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Personal profile

Quote

“Inspired by challenges of real-world applications, I develop the foundation for next generation predictive analytics.”

Research profile

Mykola Pechenizkiy is a Full Professor at the department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e), where he holds the Data Mining Chair. His research interests include data science, knowledge discovery and data mining, responsible analytics, including ethics/discrimination-awareness, context-aware predictive analytics, handling concept drift and reoccurring contexts, automation of feature construction and analytics on evolving networks.

His core expertise and research interests are in predictive analytics and knowledge discovery from evolving data, and in their application to real-world problems in industry, medicine and education. At the Data Science Center Eindhoven, he leads the Customer Journey interdisciplinary research program aiming at developing techniques for informed and responsible analytics.

Academic background

Mykola Pechenizkiy received his PhD from the Computer Science and Information Systems department at the University of Jyväskylä, Finland in 2005. In addition to his work at TU/e, he is an Adjunct Professor in Data Mining for Industrial Applications at the Department of Mathematical Information Technology the University of Jyväskylä. He has also been a visiting researcher at several universities, including Aalto University, University of Bournemouth, Columbia University, University of Cordoba, New York University, University Porto, University of Technology Sydney, Trinity College Dublin, and the University of Ulster.

Mykola has co-authored more than 100 peer-reviewed publications. He has been involved in organizing several successful conferences, thematic workshops and special issues with journals. He regularly serves on several program committees of leading data mining and AI conferences, including AAAI, IJCAI, ECMLPKDD, EDM, LAK, IDA, DSAA, DS, AISTATS, NIPS, SDM and editorial boards of DAMI, IEEE TLT and JEDM journals. He serves as the President of IEDMS, the International Educational Data Mining Society. 

Affiliated with

Partners in (semi-)industry 

  • Adversitement
  • ASML
  • Betabit
  • C-Content
  • Coosto
  • IMEC
  • Microsoft
  • Multiscope
  • NLR
  • Océ
  • Philips Lighting
  • Philips Research
  • PSV
  • Rabobank
  • Sanoma Media Group
  • Sligro Food Group
  • StudyPortals
  • Teezir

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

Research Output 2002 2019

13 Citations (Scopus)

A white-box anomaly-based framework for database leakage detection

Costante, E., den Hartog, J., Petkovic, M., Etalle, S. & Pechenizkiy, M., 2017, In : Journal of Information Security and Applications. 32, p. 27-46

Research output: Contribution to journalArticleAcademicpeer-review

Agglomeration
Neural networks
Feedback
Industry
3 Citations (Scopus)

Mining context-aware association rules using grammar-based genetic programming

Luna, J. M., Pechenizkiy, M., del Jesus, M. J. & Ventura, S., 1 Nov 2018, In : IEEE Transactions on Cybernetics. 48, 11, p. 3030-3044

Research output: Contribution to journalArticleAcademicpeer-review

Genetic programming
Association rules
Students
Experiments
4 Citations (Scopus)

Apriori versions based on MapReduce for mining frequent patterns on big data

Luna, J. M., Padillo, F., Pechenizkiy, M. & Ventura, S., 2018, In : IEEE Transactions on Cybernetics. 48, 10, p. 2851-2865

Research output: Contribution to journalArticleAcademicpeer-review

Big data
15 Citations (Scopus)

Speeding-up association rule mining with inverted index compression

Luna, J. M., Cano, A., Pechenizkiy, M. & Ventura, S., 2016, In : IEEE Transactions on Cybernetics. 46, 12, p. 3059-3072

Research output: Contribution to journalArticleAcademicpeer-review

Association rules
Data storage equipment
Data mining
Data structures
Hamming distance
6 Citations (Scopus)

Mining exceptional relationships with grammar-guided genetic programming

Luna, J. M., Pechenizkiy, M. & Ventura, S., Jun 2016, In : Knowledge and Information Systems. 47, 3, p. 571-594 24 p.

Research output: Contribution to journalArticleAcademicpeer-review

Genetic programming
Association rules

Courses

Capita selecta Data Mining

1/09/16 → …

Course

Data mining

1/09/15 → …

Course

Responsible Data Science

1/09/19 → …

Course

Seminar Data Mining

1/09/16 → …

Course

Web analytics

1/09/1331/08/20

Course

Student theses

A framework for aligning social media and web analytics data to support search engine marketing

Author: Ongan, M., 31 Aug 2011

Supervisor: Pechenizkiy, M. (Supervisor 1) & Budziak, G. (External coach)

Student thesis: Master

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A method for identifying undesired medical treatment variants using process and data mining techniques

Author: Cremers, L., 31 Jul 2018

Supervisor: Vanderfeesten, I. (Supervisor 1), Medeiros de Carvalho, R. (Supervisor 2) & Pechenizkiy, M. (Supervisor 2)

Student thesis: Master

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Analyzing machine data for predictive maintenance of electro chemical machining electrodes

Author: Wang, F., 26 Sep 2016

Supervisor: Pechenizkiy, M. (Supervisor 1), Wouters, K. (External person) (External coach) & Gao, K. (External person) (External coach)

Student thesis: Master

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Analyzing the treatment of DM2 patients using process mining

Author: Majoor, S., 2018

Supervisor: Fahland, D. (Supervisor 1), Pechenizkiy, M. (Supervisor 2) & Kaymak, U. (Supervisor 2)

Student thesis: Master

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