Organization profile

Introduction / mission

The chair studies data mining (DM) techniques and knowledge discovery approaches that are at the core of data science. The group is known for its contributions to the areas of predictive analytics, automation of machine learning and networked science, subgroup discovery and exceptional model mining, and similarity computations on complex data. Its research is inspired by theoretical computer science, systems development and real-world applications of (big) data-driven discovery in healthcare, banking, energy, retail, telecom, and education among others.

Organisational profile

We develop generic approaches and specialized techniques that cover a wide range of descriptive, predictive and prescriptive analytics and work effectively with text, image, transactional, graph and time-series data in a responsible manner. E.g. we use Deep Learning methods to develop models for high dimensional heterogeneous, unstructured and evolving data and apply this models to areas such as medical imaging, genomics, anomaly detection and sentiment analysis. We further work on methods for analyzing and explaining the model’s decisions and performance and facilitate effective DM with domain expert in the loop.

Success stories

We have created OpenML: an online collaborative platform for studying machine learning techniques. OpenML is used by almost 2,000 researchers, students, and practitioners world-wide, and contains around 20,000 datasets, 3,000 machine learning workflows, and 1,7 million shared experiments. It has won the Dutch Data Prize, as well as backing from Microsoft Research. It is crucial for the development of automated machine learning that is adopted by companies such as Philips.

Further information at OpenML.org 

  • NWO RATE-Analytics (with Tilburg University, Rabobank and Achmea) "Next generation predictive analytics for data-driven banking and insurance".
  • ImpulseKYC-Analytics (with Rabobank) "Know your customer predictive analytics" project aims at developing approaches for effective DM on heterogeneous and evolving data sources with expert-in-the-loop.
  • STW CAPA (with Adversitement and StudyPortals)"Context-aware predictive analytics" advanced the current state of the art in Web analytics.
  • NWO Veni "Detection methods for similarity structures in time-dependent data"develops foundations for advanced time series and trajectories clustering.
  • H2020 SODA (ICT-2016-1; Big Data PPP) "Scalable Oblivious Data Analytics" facilitates secure DM; together with Crypto group we develop practical approaches for DM with multi-party computation.

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  • Network Recent external collaboration on country level. Dive into details by clicking on the dots.

    Projects

    Research Output

    Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning

    Järvelä, S., Gašević, D., Seppänen, T., Pechenizkiy, M. & Kirschner, P. A., 1 Jan 2020, (Accepted/In press) In : British Journal of Educational Technology.

    Research output: Contribution to journalArticleAcademicpeer-review

  • 1 Citation (Scopus)

    Controlling the accuracy and uncertainty trade-off in RUL prediction with a surrogate Wiener propagation model

    Deng, Y., Bucchianico, A. D. & Pechenizkiy, M., Apr 2020, In : Reliability Engineering and System Safety. 196, 10 p., 106727.

    Research output: Contribution to journalArticleAcademicpeer-review

    Open Access
  • 1 Citation (Scopus)

    EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features

    Ahmadi, N., Pei, Y., Carrette, E., Aldenkamp, A. P. & Pechenizkiy, M., 1 Dec 2020, In : Brain Informatics. 7, 1, 22 p., 6.

    Research output: Contribution to journalArticleAcademicpeer-review

    Open Access
  • Prizes

    Third Prize - National Olympiad in Informatics

    Decebal C. Mocanu (Recipient), 1997

    Prize: OtherDiscipline relatedScientific

    Activities

    • 3 Invited talk
    • 1 Keynote talk
    • 1 Visiting an external academic institution

    Machine Learning, better, together.

    Joaquin Vanschoren (Speaker)
    8 Dec 2018

    Activity: Talk or presentation typesInvited talkScientific

    Tutorial on Automatic Machine Learning

    Frank Hutter (Speaker), Joaquin Vanschoren (Speaker)
    3 Dec 2018

    Activity: Talk or presentation typesKeynote talkScientific

    Democratizing and Automating Machine Learning

    Joaquin Vanschoren (Speaker)
    28 Aug 2018

    Activity: Talk or presentation typesInvited talkScientific

    Press / Media

    Beyond human: How artificial intelligence is evolving all by itself

    Joaquin Vanschoren

    20/04/20

    1 item of Media coverage

    Press/Media: Expert Comment

    Artificial intelligence is evolving all by itself

    Joaquin Vanschoren

    13/04/2015/04/20

    2 items of Media coverage

    Press/Media: Expert Comment

    Artificial intelligence automatically develops science

    Joaquin Vanschoren

    14/04/20

    1 item of Media coverage

    Press/Media: Expert Comment

    Student theses

    A framework for understanding business process remaining time predictions

    Author: Verhoef, C., 28 Oct 2019

    Supervisor: Pechenizkiy, M. (Supervisor 1) & Scheepens, R. J. (Supervisor 2)

    Student thesis: Master

    Algorithms for center-based trajectory clustering

    Author: van de L'Isle, N., 28 Jan 2019

    Supervisor: Buchin, K. (Supervisor 1) & Driemel, A. (Supervisor 2)

    Student thesis: Master

    File

    An exploration and evaluation of concept based interpretability methods as a measure of representation quality in neural networks

    Author: Remmits, Y., 30 Sep 2019

    Supervisor: Menkovski, V. (Supervisor 1) & Stolikj, M. (External coach)

    Student thesis: Master

    File