Ivan Izonin

About

I am a Professor in the Department of Artificial Intelligence at Lviv Polytechnic National University, Ukraine. I earned an MSc in Computer Science from Lviv Polytechnic National University in 2011 and an MSc in Economic Cybernetics from Ivan Franko National University of Lviv in 2012. I received my PhD in Artificial Intelligence from Lviv Polytechnic National University in 2016 and the Doctor of Sciences (DSc) degree in 2025 (a higher doctorate, post-PhD level, awarded in Ukraine). My research interests include high-speed computational intelligence, neural-like structures, neuro-fuzzy systems, non-iterative training algorithms, ensemble learning, meta-learning, and small-data analysis.

I currently work at the High-speed Computational Intelligence Lab at Lviv Polytechnic National University. Our lab focuses on information modeling based on a new ANN design concept—the Successive Geometric Transformations Model (SGTM). SGTM provides solutions for a wide range of tasks, including pattern recognition, forecasting, classification, optimization, missing-data recovery and consolidation, as well as information protection and privacy. SGTM-based neural-like structures, viewed as universal approximators, support both supervised training and self-training and can be implemented in algorithmic or hardware forms. The framework enables fast, non-iterative learning with a predefined number of computational steps, ensuring reproducible training outcomes and effective performance on both large datasets and small samples.

Employment

• Lviv Polytechnic National University Associate Professor
  2020 - Present
• Lviv Polytechnic National University Assistant Professor
  2015 - 2020

Education

• Lviv Polytechnic National University Doctor of Technical Sciences (DSc in Artificial Intelligence) - higher doctorate degree (post-PhD level) awarded in Ukraine.
  2023 - 2025
• Lviv Polytechnic National University Ph.D. (Systems and Means of Artificial Intelligence)
  2012 - 2015
• Lviv Polytechnic National University B.Sc., M.Sc. in Computer Science
  2007 - 2011
• Ivan Franko National University of Lviv B.Sc., M.Sc. in Economic Cybernetics
  2006 - 2012

Projects & Funding

• Innovative methodologies for the design of Zero-Emission and cost-effective Buildings enhanced by Artificial Intelligence, ZEBAI UK Research and Innovation
  2024 - 2028
• Artificial Intelligence in Medicine: Small Data Approach European Union (EURIZON Project)
  2024 - 2025

Publications (201)

• GateTS: Versatile and efficient forecasting via attention-inspired routed mixture-of-experts
  Neurocomputing 2027 DOI: 10.1016/j.neucom.2026.135112
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• Iterative ensemble learning for tendon loss identification in bridges
  Results in Engineering 2026 DOI: 10.1016/j.rineng.2026.113935
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• The Competence Envelope: certifying when AI and its explanations can be trusted
  Zenodo 2026 DOI: 10.5281/zenodo.20771264
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• A Method for Selecting Base Models in Stacking Ensembles Using Prediction and Error Diversity Measures
  2026 DOI: 10.3233/FAIA260668
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• Artificial Intelligence and the future of Construction
  Zenodo 2026 DOI: 10.5281/zenodo.21268246
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• Post-Quantum Cryptography and Nature-Inspired Cyber Defense: Strategic Readiness and Adaptive Techniques for Next-Gen Threat Response
  Shishir Kumar Shandilya, Chirag Ganguli, Ajay Kumar +2 more
  IEEE Access 2026 DOI: 10.1109/ACCESS.2026.3663832
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• Roadmap: Integrating artificial intelligence in structural health monitoring systems
  Measurement Science and Technology 2026 DOI: 10.1088/1361-6501/AE3ABB
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• Adaptive neo-fuzzy bagging metamodel and its fast learning
  2025 DOI: 10.31110/COLINS/2025-2/004
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• An enhanced cascade ensemble method for big data analysis
  IAES International Journal of Artificial Intelligence (IJ-AI) 2025 DOI: 10.11591/ijai.v14.i2.pp963-974
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• Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data Case
  Computation 2025 DOI: 10.3390/computation13040080
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• Enhancing the FFT-LSTM Time-Series Forecasting Model via a Novel FFT-Based Feature Extraction–Extension Scheme
  Big Data and Cognitive Computing 2025 DOI: 10.3390/bdcc9020035
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• Time Series Forecasting Model Based on the Adapted Transformer Neural Network and FFT-Based Features Extraction
  Sensors 2025 DOI: 10.3390/s25030652
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• AI-Driven Evaluation of Genetic Biomarkers for Predicting Acute Kidney Transplant Rejection Using Small Datasets
  HLA 2025
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• Bridge damage characterisation using machine learning: methods and advances
  Results in Engineering 2025 DOI: 10.1016/J.RINENG.2025.107192
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• Contributors
  Dayal Kumar Behera, Subhranil Das, Anuradha Dhull

  +25 more

  • Anastasiya Doroshenko
  • Priyansh Praveen Garg
  • Anshita Gera
  • Soma Gupta
  • Myroslav Havryliuk
  • +20 more
  Responsible and Explainable Artificial Intelligence in Healthcare 2025 DOI: 10.1016/b978-0-443-24788-0.01002-5
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• GRNN-based cascade ensemble model for non-destructive damage state identification: small data approach
  Engineering with Computers 2025 DOI: 10.1007/s00366-024-02048-1
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• GateTS: Versatile and Efficient Forecasting via Attention-Inspired routed Mixture-of-Experts
  ArXiv 2025
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• Linear ensemble model with winner-takes-all aggregation strategy for improved small data classification
  Ceur Workshop Proceedings 2025
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• Load-Balancing Strategies for Forecasting with Mixture-of-Experts Architecture
  Procedia Computer Science 2025 DOI: 10.1016/j.procs.2025.10.191
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• Mortality Risk Prediction for Multiple Trauma Patients Admitted to the Hospital via Machine Learning Algorithms
  Lecture Notes on Data Engineering and Communications Technologies 2025 DOI: 10.1007/978-3-031-84228-3_18
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