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Pavlos FafaliosView profile

Assistant Professor

• Information Systems
• Data and Knowledge Management
• Linked Data
• +7 more

Pavlos Fafalios is an Assistant Professor of Information Systems at the School of Production Engineering and Management, Technical University of Crete, and an Affiliated Researcher at the Centre for Cultural Informatics (CCI) and Information Systems Laboratory (ISL), Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH). He holds a PhD and MSc in Information Systems from the University of Crete and an Engineer's Diploma from the University of the Aegean. His academic journey includes postdoctoral research at L3S Research Center, Leibniz University of Hanover, and FORTH-ICS, as well as teaching roles at Hellenic Mediterranean University and the University of Crete. His research interests span Information Systems , Data and Knowledge Management , and Linked Data , with a strong interdisciplinary focus on applications in Cultural Heritage and the Humanities . He develops semantic technologies, knowledge graphs, and information management systems for complex, real-world domains. The recent publications highlight a strong trend in applying semantic technologies to cultural and historical data, including earthquake data modeling, feminism history documentation, and museum data integration. His work consistently focuses on knowledge representation, ontology development, and data interoperability, particularly using CIDOC CRM and Linked Data principles. Individual postdoctoral research fellowship 'Marie Skłodowska-Curie' (H2020-MSCA-IF-2019) Finalist for the '2019 ERCIM Cor Baayen Young Researcher Award' Fellowship from legacy 'Maria Michael Manasaki' (2014-2015) 1st prize in Hack4Med 2014 1st prize in Blue Hackathon 2013 PhD and MSc scholarships by FORTH Pavlos Fafalios has been involved in significant research projects such as PortADa (MSCA Staff Exchange), RICONTRANS (ERC Consolidator Grant), FE.P.I.B., ReKnow (Marie Curie project), SeaLiT (ERC Starting Grant), and ALEXANDRIA (ERC Advanced Grant), where he served as principal investigator, scientific responsible, or lead researcher. He has supervised student projects that led to award-winning software tools. He is active in scientific committees and regularly publishes in international venues. He leads and contributes to research in the Centre for Cultural Informatics (CCI) and Information Systems Laboratory (ISL) at FORTH-ICS, collaborating on interdisciplinary projects that bridge computer science with humanities and cultural heritage. His lab work emphasizes practical tools for semantic data integration, exploration, and analysis.

Angelos CharalambidisView profile

Assistant Professor

• Programming Languages and Software Systems
• Declarative Programming Languages
• Computational Logic
• +1 more

Angelos Charalambidis is an Assistant Professor at Harokopio University, affiliated with the Department of Informatics and Telematics. His academic and professional engagements focus on theoretical and applied aspects of computational logic and programming languages. Education: B.S. in Informatics, University of Athens (2005) M.S. in Computer Systems, University of Athens (2008) Ph.D. in Higher-Order Logic Programming, University of Athens (2014) His research interests include declarative programming languages, computational logic, and reasoning techniques, contributing to the broader field of Programming Languages and Software Systems. He has also explored applications in knowledge representation, preference modeling, and cloud-native systems. The trends in Angelos Charalambidis's publications highlight advancements in logic programming, including higher-order logic, fixpoint theory, ordered disjunction, and federated query processing. His work bridges theoretical foundations with practical implementations in formal methods and computational logic.

