical case-based validation. The model offers organizations a standardized and actionable framework to systematically identify maturity gaps, prioritize improvements, and strengthen data-driven decision-making and strategic alignment through effective data governance practices.

Date: 16.01.2026 / 09:00 Place: A-212

Addressing the replication crisis in Terror Management Theory, this dissertation investigates the inconsistent link between Mortality Salience and risk-taking. It moves beyond self-reports by integrating three methodologies: causal modeling via a systematic literature review (ESC-DAG), objective neural measurement using EEG and the Balloon Analogue Risk Task (BART), and Bayesian cognitive modeling. By synthesizing causal, neural, and computational evidence, the study aims to clarify the cognitive mechanisms driving risk behavior under mortality salience, ultimately contributing to resolving the field's reproducibility challenges.

Date: 14.01.2026 / 10:30 Place: A-212

This thesis addresses the inherent complexity of discourse parsing in Natural Language Processing (NLP) by developing a multilingual framework implemented for Penn Discourse TreeBank (PDTB) datasets. Leveraging the advancements of Large Language Models (LLMs) and transformer architectures, the thesis proposes a hybrid methodology that integrates fine-tuned BERT models for Discourse Connective (DC) detection and argument span labeling with in-context learning strategies for Discourse Relation Recognition (DRR). The study bridges the gap between isolated sub-tasks and end-to-end processing by defining interconnected modules that link detection, labeling, and recognition phases. Evaluating this pipeline across seven datasets in English, Portuguese, and Turkish, the framework achieves performance on par with state-of-the-art models. Additionally, the thesis contributes a novel lightweight DC detection model and introduces a method to enhance implicit discourse relation recognition using machine translation techniques, demonstrating the efficacy of these approaches in both high- and low-resource linguistic contexts.

Date: 23.12.2025 / 15:00 Place: A-212

This thesis develops a framework for six degrees-of-freedom (6DoF) spatial audio capture and reproduction using higher-order Ambisonics and rigid spherical microphone arrays (SMAs). It introduces SPWIN (Sparse Plane-Wave Interpolation), a novel method for accurately reconstructing sound fields between microphone positions, enabling realistic, head-tracked VR experiences. The framework also presents a spherical harmonic–based method for estimating sound source directivity. Performance is validated through objective interaural coherence analysis and subjective MUSHRA tests in VR. These advancements close key gaps in spatial audio reproduction and align with emerging standards such as MPAI-CAE, supporting next-generation immersive media systems.

Date: 25.08.2025 / 13:30 Place: B-223

This thesis investigates multimodal data fusion and multicompartment image analysis in acute and chronic lung diseases. In a COVID-19 cohort, we integrated imaging, clinical, and viral genomic data, using sparse canonical correlation analysis and cooperative learning to explore inter-modality associations and predict intensive care unit admission. We leveraged Word2Vec to encode the viral genome. In an interstitial lung diseases cohort, we extracted lung and pulmonary artery radiomics features from chest computed tomography scans, demonstrating predictive value for pulmonary hypertension and transplant-free survival. We illustrated that multimodal data fusion and multicompartment image analysis mirror clinical decision-making processes and improve personalized prognostication.

Date: 16.07.2025 / 15:30 Place: A-108

The thesis examines reference selection in Turkish, focusing on how linguistic and cognitive factors shape the choice of referring expressions. A novel coreference corpus based on spontaneous, goal-directed dialogs was developed using a new annotation scheme. Statistical and machine learning analyses reveal that competition and distance metrics are the strongest predictors of referential form, while grammatical and speaker roles show weaker but significant effects. The findings support and extend key predictions from theories about reference production and comprehension by focusing on a typologically different language and using naturalistic data.

Date: 18.06.2025 / 10:00 Place: A-212

A common evolutionary origin of the Obscurin and Trio protein families was identified through phylogenetic analyses, indicating ancient domain shuffling events across vertebrate lineages. Building on these findings, the novel variant effect predictor TrioNsight was developed for TrioN-like DH domains. The predictor, which integrates structural, evolutionary and physicochemical features, achieved high accuracy in distinguishing pathogenic variants (MCC: 0.906; 1.0 indicates perfect prediction). This work bridges fundamental evolutionary biology and clinical application, offering both mechanistic insights into protein family evolution and a practical tool for the interpretation of disease-associated variants in functionally critical domains.

Date: 30.05.2025 / 10:00 Place: A-212