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Persoonlijk profiel

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

Vingerafdruk Duik in de onderzoeksthema's waar Mykola Pechenizkiy actief is. Deze onderwerplabels komen voort uit het werk van deze persoon. Samen vormen ze een unieke vingerafdruk.

Hypermedia systems Engineering en materiaalwetenschappen
Data mining Engineering en materiaalwetenschappen
Students Engineering en materiaalwetenschappen
Classifiers Engineering en materiaalwetenschappen
Information systems Engineering en materiaalwetenschappen
Feature extraction Engineering en materiaalwetenschappen
Association rules Engineering en materiaalwetenschappen
Supervised learning Engineering en materiaalwetenschappen

Netwerk Recente externe samenwerking op landenniveau. Duik in de details door op de stippen te klikken.

Onderzoeksoutput 2002 2019

14 Citaties (Scopus)
9 Downloads (Pure)

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, blz. 27-46

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

Agglomeration
Neural networks
Feedback
Industry
3 Citaties (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, blz. 3030-3044

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

Genetic programming
Association rules
Students
Experiments
6 Citaties (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, blz. 2851-2865

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

Big data
16 Citaties (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, blz. 3059-3072

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

Association rules
Data storage equipment
Data mining
Data structures
Hamming distance
6 Citaties (Scopus)
5 Downloads (Pure)

Mining exceptional relationships with grammar-guided genetic programming

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

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

Genetic programming
Association rules

Cursussen

Capita selecta Data Mining

1/09/16 → …

Cursus

Data mining

1/09/15 → …

Cursus

Responsible Data Science

1/09/19 → …

Cursus

Seminar Data Mining

1/09/16 → …

Cursus

Web analytics

1/09/1331/08/20

Cursus

Pers/media

Data correlation helps recognize pickpockets

Mykola Pechenizkiy

12/07/18

2 items van Media-aandacht

Pers / media: Vakinhoudelijk commentaar

Conference Interpretation Today

Mykola Pechenizkiy

19/04/18

1 item van Media-aandacht

Pers / media: Vakinhoudelijk commentaar

AI Accelerator Program by Rockstart at JADS in Den Bosch

Mykola Pechenizkiy

19/04/17

1 item van Media-aandacht

Pers / media: Vakinhoudelijk commentaar

Europe’s Rockstart accelerator launches an AI track

Mykola Pechenizkiy

12/04/17

2 items van Media-aandacht

Pers / media: Vakinhoudelijk commentaar

Scriptie

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

Auteur: Ongan, M., 31 aug 2011

Begeleider: Pechenizkiy, M. (Afstudeerdocent 1) & Budziak, G. (Externe coach)

Scriptie/masterproef: Master

Bestand

A framework for understanding business process remaining time predictions

Auteur: Verhoef, C., 28 okt 2019

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

Scriptie/masterproef: Master

Bestand

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

Auteur: Cremers, L., 31 jul 2018

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

Scriptie/masterproef: Master

Bestand

Analyzing machine data for predictive maintenance of electro chemical machining electrodes

Auteur: Wang, F., 26 sep 2016

Begeleider: Pechenizkiy, M. (Afstudeerdocent 1), Wouters, K. (Externe persoon) (Externe coach) & Gao, K. (Externe persoon) (Externe coach)

Scriptie/masterproef: Master

Bestand