Anastasia AilamakiView profile

Professor

• Data Systems
• Database Systems
• Resource Optimization
• +4 more

Anastasia Ailamaki is a Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences and the Department of Computer Science. She is also a member of the Board of Directors of the Technical University of Crete (TUC), where she is a distinguished alumna, having earned her Master of Science in Electrical and Computer Engineering. Her leadership in data systems research has been recognized with the prestigious ERC Advanced Grant. Her research focuses on foundational approaches to optimizing system resources through declarative data systems. This work lies at the intersection of database systems, computer architecture, and performance modeling, aiming to improve efficiency and scalability in modern data-intensive applications. Her interests include query processing, data storage optimization, and the integration of hardware-aware techniques into database design. The awarded ERC project, "A Foundational Approach to Resource Optimization through Declarative Data Systems," represents a significant advancement in the field, funded with 2.5 million euros, highlighting the relevance and impact of her research agenda. ERC Advanced Grant Professor Ailamaki has advised numerous students throughout her career and has led major research initiatives, though specific names and grants beyond the ERC are not detailed in the current text. Her role on the External Advisory Committee and Board of Directors at TUC reflects her ongoing commitment to academic leadership and institutional development. She is actively involved in shaping the future of data systems research at EPFL and through her collaborations with institutions like the Technical University of Crete. Her lab or research group likely focuses on systems for data science and performance-aware computing, although specific team details are not provided.

Theodoridis IoannisView profile

Professor

• Data Science
• Databases
• Big Data Management
• +3 more

Professor Theodoridis Ioannis is a distinguished faculty member in the Department of Informatics at the University of Piraeus, where he serves as Director of the Data Science Laboratory within the School of Information and Communication Technologies. With a career spanning over two decades, he has established himself as a leading expert in data management and analysis. His research interests focus on Data Science, particularly in databases, big data management, data mining, and geoinformatics. Professor Theodoridis has made significant contributions to spatial database systems, time series analysis, and distributed data processing. His work bridges theoretical foundations with practical applications in areas such as smart cities, mobility analytics, and scientific data management. His publication record demonstrates consistent research productivity with over 100 peer-reviewed articles in top-tier venues, accumulating more than 10,000 citations. His research output shows a clear evolution from foundational database techniques toward contemporary challenges in big data analytics, machine learning integration, and privacy-preserving methods. Member of editorial board of ACM Computing Surveys (since 2016) Reviewer for numerous international journals and conferences Active participant in data management conference committees Professor Theodoridis has secured significant research funding through Horizon 2020 projects, serving as project coordinator and research team leader since 2001. His work demonstrates strong industry and academic collaboration, with applications spanning multiple domains. He has also co-authored three influential monographs in his field. He leads the Data Science Laboratory, which serves as a hub for interdisciplinary research at the intersection of database systems, machine learning, and domain-specific applications. The laboratory fosters collaboration between computer scientists, domain experts, and industry partners to address real-world data challenges.

James CheneyView profile

Professor

• Databases and data provenance
• Programming languages and compilers
• Generic programming
• +7 more

James Cheney is a Personal Chair of Programming Languages and Systems at the University of Edinburgh, working in the Laboratory for Foundations of Computer Science within the School of Informatics. He leads the Principles of Provenance research group and has been a Turing Fellow from 2018 to 2023. His educational background includes a PhD in Computer Science from Cornell University (2004), an MS in Mathematics from Carnegie Mellon University (1998), and a BS in Computer Science and Mathematics from Carnegie Mellon University (1998). Cheney's research focuses on the intersection of databases and programming languages, with particular emphasis on data provenance. His work spans several key areas: Databases and data provenance Programming languages and compilers Generic programming Logic and automated theorem proving Compression and information theory XML and related technologies His recent publications demonstrate a strong focus on language-integrated query systems, type systems for programming languages, and formal approaches to data provenance. These works often bridge theoretical foundations with practical applications in database systems and programming language design. Cheney has received several notable awards and recognitions: Royal Society University Research Fellowship (2008-2016) Turing Fellow (2018-2023) ERC Consolidator Grant for the Skye project (2016-2021) Google Research Award for Language-integrated provenance As an advisor, Cheney has supervised numerous PhD students and postdoctoral researchers who have gone on to positions at institutions including New York University, LSE, University of Southampton, Meta, and others. His research has been supported by various grants from DARPA, EPSRC, AFOSR, EU FP7, and industry partners including Google, Microsoft Research, and Huawei. Cheney leads the Principles of Provenance group, which conducts fundamental research on data provenance and its applications in security, data curation, and scientific computing. The group has worked on projects including Skye (a programming language for scientific data curation), ADAPT (a DARPA-funded project on advanced persistent threat prevention), and language-integrated provenance systems.

