I am an assistant professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology, where I lead a research line on neuro-symbolic AI in Process Analytics group. I also lead a process mining group at Fraunhofer FIT. I received my PhD in Computer Science from RWTH Aachen University with the thesis Action-oriented Process Mining, my MSc in Industrial and Management Engineering from POSTECH, and my BSc in Technology Management and Computer Science from UNIST.

Research vision

Organizations record digital traces of who did what, when, and for which case. My research develops data science methods that learn from these traces to predict and steer what happens next, in a way that organizations can trust. The methodological core is neuro-symbolic process learning: learning systems that absorb explicit process knowledge, such as clinical guidelines, safety regulations and documented procedures, both as a prior that shapes what a model believes and as a guardrail that bounds what it predicts and recommends. The foundation is process science, machine learning and statistics. The application focus is healthcare, manufacturing and public-sector organizations, where unconstrained learning is unusable and pure rule-following is too rigid.

Research lines

• Neuro-symbolic process learning: the methodological core. Formal process knowledge from guidelines, regulations and procedures enters learning systems as a prior and as a guardrail, for prediction, recommendation and anomaly detection.
• Object-centric process mining: extraction, standards, analysis and tooling for event data in which several objects interact, from SAP ERP to manufacturing and healthcare.
• Prescriptive process analytics and digital twins: predictive monitoring, recommended actions, simulation and digital twins of organizations that close the loop from insight to improvement.
• Process discovery and diagnostics: sound yet expressive process models, decomposition, performance patterns, deviations and resource effects, the descriptive side that the other lines build on.

Off the screen

As much as I enjoy doing research sitting in front of a monitor, I enjoy running, cycling and swimming just as much. I like listening to music, even better with people and drinks. I am proud of the kimchi I make with Marlene, though it will never compare to my mom’s.