Maximilian Wolf

Maximilian Wolf is a Researcher at the Chair of Data Science (Informatics X) within the Institute of Computer Science at the University of Würzburg's Faculty of Mathematics and Computer Science. He simultaneously pursues cooperative doctorate studies between University of Würzburg and University of applied sciences and arts Coburg, where he teaches courses. His dual affiliation enables him to bridge theoretical research with practical applications in machine learning and cybersecurity.

Wolf received his Master's degree in Computer Science from University of applied sciences and arts Coburg in 2021 before joining the DMIR research group at Würzburg in 2022. His academic journey demonstrates a clear progression from formal education to active research and teaching.

Wolf's research focuses on synthetic data generation for cybersecurity applications, with particular expertise in applying generative models to create benchmark datasets for network security. His work addresses critical challenges in network intrusion detection, fraud detection, and traffic classification through innovative machine learning approaches. He has developed specialized knowledge in explainable AI techniques for security applications, including Shapley-value-based anomaly explanation.

His publication record shows remarkable productivity with multiple high-quality publications in 2024 alone, demonstrating increasing sophistication in both methodology and application. Wolf's research consistently addresses real-world security challenges through cutting-edge machine learning techniques, with a clear trajectory toward becoming a significant contributor to the field of AI for cybersecurity.

Wolf has taught Advanced Data Mining and Reinforcement Learning courses at University of applied sciences and arts Coburg since Winter term 2021/22, demonstrating both technical expertise and teaching capability. He is actively involved in the Genesis project at Coburg, which focuses on generating cybersecurity benchmark datasets using generative models.