Knowledge Graphs Professors and Supervisors in Greece

Yannis Christodoulouمشاهده پروفایل

پژوهشگر

University of the AegeanGreece

Dr. Yannis Christodoulou is a postdoctoral researcher at the University of the Aegean's Department of Cultural Technology and Communication, and a lecturer at the University of West Attica's Department of Conservation of Antiquities & Artwork. He holds a PhD in Knowledge Representation and Web Ontologies from the National Technical University of Athens (NTUA), an M.Sc. in Advanced Computing from the University of Bristol, and a B.Sc. in Informatics from the Athens University of Economics & Business. His research focuses on interdisciplinary Digital Humanities, combining Knowledge Representation, Ontology Engineering, and Semantic Visualization with Cultural Heritage, Human Geography, and the Arts. He has contributed to EU-funded projects (FP7, H2020) and a UK ESRC grant, developing frameworks for conservation decision-making, digital cultural heritage management, and visitor-centric cultural systems. Notable work includes the ACUX recommendation system and semantic middleware for smart cultural spaces. His research bridges diverse domains like filmmaking, dance, and heritage preservation, emphasizing visual knowledge representation and cross-disciplinary collaboration. He has also explored socio-cultural issues, such as migration narratives through a Greek lens, showcasing his commitment to socio-technical integration in humanities research. Grants/Projects : EU FP7, H2020, ESRC (UK), and the Acropolis of Ancient Tiryns Conservation OpenLab. Labs/Teams : Collaborator in the Acropolis of Ancient Tiryns OpenLab, focusing on digital media modeling and conservation.

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Christos Nomikosمشاهده پروفایل

دانشیار

University of IoanninaGreece

Christos Nomikos is an Associate Professor at the Department of Computer Science and Engineering, University of Ioannina. He holds a PhD from the National Technical University of Athens (NTUA) and has been a faculty member since 2003. His roles include teaching and research supervision in computer science disciplines. Education: Diploma in Electrical Engineering, NTUA, 1992 PhD in Computer Science, NTUA, 1997 Research Interests: Christos Nomikos focuses on theoretical computer science and logic, including algorithm design and analysis, computational complexity, formal languages, logic programming, and graph theory. His work explores foundational aspects of programming languages and their semantics, as well as applications in knowledge representation and automated reasoning. Teaching: In the 2024-25 academic year, he teaches Discrete Mathematics II , Principles of Programming Languages , Computational Complexity , and Approximation Algorithms . He emphasizes structured learning approaches and maintains a dedicated ecourse page for students. Advising & Grants: He supervises undergraduate thesis projects in computer science, with a focus on theoretical topics like Turing machine transformations and Boolean grammar algorithms. No explicit grant information is provided in the texts. His advising process involves rigorous academic evaluation and prefers candidates with strong performance in core computer science courses. Labs/Teams: While no specific lab or team affiliations are mentioned, his research collaborations include work with international conferences and journals, indicating active participation in academic networks.

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Livieris Ioannisمشاهده پروفایل

استادیار

University of PiraeusGreece

Livieris Ioannis is an Assistant Professor in the Department of Statistics and Insurance Science at the University of Piraeus. He holds academic positions including Adjunct Professorships at the University of the Peloponnese and Technological Educational Institute of Western Greece. His research focuses on optimization methods for neural networks, machine learning, ensemble techniques, and their applications in healthcare, finance, education, and environmental science. Education: Ph.D. in Mathematics (2012), University of Patras M.Sc. in Computational Mathematics & Informatics in Education (2008), University of Patras B.Sc. in Mathematics (2006), University of Patras Research Interests: Dr. Livieris specializes in developing optimization algorithms for neural networks, semi-supervised learning, and ensemble methods. His work emphasizes practical applications such as time series forecasting (financial, environmental), medical image analysis (cancer detection, X-ray classification), and educational data mining (student performance prediction). He also explores explainable AI frameworks to enhance transparency in deep learning models. Key Contributions: He has contributed to over 50 peer-reviewed articles, including work on weight-constrained neural networks, gradient-based optimization, and CNN-LSTM models for cryptocurrency forecasting. His research has been recognized with inclusion in Stanford’s top 2% scientists (2020–2023) and a best paper award at HERCMA ’09. Awards & Roles: Associate Editor, Evolving Systems (Springer) Reviewer for 50+ journals including Neurocomputing and IEEE Transactions on Neural Networks Grants & Projects: Principal investigator in EU-funded projects like NEUROCLIMA (climate resilience via AI), ORBIS (democratic participation via AI), and PVAdapt (sustainable energy systems). He also leads initiatives in explainable AI for medical imaging and causal effect estimation in social science. Labs & Teams: Active in interdisciplinary teams at the University of Piraeus, focusing on AI-driven solutions in education, healthcare, and environmental monitoring. Collaborates with institutions like the IEEE and the Hellenic Association of ICT in Education.

