Prof. Nikos Mamoulis is a Professor at the Department of Computer Science & Engineering, University of Ioannina, and a Lead Researcher at Archimedes Research Unit, ATHENA RC. He holds a PhD from Hong Kong University of Science and Technology (HKUST) and a Diploma from the University of Patras. His research focuses on spatial data management, big data analytics, data privacy, and uncertain data systems. Education : Ph.D., Computer Science, Hong Kong University of Science and Technology (2000) 5-year Diploma, Computer Engineering and Informatics, University of Patras (1995) Research Interests : Complex data management (spatial, spatio-temporal, time-series, text, graphs), big data analysis, privacy preservation, and uncertain data systems. He develops scalable algorithms and systems for modern data management challenges, with applications in transportation, social networks, and geospatial analytics. Projects & Grants : MESA: In-memory Spatial Analytics Made Scalable (HFRI-funded, PI) MORE: Real-time Energy Data Management (H2020, Senior Researcher) Smart City Bus Platform (ERDF-funded, PI/Coordinator) Awards : Outstanding Young Researcher Award (2008-2009, HKU) Best Paper Award at SSTD 2015 Test of Time Award at MDM 2022 Labs & Collaborations : Leads the Archimedes Research Unit at ATHENA RC, collaborating with institutions like HKUST and Uppsala University on spatial and big data systems. Active in organizing top-tier conferences like SIGMOD, EDBT, and ICDE.
Yannis Theodoridis is a Professor at the Department of Informatics, University of Piraeus, leading the Information Systems Laboratory (InfoLab). He specializes in spatiotemporal databases, mobility analytics, and maritime data science. His research focuses on trajectory analysis, location-based services, and big data frameworks for transportation and maritime surveillance. Key projects include the MOD (Moving Objects Databases) initiative, the ARGOS framework for real-time trajectory prediction, and contributions to the HERMES trajectory database engine. He has advised 7 PhD students and co-authored numerous papers in IEEE and ACM venues. His work addresses challenges like vessel collision risk assessment, urban mobility optimization, and maritime event detection. He serves on the editorial board of the International Journal of Data Warehousing and Mining and contributes to conferences like PCI and ECML PKDD. His labs emphasize interdisciplinary approaches to mobility data science, integrating machine learning with domain-specific analytics.
Nikos Giatrakos is an Assistant Professor at the School of Electronic & Computer Engineering, Technical University of Crete, and a core member of the Software Technology and Network Applications Lab (SoftNet) . His work bridges Big Data systems, IoT, and advanced analytics, with a focus on real-time processing and scalable architectures. Previously, he served as a postdoctoral researcher at the same laboratory. Education PhD in Computer Science, University of Piraeus (2012) Postgraduate Diploma in Information Systems, Athens University of Economics and Business (2008) BSc in Computer Science, University of Piraeus (2006) Research Focus : Nikos specializes in software architectures for Big Data streaming, including Distributed Big Data Processing , Federated Machine Learning , Cloud-to-Edge Data Management , and Approximate Query Processing . His work has also advanced Complex Event Processing and Outlier Detection in decentralized environments. Scientific Contributions : His research has led to the DAG* workflow optimizer for IoT, the SuBiTO framework for real-time neural learning, and the INFORE approach for cross-platform analytics. He received the Best System Demonstration Award at ACM CIKM 2020 for INforE. Academic Leadership : Nikos teaches Object-Oriented Programming, Data Science, and Distributed Systems. He has supervised numerous European and national grants as Principal Investigator and served on program committees for top-tier conferences like SIGMOD, VLDB, and DEBS.
