Running Theses
A Rule-Based Agent for Semantic Matching Graph Visualization
Power grids are increasingly operated through tightly interconnected IT/OT infrastructures, which raises the attack surface and makes smaller operators with limited resources particularly vulnerable to security-relevant incidents. This thesis develops and evaluates a reproducible, resource-efficient analysis algorithm that captures essential system, process, role, location, and information-flow data to derive protection needs and criticality, and to ...
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation :
Lack of access to existing codebases
Limited knowledge of project-specific packages, dependencies, and interfaces
Difficulty maintaining consistency with established code patterns and architectures
To address these challenges, Retrieval Augmented Generation (RAG) approaches have emerged, ...
This thesis investigates how Large Language Models (LLMs) can be equipped with a deeper, architecture-level understanding of tabular data, going beyond “tables-as-serialized-text” toward tables-as-structured objects that expose row/column topology, header semantics, cell neighborhoods, and inter-cell dependencies to the model in a principled way . The target setting is Semantic Table Interpretation (STI) as studied in the SemTab challenge, focusing on three ...
This thesis investigates how to formally represent early-stage data science requirements and how to support the automation of early-stage data science through an LLM-based agent.
This thesis investigates whether Vision-and-Language Navigation (VLN) can be reliably transferred from conventional benchmarks to subway tunnel environments, enabling a quadruped robot to execute inspection-oriented navigation tasks under constrained geometry, degraded visibility, and limited connectivity. The work is motivated by recent vision-language-action approaches that connect language grounding with embodied control for legged platforms (e.g., NaVILA) ...
Sentiment analysis models detect emotion in text, but need retraining for each new context. To generate training data, Large Language Models (LLMs) are increasingly being used but performance is still limited. We aim to improve it via the creation of a structured framework for LLM-driven data synthesis.
We are seeking a motivated master’s student to explore the application of LargeLanguage Models (LLMs) and Small Language Models (SLMs) for automated semantic mapping in data integration scenarios involving sensitive information. This thesis addresses a critical challenge in modern data management: domain experts often possess the knowledge needed to align local data sources with global ...
The aim of this thesis is to extend an existing system for providing psychomotor feedback in a camera-based learning environment by automating or supporting the rule creation process.
The core objective is to leverage computer vision techniques and large language models (LLMs) to extract motion data from YouTube tutorial videos and automatically infer psychomotor feedback rules, ...
A Framework for Automated Sanitization of Cybersecurity Playbooks
Sensor-based machine learning (ML) systems (such as predictive maintenance, environmental monitoring, and industrial automation) require scalable, explainable, and continuously evolving data infrastructures. The complexity of these systems lies not only in the technical pipeline (data ingestion, feature engineering, model training, deployment, monitoring) but also in the design decisions stakeholders make along the way. These decisions ...
Enterprise Software has become a critical pillar in global digital transformation. In China, for example, next-generation platforms such as DingTalk and Feishu not only integrate office automation functionalities but also play a central role in project management, team collaboration, and workflow optimization, which enable efficient cross-department collaboration, task transparency, and workflow automation, thereby enhancing organizational ...
The healthcare and long-term care (LTC) sector is experiencing severe workforce challenges. Nurses and caregivers are frequently confronted with excessive workloads, mandatory overtime, and the constraints of complex union agreements. These pressures contribute to burnout, high turnover rates, and rising labour costs, while simultaneously undermining care quality and patient satisfaction.
Conventional workforce management (WFM) tools are ...
The goal of this thesis is to design and prototype a serious game that simulates stakeholder engagement challenges in MLOps lifecycles. The game will make use of LLM-based agents to represent typical stakeholder roles (e.g., data scientists, ML engineers, domain experts, operations managers) and allow players to interact with them in different lifecycle stages.
The aim ...
This thesis aims to explore and prototype a system for participatory governance and transparent decision-making in the context of modernizing legacy software systems. Using Large Language Models (LLMs), the system will analyze and extract decisions from various project artifacts (e.g., commit messages, meeting transcripts), represent them in a structured decision domain model, and create an ...
This thesis focuses on addressing the limitations of current Distributed Analytics Architectures by developing a declarative approach to model and automate cross-institutional analysis workflows. It aims to implement a secure, low-complexity architecture that enhances reproducibility and extensibility while evaluating its effectiveness compared to existing methods.
With rapid advances in artificial intelligence, especially large language models (e.g., OpenAI GPT, Anthropic Claude, Google Gemini), AI has demonstrated great potential in natural language processing, code generation, and automated testing. Programming, traditionally a highly specialized and creative task, is increasingly supported and partially automated by AI. Tools such as GitHub Copilot and OpenAI Codex ...
As software engineering continues to evolve, agile frameworks have become the main paradigm for project management and development. Among these, Scrum is widely adopted by numerous software organizations due to its iterative and incremental development approach, emphasis on team collaboration, and rapid feedback cycles. With the ongoing advancements in artificial intelligence technologies, particularly the recent ...
While recent advancements in natural language processing have been largely driven by increasingly powerful large language models (LLMs), the role of data quality in fine-tuning these models remains underexplored. This thesis addresses the often-overlooked but critical aspect of data-centric AI by investigating how different types and levels of data degradation affect the performance of fine-tuned ...
The increasing connectivity of Industrial Control Systems (ICS) has elevated the need for robust cybersecurity measures. However, evaluating the effectiveness of Intrusion Detection Systems (IDS) in ICS environments remains fragmented and inconsistent. This thesis addresses this challenge by developing a systematic, modular benchmarking environment that enables reproducible and standardized evaluation of machine learning-based IDS across ...
The goal of this thesis is to design, implement, and evaluate a sensor-based maintenance bot that uses Large Language Models (LLMs) to support predictive maintenance and decision-making. The bot should be capable of retrieving, analyzing, and reasoning over time series sensor data as well as unstructured maintenance-related documentation (e.g., technical manuals, incident reports). The result ...
In modern manufacturing, data plays a crucial role in optimizing processes, enhancing efficiency, and enabling interoperability across different systems. However, data models in manufacturing are often heterogeneous, proprietary, and lack standardization, making data sharing and integration challenging. The FAIR principles – Findability, Accessibility, Interoperability, and Reusability – provide a structured framework to improve data ...
