2026 Mykola Pechenizkiy: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	48	6052	5879	97	89	385	13778

Overview

Mykola Pechenizkiy is affiliated with Eindhoven University of Technology in the Netherlands. Their research contributions primarily lie within the broad field of Computer Science, with a significant focus on Artificial Intelligence. They have published extensively in areas including Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Management Science and Operations Research, and Information Systems.

The scientist's work covers several key topics, notably Domain Adaptation and Few-Shot Learning, Advanced Neural Network Applications, Adversarial Robustness in Machine Learning, Advanced Graph Neural Networks, Anomaly Detection Techniques and Applications, Complex Network Analysis Techniques, and Machine Learning and Data Classification.

Frequent publication venues where their research appears include arXiv (Cornell University), Machine Learning, Data Mining and Knowledge Discovery, Proceedings of the AAAI Conference on Artificial Intelligence, and Neural Computing and Applications.

Mykola Pechenizkiy has collaborated extensively with several researchers. The most frequent coauthors are Decebal Constantin Mocanu, Yulong Pei, Tianjin Huang, Meng Fang, and Vlado Menkovski.

Selected recent research papers include:

Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning (2020), British Journal of Educational Technology
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity (2022), arXiv (Cornell University)
Ensuring cybersecurity of smart grid against data integrity attacks under concept drift (2020), International Journal of Electrical Power & Energy Systems
EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features (2020), Brain Informatics
ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks (2021), Machine Learning

Best Publications

A survey on concept drift adaptation

João Gama;Indrė Žliobaitė;Albert Bifet;Mykola Pechenizkiy

3373
Citations
Handbook of Educational Data Mining

Cristobal Romero;Sebastian Ventura;Mykola Pechenizkiy;Ryan S.J.d. Baker

660
Citations
Predicting Students Drop Out: A Case Study

GW Gerben Dekker;M Mykola Pechenizkiy;JM Jan Vleeshouwers

574
Citations
Building Classifiers with Independency Constraints

Toon Calders;Faisal Kamiran;Mykola Pechenizkiy

529
Citations
An Overview of Concept Drift Applications

Indrė Žliobaitė;Indrė Žliobaitė;Indrė Žliobaitė;Mykola Pechenizkiy;João Gama

437
Citations
Discrimination Aware Decision Tree Learning

Faisal Kamiran;Toon Calders;Mykola Pechenizkiy

399
Citations
Diversity in search strategies for ensemble feature selection

Alexey Tsymbal;Mykola Pechenizkiy;Pádraig Cunningham

393
Citations
What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data

Jorn Bakker;Mykola Pechenizkiy;Natalia Sidorova

358
Citations
Dynamic integration of classifiers for handling concept drift

Alexey Tsymbal;Mykola Pechenizkiy;Pádraig Cunningham;Seppo Puuronen

295
Citations
AH 12 years later: a comprehensive survey of adaptive hypermedia methods and techniques

Evgeny Knutov;Paul De Bra;Mykola Pechenizkiy

290
Citations
Stress detection from speech and Galvanic Skin Response signals

Hindra Kurniawan;Alexandr V. Maslov;Mykola Pechenizkiy

277
Citations
Handling concept drift in process mining

R. P. Jagadeesh Chandra Bose;Wil M. P. van der Aalst;Indre Žliobaite;Mykola Pechenizkiy

272
Citations
Dealing With Concept Drifts in Process Mining

R. P. Jagadeesh Chandra Bose;Wil M. P. van der Aalst;Indre Zliobaite;Mykola Pechenizkiy

238
Citations
Feedback Loop and Bias Amplification in Recommender Systems

Masoud Mansoury;Himan Abdollahpouri;Mykola Pechenizkiy;Bamshad Mobasher

172
Citations
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity

Unknown

152
Citations
Introduction to the special section on educational data mining

Toon Calders;Mykola Pechenizkiy

130
Citations
Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction

M. Pechenizkiy;A. Tsymbal;S. Puuronen;O. Pechenizkiy

128
Citations
Dynamic integration with random forests

Alexey Tsymbal;Mykola Pechenizkiy;Padraig Cunningham

111
Citations
Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning

Sanna Järvelä;Dragan Gašević;Tapio Seppänen;Mykola Pechenizkiy

109
Citations
Feedback adaptation in web-based learning systems

Ekaterina Vasilyeva;Seppo Puuronen;Mykola Pechenizkiy;Pekka Rasanen

99
Citations
Stess@Work: from measuring stress to its understanding, prediction and handling with personalized coaching

Jorn Bakker;Leszek Holenderski;Rafal Kocielnik;Mykola Pechenizkiy

96
Citations
Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems

Pedro Pereira Rodrigues;Mykola Pechenizkiy;João Gama;Faculdade de Economia

64
Citations
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training

Shiwei Liu;Lu Yin;Decebal Constantin Mocanu;Mykola Pechenizkiy

32
Citations

Frequent Co-Authors

Toon Calders University of Antwerp

Sebastián Ventura University of Córdoba

Bamshad Mobasher DePaul University

Cristóbal Romero University of Córdoba

Pádraig Cunningham University College Dublin

João Gama University of Porto

Robin Burke University of Colorado Boulder

Mohamed Medhat Gaber Birmingham City University

Dragan Gasevic Monash University

Karl Tuyls DeepMind (United Kingdom)

External Links

Personal website of Mykola Pechenizkiy Google Scholar page

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