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.

• 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.