Daniel Schuster joined the Data and Web Science (DWS) Group in December 2025 as a Junior Professor for AI Methods for Process Analysis & Management. Since July 2025, he has additionally been affiliated with the Chair of Database Systems, Data Mining, and AI at LMU Munich.

He obtained his PhD from the Chair of Process and Data Science at RWTH Aachen University in July 2023, under the supervision of Wil van der Aalst. Before pursuing his doctoral studies, he completed a Master of Science in Computer Science and a Master of Science in Management, Business, and Economics, both at 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

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)

• tba

We supervise 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 contact us if you are interested.

For an overview of my latest publications, please refer to my researcher profiles on 

• Google Scholar
• ORCID
• DBLP
• Scopus