Assistant Professor

Massimiliano de Leoni

... the unexamined life is not worth living" – Plato

Group / Unit
Information Systems WSK&I

Research Profile

Massimiliano de Leoni is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e).   His research is set around Information Systems and Business Intelligence, on the hand, and Data Science, on the other hand. Information Systems record executions of the underlying processes in event logs. In the new era of Big Data, these event logs are quickly becoming richer and richer because, on the one hand, information systems are more pervasive (e.g., in home automation or logistics) and connected with external systems and, on the other hand, these event logs can be augmented with additional data that come from external "data factories, including social medias, geo-referenced physical objects (e.g. via RFID tags), questionnaires, etc. The large availability of process data is more than just a matter of volume and all the related challenges. Compared with traditional Business Intelligence, this is an opportunity to gain actionable insights to help organizations make better business decisions and become more effective and competitive

Academic Background

Massimiliano de Leoni holds a BSc in Computer Engineering fro Sapienza University of Rome (with honors, marks 110/110), an MSc from Universita di Roma ‘La Sapienza’ (with honors, marks 110/110), and a PhD in in Computer Engineering from the same University. He held postdoc positions in the Architecture of Information Systems (AIS) group, TU/e and in the Department of Computer, Control, and Management Engineering, Sapienza University of Rome. He was visiting researcher in several universities world-wide and, in 2018, Visiting Professor at Università Politecnica delle Marche.
Current research is mainly concerned with solving problems relative to the field of business process management, analysis and mining in the context of ‘big event logs’. The large availability of process data is more than just a matter of volume and related challenges. Compared with traditional business intelligence, it is an opportunity to gain actionable insights to aid organizations to make better business decisions and become more effective and competitive. This touches on areas such as decision support, multi-perspective process mining, visual analytics and process mining, as well as conformance checking and discovery of artifact-centric business processes and declarative processes  

Ancillary Activities

No ancillary activities