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George Tsekourasمشاهده پروفایل

استاد

University of the AegeanGreece

Prof. George Tsekouras is a Professor in the Department of Cultural Technology and Communication at the University of the Aegean since 2002. His research focuses on artificial intelligence, cultural informatics, and expert systems, with over 100 publications in journals and conferences. He has contributed to projects involving machine learning fairness, blockchain sustainability, healthcare monitoring systems, and cultural heritage preservation. Research interests include AI applications in cultural heritage (e.g., 3D asset quality, art recoloring for color blindness), ethical AI in criminal justice systems, energy-efficient blockchain technologies, and environmental monitoring via machine learning. His work bridges computational methods with societal challenges, emphasizing interdisciplinary approaches. Notable trends in his publications include leveraging graph neural networks for healthcare (e.g., Parkinson’s disease monitoring), exploring ChatGPT for hospitality personalization, and developing hybrid machine learning models for disaster prediction. His contributions span technical innovation and social impact, such as improving accessibility through image recoloring algorithms and reducing blockchain’s environmental footprint. No scientific awards are listed. Advising and grants details are unavailable, but his extensive publication record indicates active research collaboration. His work often integrates cultural data with computational tools, reflecting his department’s mission at the University of the Aegean.

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Dimitrios Michailمشاهده پروفایل

دانشیار

Harokopio UniversityGreece

Dimitrios Michail is an Associate Professor in the Department of Computer Science and Telecommunications at Harokopio University. He holds a Diploma in Electronic Engineering and Computer Engineering from the Polytechnic University of Crete, a Master's in Computer Science, and a Ph.D. in Algorithms from the Max-Planck Institute for Computer Science. His research focuses on algorithms for modern computational models, graph algorithms, machine learning, and their applications in computer vision, wildfire prediction, and social network analytics. He has been involved in European projects like TELEIOS and DeepCube. His work intersects earth observation, autonomous systems, and AI-driven solutions for environmental challenges. Recent research includes Earth-as-a-Graph frameworks for wildfire forecasting and novel datasets for InSAR analysis. He has contributed to libraries like JGraphT and JHeaps, emphasizing algorithmic efficiency and open-source tools. Michail's publications span graph theory, deep learning, and remote sensing applications, with a focus on real-world problems like flood mapping and fake news detection. He has collaborated on projects addressing urban mobility, public transport optimization, and disaster preparedness through spatio-temporal modeling.

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Giorgos Stamouمشاهده پروفایل

استاد

National Technical University of AthensGreece

Giorgos Stamou is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), and a Visiting Professor at the MIT Sloan School of Management and MIT Open Learning. He directs the Artificial Intelligence and Machine Learning Systems Laboratory (AILS Lab) and has been a senior researcher at the Institute of Communications and Computer Systems (2000-2008) and an academic visitor at Oxford University (2011-2012). His research spans Deep Learning , Explainable AI , and Knowledge Representation , with a focus on Large Language Models and Multimodal Learning . He has coordinated over 60 funded projects and published 150+ papers. Recent work highlights trends in LLM Evaluation , Gender Bias Mitigation , and Multimodal Music Analysis , reflecting his interdisciplinary approach to AI challenges. Stamou has served on steering committees for W3C working groups (Rule Interchange Format, Web Ontology Language) and contributed to cultural heritage metadata enrichment through the CrowdHeritage projects. He founded NTUA's MSc program in Data Science and Machine Learning (2018-2022) and has organized conference tracks on riddle-solving frameworks and hallucination detection.