Nikolaos Pelekis is a Professor at the Department of Statistics and Actuarial Science, School of Finance and Statistics, University of Piraeus, where he teaches courses in Data Science, Data Management, Information Systems, and Computer Programming. He has been actively involved in both undergraduate and postgraduate education, offering specialized courses such as "Statistical Data Mining Methods" in the Applied Statistics Master's program and "Big Data Management" in the Cybersecurity and Data Science postgraduate program. Born in 1975, Professor Pelekis earned his Bachelor's degree in Computer Science from the University of Crete (1998), followed by an MSc in Information Systems Engineering (1999) and a PhD in Moving Object Databases (2002) from UMIST University in the United Kingdom. His educational background laid the foundation for his distinguished career in data science and database management. Professor Pelekis' research spans multiple domains within data science and database management, with particular emphasis on mobility data analytics. His work focuses on data mining, big data management and analytics, with special attention to location and motion data including trajectories of moving objects. He has made significant contributions to spatial and spatiotemporal database management, moving object database systems, privacy-preserving data mining, and OLAP analysis. His research bridges theoretical foundations with practical applications, particularly in maritime and transportation domains. An analysis of Professor Pelekis' recent publications reveals a strong trend toward maritime data analytics and vessel traffic prediction. His work increasingly focuses on applying machine learning techniques to maritime trajectory data, developing systems for collision risk assessment, vessel location forecasting, and maritime route prediction. The research demonstrates a progression from foundational database management techniques to sophisticated analytics for time-critical mobility forecasting, with applications in aviation and maritime domains. Five best research paper awards 1st & 3rd place in the SemEval-2017 competition 3rd place in the ACM SIGSPATIAL Cup 2016 competition Best paper award at ACM SIGSPATIAL'14 (Path-based Queries on Trajectory Data) Best paper award at ER'13 (Baquara: A Holistic Ontological Framework for Movement Analysis with Linked Data) Best application paper award at ICDM'09 (Clustering Trajectories of Moving Objects in an Uncertain World) Ralf H. Güting best research paper award at SSTD'21 (A Novel Indexing Method for Spatial-Keyword Range Queries) Best Demo Paper award at SSTD'21 (MaSEC: Discovering Anchorages and Co-movement Patterns on Streaming Vessel Trajectories) Professor Pelekis has been actively involved in advising and research funding acquisition. He has participated in over 10 European and National Research and Development projects as principal investigator or key researcher. His leadership extends to directing research laboratories and coordinating large-scale collaborative projects. As co-founder of the Data Science Lab - DataStories at the University of Piraeus, he has mentored numerous researchers and students. His research has been supported by prestigious funding programs including Horizon Europe, Horizon 2020, and national research initiatives. Professor Pelekis co-founded and leads the Data Science Lab - DataStories at the University of Piraeus, which comprises 9 faculty members from 4 different Departments along with experienced and young researchers. He previously served as Head of Research for the Information Management Lab (InfoLab) at the Department of Informatics, University of Piraeus (2005-2014). His current research team is actively engaged in multiple European projects including "DAT.AI – Energy-efficient AI-ready Data Spaces" and "EMERALDS – Extreme-scale Urban Mobility Data Analytics as a Service," focusing on cutting-edge applications of data science in maritime and urban mobility contexts.
Angelos Alexopoulos is an Assistant Professor at the Department of Economics , Athens University of Economics and Business . He has held Research Associate positions at the University of Cambridge, University College London, and University of Exeter in the UK. PhD: Athens University of Economics and Business Research Focus: Computational Statistics, Econometrics, Bayesian Analysis, Network Modelling Publications span Bayesian inference, epidemic forecasting, machine learning for fraud detection, and econometric methodology. Key journals include Journal of the Royal Statistical Society , Journal of Computational and Graphical Statistics , and Statistics and Computing . 2024: Gaussian invariance in MCMC 2024: Epidemic nowcasting models 2023: VAT fraud detection with ML Awards include certifications in Deep Learning (Coursera), Blockchain (edX), and Object-Oriented R Programming (DataCamp).
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.
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.
Konstantinos Soulis is an Assistant Professor at the Department of Natural Resources Management and Agricultural Engineering , Agricultural University of Athens , specializing in Geoinformatics, Hydrology, and Water Resources Management . His work integrates Geographic Information Systems (GIS) , Remote Sensing , and Hydrological Modeling to address agricultural and environmental challenges. Research Interests: Focus on Agro-hydrology , Soil-Water Dynamics , Wildfire Hydrological Effects , and Climate Dataset Applications in agriculture. Editorial Roles: Co-Editor-in-Chief of the Hydrological Sciences Journal and member of editorial boards for Remote Sensing and Journal of Mountain Science . Teaching: Offers courses in Advanced Hydrology , GIS Applications , and Spatial Statistics at both undergraduate and postgraduate levels. Awards: Recognized with the Best Associate Editor Award (2020) and multiple Publons Peer Review Awards (2017-2019). Ranked in the Top 2% of Researchers Worldwide (2021-2022). Publications: Over 70 works focusing on hydrological modeling , green roof systems , and land use impact assessment using geospatial technologies.
Professor Michalis Vazirgiannis is affiliated with the Informatics Department at Athens University of Economics and Business (AUEB). He holds a PhD in Informatics from the National and Kapodistrian University of Athens (1994), with prior degrees in Physics, Robotics, and Knowledge-Based Systems. His research focuses on data mining, machine learning, clustering algorithms, and web mining. He has led EU-funded projects like SQO-OSS and NGWeMiS, and contributed to initiatives like DB-GLOBE and I-KnowUMine. Awards include ERCIM Post-Doctoral Scholarship (2001) and Marie Curie Fellowship (2006). He serves on editorial boards (e.g., IDA Journal) and program committees for conferences like IEEE/ICDM and ECML/PKDD. His work spans theoretical advancements and practical applications in distributed systems, semantic networks, and temporal graph analysis. Education: BSc Physics, NKUA (1986) MSc Robotics, NKUA (1988) MSc Knowledge-Based Systems, Heriot-Watt University (1989) PhD Informatics, NKUA (1994) Research interests include semi-supervised learning, page ranking, and distributed feature selection. His publications address clustering frameworks, peer-to-peer systems, and normalized PageRank for evolving graphs. He has contributed to patents and held visiting researcher positions at INRIA, Fraunhofer, Max Planck, and IBM India.