Zhenman FangView profile

Associate Professor

• Hardware Acceleration
• Computer Architecture
• Big Data Computing
• +7 more

Dr. Zhenman Fang is an Associate Professor in the School of Engineering Science (Computer Engineering Option) and Associate Member in the School of Computing Science at Simon Fraser University, Canada. He founded and directs the HiAccel Lab, focusing on accelerator-rich architectures. His PhD (2014) is from Fudan University, China, with 15 months spent at the University of Minnesota. Prior to SFU, he was a Staff Software Engineer at Xilinx (2017-2019) and a postdoc at UCLA (2014-2017). His research spans: Hardware acceleration for ML, big data, genomics, and HPC FPGA-based customizable computing and near-data processing Compiler/runtime systems for heterogeneous platforms Performance/reliability optimization of accelerator-rich systems His recent publications (2024-2025) focus on FPGA acceleration for machine learning (e.g., on-device training, quantization), computational chemistry, image/video compression, database systems, and reconfigurable computing, demonstrating cross-domain applications of specialized hardware. Awards & Honors: Best Paper Awards: FPL 2024, MEMSYS 2017, TCAD 2019 Best Paper Nominations: ICCAD 2025, FCCM 2025, HPCA 2017, ISPASS 2018 SFU Research Excellence Horizon Award (2025) NSERC Alliance, CFI JELF, and Xilinx University Awards He advises 20+ PhD/Master's students in HiAccel Lab, focusing on accelerator design. Major grants include NSERC Alliance (2020) and CFI JELF (2019). The lab operates a 10-node cluster with FPGA/GPU infrastructure.

• Data Mining
• Machine Learning
• Graph Mining
• +9 more

Michalis Vazirgiannis is a Professor in the Department of Informatics at the Athens University of Economics and Business (AUEB), specializing in data mining and machine learning with applications in web and social network analysis. His work bridges theoretical algorithms and real-world scalability challenges. Education: Bachelor Degree in Informatics, National and Kapodistrian University of Athens, 1986 Master (M.Sc.) in Robotics, National and Kapodistrian University of Athens, 1988 Master (M.Sc.) in Knowledge Based Systems, Heriot Watt University, Edinburgh, 1989 Ph.D. in Informatics, National and Kapodistrian University of Athens, 1994 Research Focus: Professor Vazirgiannis pioneers clustering algorithms with subjective/objective validation, distributed feature selection for evolving graphs, and temporal link analysis for dynamic page ranking. His research addresses critical gaps in semi-supervised learning for large-scale web and social networks, emphasizing dimensionality reduction and ranking predictability in temporal contexts. Publication Trends: His 2007-dominated publications reveal a strategic shift toward graph-based web mining, with recurring themes of distributed processing (P2P similarity search), clustering validity frameworks, and semantic web personalization. The work consistently targets scalability bottlenecks in real-world network data. Scientific Recognition: ERCIM Post-doctoral Scholarship (2001) Marie Curie European Scholarship (2006) Leadership & Collaboration: As ERASMUS coordinator for AUEB's Informatics Department and editorial board member of Intelligent Data Analysis journal, he bridges academia and industry. His EU project leadership (FP6 SQO-OSS, Marie Curie NGWeMiS) and program committee roles (IEEE/ICDM 2008, ECML/PKDD 2008) highlight his influence in data mining standardization. International collaborations span INRIA, Fraunhofer, Max Planck, and IBM Research. Technical Innovation: His patent contributions and invited lectures at ECML/PKDD 2006/SIAM/SDM 2006 demonstrate applied impact, particularly in web personalization engines (SEWeP) and evolving graph analytics.