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Chao Pengمشاهده پروفایل

مدرس پاره‌وقت

National and Kapodistrian University of AthensGreece

Chao Peng is a Principal Research Scientist at ByteDance where he leads the Trae Research team (ByteDance Software Engineering Lab), conducting cutting-edge research on AI agents for software engineering. He also serves as a Part-time Postgraduate Student Mentor at Fudan University's School of Computer Science, bridging industry research with academic mentorship. PhD in Informatics (2021), University of Edinburgh, UK MSc in High Performance Computing and Data Science (2017), University of Edinburgh, UK BEng in Computer Science and Technology (2016), Xuzhou University of Technology, China Dr. Peng's research focuses on the intersection of software testing, program analysis, and large language models. His work explores how AI agents can revolutionize software engineering practices, with particular emphasis on automated bug detection, code generation, and testing frameworks. He has pioneered approaches for evaluating LLM performance in software engineering contexts and developing agent-based systems that enhance developer productivity while maintaining code quality and security. His recent publications demonstrate a clear trend toward integrating large language models with traditional software engineering practices. The research spans code generation evaluation, security vulnerability detection, automated bug reproduction, and issue localization. These works collectively advance the field of AI-assisted software development by addressing practical challenges in reliability, security, and efficiency of AI-generated code. Distinguished Reviewer for FSE'25 School of Informatics Scholarship (fully-funded PhD scholarship) Outstanding Graduate Scholarship at Xuzhou University of Technology Multiple China National Scholarships Honours Spot Bonus at ByteDance Certificate of Achievement for HPCAC Student Cluster Competition Dr. Peng actively mentors students through his role at Fudan University and previously at the University of Edinburgh, where he served as sub-supervisor for MSc projects and teaching assistant for software testing courses. His research has attracted significant industry attention, leading to multiple collaborations between ByteDance and academic institutions. He frequently serves on program committees for major software engineering conferences including ASE, FSE, and ICSE, demonstrating his leadership in the field. As leader of the Trae Research team at ByteDance Software Engineering Lab, Dr. Peng oversees research on AI agents for software engineering, including the application and evaluation of AI agents and training LLMs for agent-based systems. The lab's work focuses on practical systems that predict, detect, diagnose, and fix bugs across various software systems, with particular emphasis on real-world applications and measurable impact on developer productivity.

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Işıl Dilligمشاهده پروفایل

دانشیار

National and Kapodistrian University of AthensGreece

Işıl Dillig is an Associate Professor of Computer Science at the University of Texas at Austin, where she leads the UToPiA research group. Her academic career spans over a decade of significant contributions to programming languages research, particularly in program analysis, verification, and synthesis. Dr. Dillig received all her academic degrees (BS, MS, and PhD) from Stanford University before joining the faculty at UT Austin. Her educational background established the foundation for her innovative research approach that bridges theoretical computer science with practical applications. Her research focuses on developing techniques to make software systems more reliable, secure, and easier to build through advanced program analysis, verification, and synthesis methods. She has pioneered approaches that combine symbolic reasoning with machine learning to tackle complex software engineering challenges across multiple domains including security, databases, and programming language theory. Her work demonstrates exceptional depth in creating practical tools that address real-world software development problems while maintaining strong theoretical foundations. Analysis of Dr. Dillig's publication record reveals a consistent trajectory of innovation in program synthesis, with recent work expanding into neurosymbolic approaches that bridge neural networks with formal methods. Her research shows strong connections between theoretical foundations and practical applications, particularly in security-critical systems, database technologies, and blockchain applications. The evolution of her work demonstrates increasing sophistication in handling complex program structures while maintaining practical usability. Dr. Dillig has received prestigious recognition for her research contributions: Sloan Fellowship NSF CAREER award As a dedicated educator and research leader, Dr. Dillig has served in significant roles including Program Chair for PLDI 2022 and Steering Committee member for PLDI. She has mentored numerous students through her UToPiA research group, guiding research in program synthesis, verification, and analysis. Her work has been supported by substantial research grants that have enabled innovative projects at the intersection of programming languages and security. Dr. Dillig leads the UToPiA (UT Austin Programming, Languages, and Analysis) research group, which focuses on developing novel techniques for program analysis, verification, and synthesis. The group maintains strong collaborations with industry partners and academic institutions worldwide, translating theoretical advances into practical tools that address real software engineering challenges.

