Deep Machine Learning Professors and Supervisors in Greece

Baishakhi Rayمشاهده پروفایل

دانشیار

National and Kapodistrian University of AthensGreece

Baishakhi Ray is an Associate Professor of Computer Science at Columbia University's School of Engineering and Applied Science. Her work focuses on the intersection of AI, Software Engineering, and Security. She received her Ph.D. from the University of Texas, Austin and has established herself as a leading researcher in software engineering with AI applications. Dr. Ray's educational background includes a Ph.D. from the University of Texas, Austin. Her academic journey has led to a prominent position at Columbia University where she continues to advance research in software engineering. Dr. Ray's research spans several critical areas at the intersection of software engineering and artificial intelligence. Her work explores how machine learning techniques can improve software development processes, enhance security practices, and address challenges in program analysis. She has made significant contributions to understanding how large language models can be effectively applied to code generation, vulnerability detection, and software testing. Her research has practical implications for improving software reliability and security in real-world applications, particularly in safety-critical domains like autonomous systems. Analysis of Dr. Ray's recent publications reveals a clear trajectory toward leveraging AI for practical software engineering challenges. Her work has evolved from foundational program analysis techniques to cutting-edge applications of large language models in code understanding and generation. A notable trend is her focus on making AI-assisted software development more reliable, secure, and energy-efficient. Her research increasingly addresses the practical limitations of current AI approaches while developing novel methodologies to overcome them. IEEE TCSE Rising Star Award NSF CAREER Award IBM Faculty Award VMWare Faculty Award ICSME Most Influential Paper Award (2023) FSE'17 Distinguished Paper ASE'22 Distinguished Paper ISSTA'23 Distinguished Paper CACM Research Highlights Dr. Ray actively mentors graduate students and has built a productive research group focused on AI-driven software engineering solutions. Her research has been supported by prestigious grants including the NSF CAREER award. She has successfully guided numerous students through their research projects, with several of her advisees making significant contributions to publications in top-tier conferences. Dr. Ray also serves as an Amazon Visiting Academic, bridging academia and industry to address real-world software engineering challenges. Through her leadership in various research projects and collaborations, Dr. Ray has established a dynamic research environment that combines theoretical rigor with practical applications. Her work often involves interdisciplinary collaboration across computer science subfields, particularly connecting software engineering with security and AI research communities.

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Paraskevas Koukarasمشاهده پروفایل

پژوهشگر ارشد

International Hellenic UniversityGreece

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.

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Evangelos Kalampokisمشاهده پروفایل

استادیار

University of MacedoniaGreece

Evangelos Kalampokis serves as Assistant Professor of Information Systems and eGovernment at the Department of Business Administration, University of Macedonia, Greece. He maintains additional affiliations with the Hellenic Open University as an Adjunct Lecturer and with the Information Technologies Institute of the Centre for Research & Technology - Hellas (CERTH-ITI) in Thessaloniki. Dr. Kalampokis earned his Diploma (MEng) in Electrical and Computer Engineering from Aristotle University of Thessaloniki, followed by an MSc in Business Administration-MBA from the University of Macedonia, and completed his Ph.D. on "Linked Open Government Data Analytics" at the same institution. His academic foundation bridges engineering, business administration, and data science. His research spans Digital Government, Digital Health, Business Intelligence, Explainable Artificial Intelligence, Large Language Models, and Knowledge Graphs. With over fourteen years of experience, he has served as technical consultant and project initiator for numerous EU-funded initiatives including H2020 OpenGovIntelligence and FP7 OpenCube. His work consistently focuses on practical applications of advanced data technologies in public sector contexts, with particular emphasis on creating usable AI systems that government agencies can implement. Dr. Kalampokis' recent publications reveal a strategic shift toward healthcare applications of AI, particularly in oncology and cardiology, while maintaining his strong foundation in government data systems. His work demonstrates increasing sophistication in combining Large Language Models with structured government data to create practical decision support tools across multiple domains including healthcare, legal interpretation, and urban planning. He has published over 95 papers in high-impact journals including PLOS Digital Health (IF: 7.7), IEEE Intelligent Systems (IF: 6.1), and Journal of Web Semantics (IF: 3.1), establishing himself as a leading researcher at the intersection of AI and public sector applications. Dr. Kalampokis serves as proposal evaluator for the European Commission under Horizon Europe and has organized major conference tracks including the "AI, Data Analytics & Automated Decision Making" Track at EGOV-CeDEM-EPART 2023. His current research portfolio includes digiGOV-innoHUB (European Digital Innovation Hub for Digital Governance), TealHelix (Building Resilience Through Inclusive Food Labeling), and AI4Deliberation (Artificial Intelligence for Democratic Deliberations), reflecting his growing influence in shaping Europe's digital governance landscape.

