• Source: Scopus
20122021

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Personal profile

Research profile

Felix Mannhardt is an Assistant Professor in the Process Analytics group at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). He is also co-founder of KIT-AR and works there as Principal Data Scientist (part-time). KIT-AR is a spin-off company created out of the EU H2020 project HUMAN Manufacturing and currently building an innovate industrial AR solution.

Felix's research interest is the development and application of Process Mining methods related to low-level event data (event abstraction, activity recognition, sensor data, multi-perspective event logs), related to conformance checking (combination of control-flow with rules over data), as well as related to trust and privacy concerns (privacy-preserving methods, privacy and trust models).

Academic background

Felix obtained his PhD from Eindhoven University of Technology in 2018 based on his thesis on Multi-perspective Process Mining under the supervision of Hajo Reijers and Wil van der Aalst. The thesis received awards both from the academic community through the Process Mining Dissertation Award 2019 at the 1st International Conference on Process Mining (ICPM) as well as from the industrial side with the Cleonis Thesis Award at Celosphere 2019. After his PhD, he worked for three years as Research Scientist at the Norwegian research institute SINTEF in Trondheim and was appointed part-time Associate Professor at the Norwegian University of Science and Technology (NTNU) in 2019. Felix published more 30 peer-reviewed publications and also contributed contributed significantly to the leading open-source Process Mining frameworks ProM and bupaR. He organised two workshops on Trust and Privacy aspects of Process Analytics at major conferences (I3E and ICPM) and served on the program committe of several workshops as well as the BPM and ICPM conferences.

Quote

Understanding the right granularity of data and refining it to that granularity are fundamental problems for data and process science both regarding generating value from data and regarding ethical questions.

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