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Laura Dietzمشاهده پروفایل

دانشیار

National and Kapodistrian University of AthensGreece

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire, where she leads the TREMA lab. Her academic journey began with a PhD from the Max Planck Institute for Informatics in Saarbruecken, Germany (2011), followed by postdoctoral positions at the University of Massachusetts Amherst (2010-2015) and University of Mannheim (2015-2016). Her educational background includes PhD studies at both the Max Planck Institute for Informatics (2007-2011) under Prof. Gerhard Weikum and Prof. Tobias Scheffer, and earlier research at Humboldt University in Berlin. She has built a distinguished career bridging theoretical computer science with practical applications in information retrieval and machine learning. Dietz's research primarily focuses on the intersection of information retrieval, natural language processing, and knowledge graphs, with a parallel research initiative in watershed data science. She is particularly known for her work on entity-aspect linking, complex answer retrieval, and the vision of automatic Wikipedia construction. Her approach integrates fine-grained knowledge annotations with text understanding to create comprehensive information systems that go beyond traditional 10-blue-links search paradigms. In watershed data science, she applies similar machine learning techniques to environmental data streams, focusing on solute transport analysis during storm events. Her recent publications reveal a strong trend toward fine-grained semantic understanding, particularly in entity-oriented search tasks. She has pioneered methods for entity-aspect linking that significantly improve retrieval accuracy by capturing different contexts in which entities appear. Her work increasingly integrates knowledge graphs with neural architectures, showing sophisticated understanding of how to leverage both structured and unstructured information for better search experiences. Best paper award at JCDL 2018 for work on entity-aspect linking NSF CAREER Award (2019-2023) for "Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval" OSSI Award 2013 from UMass ICB3 for open-source hardware/software Dietz actively mentors PhD and Masters students through the TREMA lab, with current research focusing on entity ranking, topic extraction, conversational search, and watershed forecasting. Her grant portfolio includes the NSF CAREER award and funding from the Northeast Big Data Innovation Hub for forecasting salinity in rivers during storm events. She has also coordinated the TREC Complex Answer Retrieval track (2017-2019), creating important benchmarks for the IR community. The TREMA lab (Text Retrieval, Entity Modeling, and Applications) serves as the hub for Dietz's research activities, bringing together students and collaborators to work on cutting-edge problems in information access. The lab's work spans both theoretical contributions to information retrieval and practical applications in domains ranging from environmental science to scientific publication analysis.

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Mayur Naikمشاهده پروفایل

استاد

National and Kapodistrian University of AthensGreece

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He holds office in Room 642B, Amy Gutmann Hall and maintains an active research program focused on the intersection of programming languages and artificial intelligence. Before joining UPenn, he was faculty at Georgia Institute of Technology and a researcher at Intel Labs, Berkeley. Naik received his PhD in Computer Science from Stanford University in 2008 under Alex Aiken, a Masters from Purdue University in 2003 under Jens Palsberg, and a Bachelors from BITS Pilani in 1999. He grew up in Goa, India. His primary research interests center around neurosymbolic programming, which combines symbolic reasoning with machine learning to create more accurate, interpretable, and domain-aware AI systems. His group develops language design, learning algorithms, and compiler optimizations in this space, with their most mature effort being the Scallop neurosymbolic programming language and compiler toolchain. He also conducts research in trustworthy AI for healthcare applications and AI-enabled programming tools that improve programmer productivity. Analysis of his recent publications shows a strong trend toward neurosymbolic programming frameworks (Scallop, TorchQL), LLM-assisted program analysis (IRIS), and applications of these techniques to security, healthcare, and computer vision. His work consistently bridges theoretical foundations with practical implementations, often releasing open-source systems. Misra Family Professor (endowed chair, effective July 2024) Multiple distinguished paper awards (PLDI 2019, FSE 2015, PLDI 2014) Test-of-Time Paper Awards (FSE 2013, FSE 2012, EuroSys 2011) His student Elizabeth Dinella won the 2025 ACM SIGSOFT Outstanding Dissertation award Naik has advised numerous PhD students who have gone on to faculty positions at top institutions including Peking University, University of Toronto, Ashoka University, Bryn Mawr College, and Johns Hopkins University. His research is supported by grants from NSF, Google, Amazon, and other industry partners. His lab maintains active collaborations with clinicians and bioinformatics researchers to apply neurosymbolic programming to healthcare problems. His research group, which includes current PhD students and postdocs, develops practical open-source systems and applies them to diverse domains including computer vision, cybersecurity, medicine, and bioinformatics. The group maintains strong industry connections with Google, Microsoft, Amazon, and other tech companies.

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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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