Prof. Dr. Daniel Schuster
Junior Professor of AI Methods for Process Analysis & Management
Daniel Schuster joined the Data and Web Science (DWS) Group, part of the Institute of Computer Science and Business Informatics, in December 2025 as a Junior Professor for AI Methods for Process Analysis & Management. Since July 2025, he has also been affiliated with the Chair of Database Systems, Data Mining, and AI at LMU Munich.
He received his PhD in July 2023 from the Chair of Process and Data Science at RWTH Aachen University, where he was supervised by Wil van der Aalst. Prior to his doctoral studies, he earned an M.Sc. in Computer Science and an M.Sc. in Management, Business, and Economics, both from RWTH Aachen University.
Research Interests
His research interests lie within the broader domain of process mining, with a particular emphasis on:
- Hybrid intelligence in process discovery, including interactive and incremental approaches
- AI-driven techniques for process mining, such as the incorporation of large language models into process mining tasks
- Methods for representing, processing, and analyzing partially ordered event data
Teaching
Courses
Spring Semester 2026 (FSS 26)
- Lecture: Advanced Process Mining (IE 692)
Range of application: M.Sc. Mannheim Master in Data Science, M.Sc. Business Infromatics - Seminar: AI-Driven Process Mining (CS 733)
Range of application: M.Sc. Mannheim Master in Data Science, M.Sc. Business Infromatics
Fall Semester 2026 (HWS 26)
- Seminar: Advanced Topics in Process Mining (CS 733)
Range of application: M.Sc. Mannheim Master in Data Science, M.Sc. Business Infromatics
Spring Semester 2027 (FSS 27)
- Lecture: Advanced Process Mining (IE 692)
Range of application: M.Sc. Mannheim Master in Data Science, M.Sc. Business Infromatics - Seminar: Advanced Topics in Process Mining (CS 733)
Range of application: M.Sc. Mannheim Master in Data Science, M.Sc. Business Infromatics
Master’s Thesis Topics
Interested in a Master’s theses in the broader area of process mining and related fields?
Prerequisites:
- Understanding of fundamental concepts of process mining (beneficial)
- Programming skills (required)
Possible sub-areas include:
- AI techniques for process mining (including LLM-based approaches)
- Techniques for handling partially ordered event data
- Interactive and incremental process discovery
- Other related topics based on student interests
These areas provide an overview of our focus. All thesis topics are defined individually upon request. Please reach out if you are interested.
Publications
For an overview of my latest publications, please refer to my researcher profiles on
Contact
Prof. Dr. Daniel Schuster (he/him)
School of Business Informatics and Mathematics
B 6, 26 – Room B 0.15
68159 Mannheim
by appointment