Head of the Research Group
Research Associates and PhD students
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Research and Teaching Assistants
Research Students
Extracting casual graphs on supply chain resilience and sustainablity from documents using open source LLMs
Prompt Engineering for Reducing Sensitivity Leakage in LLM-Based Analysis of Vehicular Telematics
Realtime Agricultural Product Price Forecasting Using Explainable Deep Learning
Designing Counterfactual ‘What-If’ Scenarios for Logistics Digital Twin Replays
Enhancing Data Quality in Project Management: A Machine Learning Approach to Real-Time User Guidance
Prompting Strategies to Reduce Data Sensitivity in LLMs: Insights from Vehicle Telematics with Time Series Imputation
Object Detection of Surgical Equipment in Hospitals Using Explainable Deep Learning
Optimizing Production Planning for Circular Economy Integration: A Reinforcement Learning Approach
KPI Set for Operational Forest Digital Twins (Indicator Dictionary + Computation Specs)
From PDFs to Knowledge Graphs: An Ontology-Driven, Agentic AI Pipeline for Supply-Chain Resilience
Transformer-Based Foundation Models for Predictive Maintenance
LLM Prompt for Privacy Reducing Sensitivity Leakage in LLM Applied to Vehicular Systems
An Ontology-Based Modeling Approach for Integrating KPIs and Operational Constraints in Sustainable Urban Logistics Systems
The Impact of Process Design and Data-Supported Technology Usage on Process Efficiency in Work Systems: A Data-Informed Analysis
Developing a Conceptual Framework for Using Large Language Models (LLMs) in Sustainability/ESG Data Collection and Report Generation: A Systematic Literature Review
Safety-Constrained Causal Reinforcement Learning for CO₂-Efficient Fleet Dispatching
Federated Learning Framework for Privacy-Preserving ESG Risk Assessment
Building Feature Graphs from Logistics Data for Causal Analysis
Ontology-Guided Hybrid Causal Discovery in ESG Data: LLM-Based Development of Query Interface
A Hybrid Decomposition-Deep Learning Framework for Short-Term Demand Forecasting
Comparative Analysis of Statistical, Machine Learning, and Deep Learning Imputation Algorithms for Time Series Data: Foundations for Reliable Causal Discovery
Combining XGBoost, Causal AI and Generative AI for Explainable Oncology under the EU AI Act
LLMs for Extracting Key ESG Indicators from Public Reports / Retrieval-Augmented Verification Framework for Faithful KPI Extraction
Linking Supply Chain Resilience & Sustainability KPI Ontologies to Causal Graphs and Comparing Them with Data-Driven Causal Discovery
LLM-Assisted Collection of ESG Data from News Articles and Media Sources
Automated Root Cause Analysis in Automotive Software testing Leveraging LLMs
Developing a Conceptual Framework for Using Large Language Models (LLMs) to Make the ESG Reporting Process More Sustainable: A Systematic Literature Review
Research topic: What are the benefits and challenges of using federated learning for ESG assessments in supply chains?
Plant Disease Detection using Explainable Deep Learning Integrating with LLMs
Comparing How LLMs Differ in Extracting Causal Relationships from Natural Language Text through ESG Reports
Exploring Machine Learning and Deep Learning Architectures for Time Series Predictive Maintenance
From Algorithms to Time Series: Validating Causal Discovery Methods for Infrastructure Decline Analysis in South African Logistics
Uncovering Causal Relationships in Text Documents with Large Language Models (LLMs)
Causal Analysis of Digital Product Passports on Consumer Purchasing Behavior
LLM Prompt for Privacy Reducing Sensitivity Leakage in LLM Applied to Vehicular Systems
RAG-Powered LLMs for Smart Document Retrieval in the Logistics Industry
Extracting Structured Data from Unstructured ESG reports Using Large Language Models
Former Members
Former Research Students
Identifying essential ESG key performance Indicators of the automotive industry: An Industry based survey using MCDM methods
Research topic: Object detection from 3D images of digital twin assets for transport infrastructure management using explainable deep learning approach
Research topic: Deep reinforcement learning for Industrial task scheduling: PPO algorithm approach
Research topic: Efficient routing with GTFS data for public transportation using explainable machine learning
Research topic: The role of causal machine learning to improve decision making in Supply Chain Management
Research topic: Trustable plant disease detection using explainable deep learning approach
Research topic: Predictive maintenance of aircraft components: Impact of using explainable machine learning on cost, safety, and efficiency
Research topic: Exploring autoregressive models in TensorFlow through the application of energy forecasting
Research topic: Comparative analysis of traditional and AI autoregressive models for enhancing demand forecasting
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