Έχει συμμετάσχει σε δεκάδες εθνικά και ευρωπαϊκά ερευνητικά προγράμματα, ανάμεσα στα οποία τα Horizon 2020/Horizon Europe AIXPERT, ALLIES, DIONE, LevelUp και ORBIS, σε ρόλους Principal Investigator και Technical Coordinator. Διατελεί κριτής σε πλήθος διεθνών επιστημονικών περιοδικών και μέλος επιστημονικών επιτροπών πολλών διεθνών συνεδρίων. Συμμετέχει στα Editorial Boards διεθνών περιοδικών - μεταξύ άλλων των Evolving Systems και Neural Computing & Applications - ως Area Editor και Associate Editor, ενώ έχει διατελέσει Co-Editor σε ειδικά τεύχη κορυφαίων περιοδικών με θέματα Μηχανικής Μάθησης, Ensemble Learning και Χρονοσειρών.
Ioannis E. Livieris is an Associate Professor in the Department of Management Science and Technology at the University of Peloponnese, with a research focus on Quantitative Methods and Machine Learning for Data Analysis. He received his PhD in Mathematics and Computational Intelligence from the University of Patras in 2012. He has co-authored more than 67 papers in international scientific journals, 15 book chapters, and 19 conference papers, along with 2 scientific books. With an h-index of 32 and over 4,500 citations (Google Scholar), his research has received international recognition, including his recurring inclusion in the Stanford List of World's Top Scientists - ranking in the top 2% of scientists worldwide in both single year impact and lifetime impact.
He has participated in dozens of national and European research projects, including the Horizon 2020/Horizon Europe projects AIXPERT, ALLIES, DIONE, LevelUp, and ORBIS, serving as Principal Investigator and Technical Coordinator. He serves as a reviewer for numerous international scientific journals and as a member of program committees of many international conferences. He is a member of the Editorial Boards of international journals — including Evolving Systems and Neural Computing & Applications - serving as Area Editor and Associate Editor, and has served as Co-Editor of special issues in leading journals on topics related to Machine Learning, Ensemble Learning, and Time-Series Forecasting.
- Μηχανική Μάθηση & Βαθιά Μάθηση
- Αριθμητική Βελτιστοποίηση
- Χρονοσειρές
- Εφαρμογές Τεχνητής Νοημοσύνης
- Μαθηματικός Προγραμματισμός
- Machine learning & Deep Learning
- Numerical Optimization
- Time-series
- AI applications
- Mathematical Programming
- I.E. Livieris and P. Pintelas. Globally convergent modified Perry conjugate gradient method.Applied Mathematics and Computation, Vol. 218, No. 18, pp. 9197–9207, 2012.
- I.E. Livieris and P. Pintelas. A new class of spectral conjugate gradient methods based on a modified secant equation for unconstrained optimization.Journal of Computational and Applied Mathematics, Vol. 239, pp. 396–405, 2013.
- I.E. Livieris and P. Pintelas. A limited memory descent Perry conjugate gradient method.Optimization Letters, Vol. 10, No. 8, pp. 1725–1742, 2016.
- I.E. Livieris and P. Pintelas. An adaptive nonmonotone active set weight-constrained neural network training algorithm.Neurocomputing, pp. 294–303, 2019.
- I.E. Livieris and P. Pintelas. An advanced active set L-BFGS algorithm for training constrained neural networks.Neural Computing and Applications, 2019.
- I.E. Livieris, E. Pintelas and P. Pintelas. A CNN-LSTM model for gold price time series forecasting.Neural Computing and Applications, 2020.
- I.E. Livieris, S. Stavroyiannis, L. Iliadis and P. Pintelas. Smoothing and stationarity enforcement framework for deep learning time-series forecasting.Neural Computing and Applications, 2021.
- I.E. Livieris and P. Pintelas. A novel multi-step forecasting strategy for enhancing deep learning models' performance.Neural Computing and Applications, Vol. 34, pp. 19453–19470, 2022.
- I.E. Livieris. A novel forecasting strategy for improving the performance of deep learning models.Expert Systems with Applications, 2023.
- E. Pintelas, I.E. Livieris and P. Pintelas. Quantization-based 3D-CNNs through circular gradual unfreezing for DeepFake detection.IEEE Transactions on Artificial Intelligence, 2025.