• Machine Learning
• Theoretical Computer Science
• Algorithms
• +3 more

Christos Tzamos is an Associate Professor at the National and Kapodistrian University of Athens. His research focuses on the intersection of machine learning, theoretical computer science, and algorithmic game theory. He has made significant contributions to topics like online learning, optimization under noise, and mechanism design. His work often bridges foundational theory with practical algorithmic solutions. Position: Associate Professor Affiliation: National and Kapodistrian University of Athens Key research interests include: Machine Learning (e.g., robust classification, active learning) Algorithms (e.g., optimization, approximation algorithms) Computational Economics (e.g., auction design, Pandora’s box problems) His most recent articles (2023–2025) explore advanced topics such as statistical query hardness, noise-robust learning frameworks, and optimized mechanisms for multi-item markets. Notable trends in his work include developing algorithms resilient to adversarial noise and designing efficient learning strategies under constraints. No scientific awards are explicitly listed in the provided texts, though his prolific publication record reflects strong academic standing. Labs, teams, or collaborations are not detailed here, but his research often involves interdisciplinary collaborations with leading institutions.

Georgia KoloniariView profile

Associate Professor

• Social Networks
• Graph Databases
• Distributed Systems
• +4 more

Georgia Koloniari is an Associate Professor at the Department of Applied Informatics, School of Information Sciences, University of Macedonia. She holds a PhD in Computer Science from the University of Ioannina (2009), along with an MSc (2003) and BSc (2001) in Computer Science from the same institution. Her research focuses on distributed systems, graph databases, and peer-to-peer (P2P) systems, with notable contributions to social network analysis, XML data management, and privacy-preserving techniques. She has led or participated in multiple funded research projects, including the Cloud9 project (2011–2014) and the Self-Peer initiative (2005–2008). Her teaching roles include instructing courses such as Distributed Systems , Data Structures , and Advanced Information Systems at the undergraduate level, and IT Infrastructure and Databases at the graduate level. She has also contributed to online educational initiatives, including the development of the Data Structures course in the Open Digital Courses project. Dr. Koloniari’s research interests span social network evolution, graph database management, and distributed data processing. She has published extensively on topics like clustered overlay networks, privacy-preserving record linkage, and temporal query processing. Her work integrates game-theoretic approaches and algorithmic solutions to address challenges in P2P systems and cloud computing. Her recent publications highlight advancements in privacy-preserving techniques (e.g., PRIVATEER toolkit) and time-aware social search systems. She remains active in academic service, serving on program committees for conferences like WWW and EDBT, and reviewing for journals such as IEEE Transactions on Parallel and Distributed Systems. Labs/Teams: Her involvement with the DMOD Lab (Digital Media, Data Management, and Distributed Systems Lab) reflects her focus on interdisciplinary research in data management and distributed computing.

Anastasios GounarisView profile

Professor

• Distributed Databases
• Autonomous data processing
• Big Data Management
• +7 more

Anastasios Gounaris is a Professor at the Department of Informatics, Aristotle University of Thessaloniki, where he has been a faculty member since April 2008. He previously served as a Visiting Lecturer at the University of Cyprus (2007-2008) and held research positions at the University of Manchester and CERTH. His academic journey includes a PhD from the University of Manchester (2005), an MPhil from UMIST (2002), and a degree in Electrical and Computer Engineering from Aristotle University of Thessaloniki (1999). His research spans Distributed Databases, Autonomous Data Processing, Big Data Management, Workflow Optimization, and Data Mining . He has made significant contributions to large-scale data management systems, massive parallelism techniques, and business process analytics. His work bridges theoretical computer science with practical industrial applications, particularly in predictive maintenance and edge computing environments. His recent publications (2024-2025) demonstrate a strong focus on process mining, anomaly detection, predictive maintenance systems, and edge analytics . These works address critical challenges in handling evolving data streams, optimizing task allocation in resource-constrained environments, and developing parameter-free algorithms for real-world applications. His research shows consistent progression from foundational database systems work to cutting-edge applications in industrial IoT and healthcare analytics. Dr. Gounaris has successfully supervised numerous PhD students to completion, including Athanasios Naskos (2017), Georgia Kougka (2017), Christos Bellas (2022), Theodoros Toliopoulos (2022), Anna-Valentini Michailidou (2023), Ioannis Mavroudopoulos (2024), and Konstantinos Varvoutas (2025). His research has been supported by multiple national and European projects including DataflowOpt, PRECognition, NavGreen, CUREX, Rainbow, Lifechamps, and Trineflex. He is an active member of Datalab (formerly Delab) and has contributed to industry applications through collaborations with companies such as Comidor, Gnomon, Istognosis, Atlantis, AFS, Follow-Apps, Sboing, and Upcom. His work demonstrates a strong commitment to transferring research results to real-world applications while maintaining academic rigor.