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Apostolos Ampatzoglouمشاهده پروفایل

دانشیار

University of MacedoniaGreece

Apostolos Ampatzoglou serves as Associate Professor in the Department of Applied Informatics at the University of Macedonia, where he leads research in software engineering with particular emphasis on technical debt management and software quality. Previously, he held an Assistant Professor position at the University of Groningen (2013-2016) and maintains active collaborations across European research initiatives. His academic credentials include a PhD in Software Engineering from Aristotle University of Thessaloniki (2013), MSc in Computer Systems from the University of Macedonia (2005), and BSc in Informatics from the Technological Education Institute of Thessaloniki (2003). Dr. Ampatzoglou's research program centers on technical debt quantification, reverse engineering, and software maintainability. He pioneered financial models for technical debt management and developed metrics for assessing ripple effects in software systems. His work bridges theoretical foundations with industrial applications through projects like SDK4ED and SmartCLIDE, focusing on energy-efficient development and cloud service engineering. Current investigations explore machine learning applications for vulnerability prediction and technical debt prioritization. His extensive publication record reveals strong trends in applying AI to software quality assurance, with recent work emphasizing transformer models for vulnerability detection and explainable AI for technical debt identification. He actively investigates energy-aware development practices and service-oriented architecture patterns in cloud environments. Notable recognitions include: Top-7 reviewer for Journal of Systems and Software (2022) Best paper awards at IGSCC'21, ICSR'18, and EASE'17 18th most active Consolidated Researcher in Software Engineering (2013-2020) 3rd most active Early Stage Researcher (2010-2017) Multiple personal research grants from National Scholarships Association He has successfully supervised PhD candidates including Dr. Areti Ampatzoglou (Best PhD Thesis Award, University of Groningen) and Dr. Elvira-Maria Arvanitou (VERSEN Institute award). His research is funded through major European projects such as SDK4ED, SmartCLIDE, and SKILLAB, with recent grants exceeding €2M in total funding. Current work focuses on FAIR data implementation and skills gap analysis in European labor markets. As leader of the Software Engineering Lab at the University of Macedonia, he directs a 15+ member team working on technical debt management tools, cloud development environments, and XR applications for warehouse management. The lab maintains strong industry partnerships with technology clusters in Thessaloniki and participates in the Eclipse Foundation ecosystem.

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Panagiotis Papadimitriouمشاهده پروفایل

دانشیار

University of MacedoniaGreece

Panagiotis Papadimitriou serves as an Associate Professor at the Department of Applied Informatics, University of Macedonia in Thessaloniki, where he leads the NetCloud research group. Previously, he held positions as Assistant Professor at Leibniz University Hanover's Institute of Communications Technology and as a Post-Doctoral Researcher at Lancaster University (UK) and Telecom SudParis (France), with additional research experience at the University of Cambridge. His research spans Internet architectures and technologies with emphasis on Network Function Virtualization (NFV), Software-Defined Networking (SDN), Time-Sensitive Networking (TSN), Internet-of-Things (IoT), and Edge Computing. His work includes ML-assisted NFV orchestration (SFC-graph embedding, transformation, and multi-domain partitioning), network slicing orchestration, programmable dataplanes with P4, deterministic networks, datacenter network fabrics, and edge computing. Notable research outcomes include distributed network processing architectures (MIDAS, DistNSE), multi-provider service mapping mechanisms (Nestor), open-source NFV orchestration platforms (TeNOR), and orchestration platforms for cross-slice communication (MESON). His recent publications show a strong focus on In-Band Network Telemetry, Service Function Chain optimization, Time-Sensitive Networking implementations, and cross-slice communication systems, demonstrating his leadership in network virtualization and edge computing research areas. His work consistently addresses practical implementation challenges while advancing theoretical foundations in network management. Scientific Recognition: Senior Member of IEEE Associate Editor of IEEE Transactions on Network and Service Management Counselor of the IEEE Student Branch at University of Macedonia As Principal Investigator of the Horizon Europe NEPHELE project and previous EU-funded projects (NECOS, T-NOVA, CONFINE), he has secured over 1M euro in research funding. He actively contributes to the academic community through conference organization roles including IEEE ICC NGNI 2025, IEEE ICIN 2024, and IFIP/IEEE CNSM 2022 as General Co-Chair. The NetCloud research group he leads maintains a dedicated edge computing facility used for both research and teaching, and is known for its commitment to open-source software and community service through academic network resources. His educational background includes a Ph.D. in Electrical and Computer Engineering from Democritus University of Thrace (2008), an M.Sc. in Information Technology from University of Nottingham (2001), and a B.Sc. in Computer Science from University of Crete (2000).

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Nikolaos Bakasمشاهده پروفایل

استادیار

The American College of GreeceGreece

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.

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