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Joos Buijs

  • MSc.
  • Assistant Professor
  • Technische Universiteit Eindhoven Faculteit Wiskunde en Informatica
  • 12h-indexImpact measure calculated using publication and citation counts. Updated daily.
  • 1203CitationsNumber of citations received by Joos's publications. Updated daily.

About

Joos Buijs is an Assistant Professor in the Architecture of Information Systems Group (AIS) at the Section of Information Systems (IS) of the Department of Mathematics and Computer Science (Math&CS) at the Eindhoven University of Technology (TU/e). Joos Buijs is part of the Data Science Center Eindhoven (DSC/e), while he also works on the topic of process mining in the group of prof. dr. ir. W.M.P. van der Aalst. His current research interests include: Process mining in healthcare: costs are rising in healthcare and increasing process efficiency is one of the key changes to make to make the costs manageable. Applying process mining on the abundance of data available in hospitals, clinics and other medical institutions allows process improvement based on facts and data. Joos is actively looking for case studies and contacts in healthcare. Learning analytics: analysing how students learn can be used to improve efficacy and efficiency of MOOCs, but also physical lectures supported by technology (aka the 'flipped classroom'). By applying process mining on learning data insights in the learning process can be obtained, which have proven to be a valuable to the data mining results. Next to these research topics Joos is also involved in MOOC creation. Related to the learning analytics of course, we also create MOOCs on the topic of process mining. There is the Coursera MOOC "Process Mining: Data Science in Action". And on July 11, 2016 the first session of the FutureLearn MOOC "Process mining with ProM" will launch, a hands-on MOOC where you learn how you can apply process mining on your own data! All these activities are sponsored by the European Data Science Academy (EDSA) project.

Recent publications

  • Detecting change in procebes using comparative trace clustering

    • Hompes B
    • Buijs J
    • Van Der Aalst W
    • et al.
    N/AReaders
    9Citations
  • Enhancing proceb mining results using domain knowledge

    • Dixit P
    • Buijs J
    • Van Der Aalst W
    • et al.
    N/AReaders
    3Citations

Professional experience

Assistant Professor

University of Technology Eindhoven Department of Mathematics and Computer Science

October 2014 - Present

Ph.D. Student

Einhoven University of Technology

May 2010 - July 2014(4 years)

Education

Master of Science

Eindhoven University of Technology

August 2007 - May 2010(3 years)

Co-authors (95)

Other IDs