Welcome to my personal website!

I am a Professor of Mathematics and Informatics at the Department of Economics of the National and Kapodistrian University of Athens. My current principal interests include but are not limited to Neural Networks, Intelligent Optimization, Numerical Linear Algebra, Linear and Multilinear Algebra and Mathematical Finance. On this site you can find information about my current research, publications, courses taught, and other items that may be of interest to you.

New Special Issue: Next-Gen Neural Networks: Advances, Challenges, and Real-World Applications

Guest Editors
Prof. Vasilios N. Katsikis
Department of Economics, Director of the Division of Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Athens, 10559 Greece
Email: vaskatsikis@econ.uoa.gr
Research Interests: Artificial intelligence; computational optimization; intelligent optimization; computational finance; mathematical finance
Website: http://scholar.uoa.gr/vaskatsikis
Prof. Theodore E. Simos
1. Center for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, West Mishref, Mubarak Al-Abdullah 32093, Kuwait, 2. Section of Mathematics, Department of Civil Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Email: simos@ulstu.ru
Research Interests: Scientific Computation; Applied Numerical Analysis; Computational Chemistry; Computational Material Sciences; Computational Physics; The numerical solution of the Schrödinger equation and related problems; The numerical solution of Partial Differential Equations of the Schrödinger type; The numerical solution of first and second order periodic and oscillatory initial-value problems; The numerical solution of second-order boundary-value problems; The numerical solution of engineering, physical chemistry and chemical physics problems; Parallel algorithms and expert systems; Development of software packages; Constraint optimization problems.
Website: http://theodoresimos.org/
Prof. Spyridon D. Mourtas
Department of Economics, Director of the Division of Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Athens, 10559 Greece
Email: spirmour@econ.uoa.gr
artificial intelligence; computational optimization; intelligent optimization; computational finance; mathematical finance
Website: http://users.uoa.gr/~spirmour/
Manuscript Topics

Neural networks are at the core of modern artificial intelligence, driving advancements in various domains such as computer vision, natural language processing, robotics, healthcare, and autonomous systems. While deep learning has achieved remarkable success, challenges such as explainability, robustness, efficiency, and adaptability remain critical research areas.
This Special Issue aims to showcase cutting-edge research and practical applications of next-generation neural networks. We invite original contributions on novel architectures, optimization techniques, interdisciplinary applications, and emerging trends that push the boundaries of neural network capabilities. Topics of interest include, but are not limited to:


• Innovative neural network architectures (transformers, graph neural networks, spiking neural networks, etc.)
• Efficient and scalable learning (low-power AI, federated learning, edge computing)
• Explainable and interpretable neural networks
• Robustness and security in neural networks (adversarial attacks, bias mitigation, privacy-preserving AI)
• Hybrid AI approaches (neural-symbolic integration, neuroevolution, quantum AI)
• Applications in healthcare, finance, industry 4.0, and beyond
• Human-AI collaboration and interactive neural networks




We welcome original research papers, review articles, and case studies that provide novel insights into the future of neural networks.
Keywords
• Neural Networks
• Deep Learning
• Explainable AI (XAI)
• Transfer Learning
• Edge AI
• Federated Learning
• Self-Supervised Learning
• Adversarial Robustness
• Neuro-Symbolic AI
• Quantum Neural Networks
• Efficient AI Models
• Real-World AI Applications




Instruction for Authors    
https://www.aimspress.com/math/news/solo-detail/instructionsforauthors   
Please submit your manuscript to online submission system    
https://aimspress.jams.pub/




Paper Submission


All manuscripts will be peer-reviewed before their acceptance for publication. The deadline for manuscript submission is 31 August 2025
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 Recent Publications

  
  

  
  
  
  

