Graduate School of Informatics

This thesis studies multi-view, multimodal BEV perception for centerline-centric road topology understanding in autonomous driving. It focuses on improving centerline detection within a transformer-decoder framework and examines how those gains propagate to topology reasoning. The work develops three stages: mask-based centerline prediction with directional supervision and mask-Bezier fusion, Bezier-driven decoder attention through multi-point and Bezier deformable attention, and geographically disjoint plus long-range multimodal evaluation. Experiments on OpenLane-V2 and OpenLane-V1 show strong camera-only and fused camera-LiDAR performance, supporting state-of-the-art road topology understanding under consistent protocols.

Date: 16.04.2026 / 14:00 Place: A-212

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

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