جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Data Analytics
• Social Media Analysis
• +۵ مورد دیگر

Dr. Paraskevas Koukaras is an Academic Scholar at the International Hellenic University (IHU) and a Postdoctoral Research Associate at the Information Technologies Institute (ITI) of the Centre for Research and Technology - Hellas (CERTH), affiliated with the School of Science and Technology at IHU. His educational background includes: IT Engineer from the Department of Informatics, Alexander Technological Educational Institute of Thessaloniki (ATEI) PhD in Interdisciplinary data science methods using machine learning for enhanced knowledge acquisition from the School of Science and Technology, International Hellenic University (IHU) Dr. Koukaras' research spans social media analytics, energy systems, and machine learning. His work focuses on energy load forecasting and optimization, data analytics, information modeling, and graph mining in heterogeneous networks. He applies these techniques to address challenges in public health, financial markets, and building energy efficiency through prescriptive analytics. His recent publications (2020-2023) demonstrate a strong focus on applying machine learning to real-world problems, particularly in social media analysis for public health during the COVID-19 pandemic, energy forecasting, and fake news detection. His work consistently employs multi-model approaches leveraging both traditional and deep learning techniques across healthcare, finance, and energy domains. Dr. Koukaras has participated in major European research projects including eDREAM (H2020), DRIMPAC (H2020), PRECEPT, SmartWins, easySRI, and SMACCs, focusing on demand response technologies and energy ecosystems. He contributes to academic training through teaching courses in data science and computing despite no formal advisees being listed. He operates within interdisciplinary research teams at ITI/CERTH and IHU's School of Science and Technology, collaborating on AI applications for energy efficiency, healthcare support, and social media analytics in residential and industrial contexts.

Angelo Sifalerasمشاهده پروفایل

استاد

• Mathematical Programming
• Network Optimization
• Heuristic Methods
• +۲ مورد دیگر

Angelo Sifaleras is a Full Professor at the Department of Applied Informatics, School of Information Sciences, University of Macedonia (Thessaloniki, Greece). He also serves as an Adjunct Professor of Quantitative Methods at the Hellenic Open University. His academic credentials include a Ph.D. in Applied Informatics (University of Macedonia, 2007) and a B.Sc. in Mathematics (Aristotle University of Thessaloniki, 1999). Research Focus: Professor Sifaleras specializes in Mathematical Programming , Network Optimization , and Heuristic Methods . His work integrates computational optimization with real-world logistics, supply chains, and sustainable systems. Key research themes include metaheuristic algorithms for vehicle routing, pollution-aware logistics, and parallel computing solutions for network analysis. Publication Trends: His 15 most recent articles emphasize hybrid metaheuristics (e.g., combining reinforcement learning with Variable Neighborhood Search), sustainable supply chain modeling, and high-performance computing applications. Dominant domains include operations research, environmental optimization, and educational algorithm visualization. Teaching & Service: He instructs courses in Combinatorial Optimization, Linear Algebra, and Heuristic Methods, employing tools like CPLEX, Gurobi, and SageMath. As an editor for Operations Research Forum and the Yugoslav Journal of Operations Research , he supports academic discourse in optimization. A Senior ACM member, he contributes to conferences like ICVNS and ITS.

Nikolaos Bakasمشاهده پروفایل

استادیار

• Machine Learning
• Numerical Methods
• High-Performance Computing
• +۴ مورد دیگر

Dr. Nikolaos Bakas serves as an Assistant Professor in the Information Technology Department at the School of Liberal Arts and Sciences, The American College of Greece, Deree. His academic profile centers on bridging theoretical mathematics with practical machine learning implementations through rigorous algorithmic development. His research program focuses on fundamental mathematical modeling of machine learning systems , with specialized expertise in Numerical Methods and High-Performance Computing (HPC) . Key contributions include stochastic optimization algorithms (notably the ITSO framework), gradient-free neural network training techniques, and computational mechanics applications. This interdisciplinary work connects theoretical mathematics with engineering solutions in structural analysis and environmental risk assessment. Analysis of his 2019-2023 publications reveals a clear trajectory toward practical HPC implementations of machine learning frameworks, with increasing emphasis on domain-specific applications. His research consistently addresses computational efficiency challenges while maintaining mathematical rigor, particularly in stochastic search methods and partition-based approximation systems. As Principal Investigator for multiple industry and academic projects, Dr. Bakas demonstrates active research leadership with direct organizational consulting experience. His project portfolio indicates strong translation of theoretical research into real-world AI and HPC adoption, though specific grant details remain undisclosed.