Dr. Jan Stodt is an AI researcher specializing in Explainable Artificial Intelligence (XAI), machine learning, and regulatory compliance. His academic career began at Furtwangen University, where he earned his Bachelor’s and Master’s degrees in Computer Networking and Computer Science. His early work combined cryptography, blockchain security, and industrial privacy protection systems, with a strong emphasis on trustworthy data handling in Industry 4.0 environments. He later pursued a Ph.D. in Explainable AI at the University of Plymouth in collaboration with Furtwangen University. His doctoral thesis focused on enhancing the interpretability and reliability of neural networks, especially in high-risk applications such as medical AI. He introduced innovative XAI evaluation metrics (ESA, WESA, UIoU), developed the domain-adapted FastCAM method for endoscopic image analysis, and proposed the Explainable Object Classification (EOC) framework that integrates expert knowledge and visual features for decision transparency. As academic staff at Furtwangen University, Dr. Stodt has led and contributed to several interdisciplinary research projects. These include the KIEBiZ project, developing certifiable AI methods for endoscopic imaging; the NIO project, integrating explainable and adaptive AI into oncology workflows; and MoDe ProBio, applying digital twins and AI for sustainable bioprocess optimization in the bioeconomy sector.