    He, Y., Wang, X., Tie, Y., Yang, H., Simos, T. E., Mourtas, S. D., & Katsikis, V. N. (2025). Solving Lur'e equations through zeroing neural networks. Information Sciences, 718, 122418. WebsiteAbstract
Solving Lur'e equations plays a critical role in addressing linear-quadratic optimal control (LQOC) problems, especially in cases where the control cost matrices are singular. This paper introduces, for the first time, two novel zeroing neural network (ZNN) models—ZNNLE and ZNNLE-LQOC—specifically designed to solve the Lur'e equation system and the LQOC problem, respectively. The proposed models extend the applicability of the ZNN methodology to these challenging scenarios by offering robust and efficient solutions to time-varying matrix equations. Theoretical analyses confirm the validity of both models, while numerical simulations and practical applications demonstrate their effectiveness. Moreover, a comparative study with an enhanced alternating-direction implicit (ADI) method highlights the superior performance of the ZNNLE-LQOC model in solving LQOC problems.
  


  
  
  



  
  
  
  

    Katsikis, V. N., Liao, B., & Hua, C. (2025). Survey of Neurodynamic Methods for Control and Computation in Multi-Agent Systems. Symmetry, 17. WebsiteAbstract
Neurodynamics is recognized as a powerful tool for addressing various problems in engineering, control, and intelligent systems. Over the past decade, neurodynamics-based methods and models have been rapidly developed, particularly in emerging areas such as neural computation and multi-agent systems. In this paper, we provide a brief survey of neurodynamics applied to computation and multi-agent systems. Specifically, we highlight key models and approaches related to time-varying computation, as well as cooperative and competitive behaviors in multi-agent systems. Furthermore, we discuss current challenges, potential opportunities, and promising future directions in this evolving field.
  


  
  
  



  
  
  
  

    Yang, Y., Wu, P., Katsikis, V. N., Li, S., & Feng, W. (2025). A novel real-time noise-resilient zeroing neural network and its applications to matrix problem solving. Mathematics and Computers in Simulation. WebsiteAbstract
Given the critical role of zeroing neural networks (ZNN) in various fields and the practical demand for models in effectively resisting real-time noise, this study introduces a novel anti-noise integral zeroing neural network (AN-IZNN) model alongside its enhanced counterpart (EAN-IZNN), for the applications of matrix problem solving. Theoretical analysis demonstrates their ability to achieve convergence even under different noise conditions. Both theoretical analyses and simulation validations highlight the superior performance of the proposed models over existing neural network models. Notably, the root mean square error of the proposed AN-IZNN and EAN-IZNN models is reduced by 92.6249% and 91.4178%, respectively, compared to scenarios without the proposed method, demonstrating the effectiveness of the solution.
  


  
  
  





  
  
  
  

    Katsikis, V. N., Mourtas, S. D., Sahas, R., & Balios, D. (2024). A Weights Direct Determination Neural Network for Credit Card Attrition Analysis. In L. A.  Maglaras, Das, S., Tripathy, N., & Patnaik, S. (Eds.), Machine Learning Approaches in Financial Analytics (pp. 325–346). Cham: Springer Nature Switzerland. WebsiteAbstract
Cost reduction is a component that contributes to both the profitability and longevity of a corporation, especially in the case of a financial institution, and can be accomplished through greater client retention. Particularly, credit card customers comprise a volatile subset of a bank's client base. As such, banks would like to predict in advance which of those clients are likely to attrite, so as to approach them with proactive marketing campaigns. Credit card attrition is generally a poorly investigated subtopic with a variety of challenges, like highly imbalanced datasets. This article utilizes neural networks to address the challenges of credit card attrition since they have found great application in many classification problems. More particularly, to overcome the shortcomings of traditional back propagation neural networks, we construct a multi-input trigonometrically activated weights and structure determination (MTA-WASD) neural network which incorporates structure trimming as well as other techniques that boost its training speed as well as diminish the danger and the subsequent detrimental effects of overfitting. When applied to three publicly available datasets, the MTA-WASD neural network demonstrated either superior or highly competitive performance across all metrics, compared to some of the best-performing classification models that MATLAB's classification learner app offers.
  


  
  
  






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