Antonia SaravanouView profile

Researcher

• Data Mining
• Machine Learning
• Anomaly Detection
• +3 more

Antonia Saravanou is a Ph.D. graduate from the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens (NKUA), advised by Prof. D. Gunopulos. She holds an M.Sc. in Advanced Information Systems and a B.Sc. in Computer Science from the same department. Since 2011, she has worked as a Research Scientist and Engineer at NKUA and Athens University of Economics and Business (AUEB), and is affiliated with the Knowledge Discovery in Databases Laboratory (KDDLab) and the Management of Data, Information & Knowledge Group (Madgik). Her research spans Data Mining, Machine Learning, and Anomaly Detection, with a focus on Social Network Analysis, Graph Representations, and Healthcare Applications. She has completed research visits at Stanford University's Geometric Computing Group, Spotify Tech Research, and Bloomberg AI. Education: Ph.D., Informatics and Telecommunications, NKUA M.Sc., Advanced Information Systems, NKUA B.Sc., Computer Science, NKUA Her research explores graph-based methods for event detection in social networks, self-supervised node representation learning, and applications in healthcare and news analysis. Projects include music recommendation systems via graph representations, infant mortality prediction models using birth certificate data, and real-time news monitoring frameworks. She has also worked on knowledge graph applications for news ranking and anomaly detection in sparse time series data. Scientific Awards: Outstanding Reviewer for ICLR 2021 Top Reviewer for NeurIPS 2018 As a teaching assistant, she has supported graduate and undergraduate courses at NKUA, including Mining Big Datasets, Data Mining, and Artificial Intelligence. She actively participates in outreach programs like ACM Student Chapter UoA, Rails Girls Athens, and Django Girls.

Nikolaos E. PanagiotouView profile

Researcher

• Text Mining
• IoT/Smart-Cities
• Spatio-temporal Analysis
• +3 more

Nikolaos E. Panagiotou is a Research Engineer at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, where he has been working since March 2014. He is also a Ph.D. candidate in Real-time Data Analysis in Massive Streams and Social Media at the same institution under the supervision of Prof. D. Gunopulos. He is an active member of the KDDLAB research group at the University of Athens, participating in EU research projects including INSIGHT and VaVeL. His educational background includes: MSc. with Distinction in Artificial Intelligence, Learning from Data from the School of Informatics, University of Edinburgh (2012-2013) BSc. in Informatics and Telecommunications from the Department of Informatics and Telecommunications, University of Athens (2007-2011) Dr. Panagiotou's research focuses on real-time analysis of high-volume data streams with particular emphasis on text and sensor streams, including news and social media content. His current research interests include: Text Mining: event detection, first story detection, open information extraction IoT/Smart-Cities: sensor analysis, heterogeneous sensor aggregation, real-time visualization Spatio-temporal Analysis: congested road-segments, travel time estimation, OSM His work bridges theoretical research with practical applications in smart city technologies and real-time data processing systems. His publication record demonstrates a strong focus on smart city applications, real-time data processing, and information extraction from diverse data sources. The research shows progression from foundational work on stream mining and event detection to sophisticated applications in urban mobility and news monitoring systems. His most recent work continues to advance techniques for handling sparse data environments and real-time analytics, with particular emphasis on travel time estimation and hybrid analytical approaches. Dr. Panagiotou has been involved in significant EU-funded projects: INSIGHT project: Implementation of real-time analysis modules and visualization engine for the smart city of Dublin VaVeL project: Design of integration schemes and development of real-time analysis modules using spatio-temporal data He has developed multiple demonstration systems including the News Monitor for real-time news analysis and various smart city monitoring dashboards. His technical expertise spans real-time data processing, stream mining, spatio-temporal analysis, and visualization systems, with applications in urban mobility and news monitoring. His collaborative work with researchers across Europe demonstrates his integration into the international research community in data science and smart city applications.

• Semantic Web
• Digital Libraries
• Software Engineering
• +7 more

Gioldasis Nektarios is a Lecturer in the School of Electronic and Computer Engineering at the Technical University of Crete , affiliated with the Distributed Information Systems and Applications Laboratory (TUC/MUSIC) . He has been a permanent member of the laboratory since 2006 and has contributed to numerous Research & Development projects. MSc in Informatics, School of Electronic and Computer Engineering, Technical University of Crete (2002) BSc in Applied Informatics, University of Macedonia (1999) His research interests span critical domains including: Semantic Web technologies and Open Linked Data Software engineering for Service Oriented and Multi-Tier Architectures Data modeling, metadata management, and semantic interoperability Digital libraries and multimedia management systems The article portfolio demonstrates expertise in XML-Semantic Web integration, query mediation, and cultural data systems, with a 2023 publication on location-based game platforms. Key trends include: Interoperability frameworks (2015-2012) Ontology-driven data access (2010-2009) Geospatial and cultural heritage applications (2011-2007)

Papadopoulos ApostolosView profile

Associate Professor

• Databases
• Data Mining
• Spatial Databases
• +7 more

Apostolos Papadopoulos is an Associate Professor at the Department of Informatics , Aristotle University of Thessaloniki , Greece. His research focuses on databases, data mining, and big data analytics. Born in Eleftheroupolis, Greece Diploma in Computer Engineering and Informatics (University of Patras) PhD in Informatics (Aristotle University of Thessaloniki) Research Interests: Spanning DATABASES and DATA MINING , his work includes: Efficient query processing in multidimensional and spatio-temporal databases Graph mining algorithms (dense subgraphs, frequent patterns, probabilistic graphs) Parallel/distributed systems (Spark, Hadoop) for massive datasets Real-time data stream analysis Scientific Recognition: Best Paper at EDBT 2014 (selected for ACM Transactions on Database Systems) Best Knowledge Discovery Paper at ECML/PKDD 2008 Google Scholar h-index: 25 PhD Supervision: Mentoring 4 ongoing PhD students and one graduated candidate (Eleftherios Tiakas).

• Computer Vision
• Neural Networks
• Pattern Recognition
• +3 more

Nikolaos Vassilas serves as Professor in the Department of Informatics and Computer Engineering at the University of West Attica, Greece, with leadership roles including Director of the Intelligent Knowledge, Image & Information Systems research Laboratory (ISLab) since 2019 and former Director of the Joint Greek-French Master Degree Program (2016-2020). His academic journey includes research positions at NCSR "Demokritos" and EPFL. His educational credentials feature a Diploma in Electrical Engineering from Aristotle University of Thessaloniki (1983), and M.S.E.E. and Ph.D. degrees from the University of Minnesota (1987, 1990). Professional affiliations include IEEE Computational Intelligence Society, Hellenic Society of Artificial Intelligence, and Greek Computer Society. Professor Vassilas' research centers on Computer Vision and Neural Networks with applications in urban analysis and remote sensing. His work integrates deep learning for building extraction from aerial/LiDAR data, semantic modeling in 3D urban environments, and neural network-based environmental prediction systems. Key methodological focuses include mean-shift algorithms, Hough transforms, and multisource spatial data fusion. Analysis of his recent publications reveals consistent specialization in computer vision techniques for urban infrastructure analysis, particularly building contour detection and 3D reconstruction using convolutional neural networks. The integration of aerial imagery with elevation data and robust line detection algorithms represents a dominant research trajectory across his 70+ publications. No scientific awards were documented in the source materials. He has coordinated two European Union co-funded research programs and participated in multiple national projects, with expertise in environmental data processing and intelligent spatial analysis systems. His leadership extends to directing research laboratories and international academic programs, demonstrating significant grant management experience. Professor Vassilas leads the Intelligent Knowledge, Image & Information Systems research Laboratory (ISLab), which focuses on advancing computer vision techniques for urban planning and environmental monitoring applications through interdisciplinary team collaboration.