Vasilis Charmandaris is a Professor of Physics at the University of Crete and an Adjunct Research Professor at the European University Cyprus . He previously served as Director of the Institute of Astrophysics at the Foundation for Technology & Research (FORTH) and of the Skinakas Observatory , and was elected President of FORTH in March 2025. His research focuses on observational astrophysics, particularly extragalactic systems, active galactic nuclei (AGN), and star formation in infrared-bright galaxies. Education University of Crete Degree in Physics, Aristotle University of Thessaloniki (1989) Ph.D. in Astrophysics, Iowa State University (1995) Charmandaris’ work leverages JWST , Spitzer , and ALMA data to study the interplay between AGN, star formation, and the interstellar medium (ISM) in merging galaxies. His recent publications emphasize dust-obscured activity , molecular outflows , and high-redshift galaxy analogs , with a strong focus on PAH emission , gas dynamics , and feedback mechanisms . Scientific Awards Marie Curie Fellowship As a leader in observational astrophysics, Charmandaris has directed major research institutes and contributed to GOALS-JWST campaigns. His work spans galactic winds , cluster formation , and instrumentation development (e.g., CAFE tool ), with ongoing efforts in high-redshift galaxy mergers and cosmic feedback processes . Research Infrastructure Director, Skinakas Observatory (2019–2025) Director, Institute of Astrophysics at FORTH (2013–2018, 2019–2025) Principal Investigator on multiple JWST and ALMA programs
Associate Professor Dimitris Zisis leads the Smart Move Laboratory at the University of the Aegean's Department of Product & Systems Design Engineering, focusing on autonomous vessels and marine robotics. His research spans maritime informatics, trajectory analytics, and Big Data applications. He has published over 80 works with 3,900+ citations, including a top-0.1% cited paper on cloud computing security. As a Horizon Europe National Representative for Cluster 4 (Digital, Industry, Space), he advises EU digital policy. He previously led BDVA's Data Science/AI Task Force and participated in EU High-Level Expert Groups on B2G data sharing. He chairs the IEEE's maritime systems committees and edits Elsevier's Future Generation Computer Systems journal. Projects: Horizon 2020 VesselAI, INFORE, BigDataOcean Awards: Top 2% scientist in Information Systems (2019-2021) Memberships: IEEE Senior Member, Hellenic Institute of Marine Technology Fellow His lab develops advanced maritime systems, including the Accu-Waves platform for port safety and the Ro-Boat Race for autonomous vessel innovation. Recent work emphasizes federated learning for vessel location forecasting, scalable maritime event detection, and digital twin applications.
Paris Mastorocostas is a Professor at the University of West Attica, specializing in computational intelligence, signal processing, and algorithmic data mining. His research focuses on neuro-fuzzy systems, deep learning applications, and their integration into domains like energy systems, transportation networks, and industrial automation. He has pioneered methodologies for short-term load forecasting, telecommunications fraud detection, and adaptive noise cancellation in medical signals. Key research themes include: Neuro-fuzzy modeling for dynamic systems Machine learning in energy and transportation Data warehouse development using Python/MySQL UAV-based inventory quantification Graph neural networks for urban metro flow His work demonstrates a strong interdisciplinary approach, combining algorithm design with practical industrial applications. Notable contributions include the ReNFuzz-LF model for electricity load forecasting and TMD-BERT for transportation mode detection. His publications span over 25 years, showing sustained innovation in computational intelligence techniques and their real-world implementation.
Christos Doulkeridis is a Professor at the Department of Digital Systems, University of Piraeus, Greece. He specializes in parallel and distributed query processing, large-scale data management, and spatio-temporal data systems. His work focuses on optimizing big data frameworks for mobility analytics and distributed knowledge discovery. He holds a PhD from Athens University of Economics and Business (2007) and has been involved in several EU-funded projects like EMERALDS, Green.DAT.AI, and MobiSpaces as Principal Investigator or Coordinator. Education: PhD in Informatics (2007), Athens University of Economics and Business M.Sc. in Information Systems (2003), Athens University of Economics and Business Diploma in Electrical and Computer Engineering (2001), National Technical University of Athens Awards: Best Paper Awards at SIGSPATIAL, SSTD, EuroVA Marie-Curie and ERCIM Fellowships SemEval 2017 Task 4 & 6 competition wins His research interests include scalable data processing frameworks, mobility data analytics, and spatio-temporal query optimization. He leads projects like MobiSpaces (Horizon Europe), aiming to create energy-efficient data spaces for mobility data. He has published over 100 papers in top venues like EDBT, SIGMOD, and ICDE, focusing on distributed systems, query processing, and machine learning applications in data management. Teaching: He teaches undergraduate and graduate courses in data structures, data analysis, big data processing, and database systems at the University of Piraeus. His courses integrate practical tools like Spark and Hadoop for real-world data challenges.
Nikos Pelekis is a Lecturer at the Department of Statistics and Insurance Science and a researcher at the Information Management Group in the Department of Informatics at the University of Piraeus. His research specializes in mobility data management, spatiotemporal databases, and knowledge discovery from moving objects. Research Focus: Design of trajectory database engines (HERMES) Semantic-aware mobility data mining Privacy-preserving techniques for sensitive trajectory data Key Achievements: Best Paper Award at ER'13 and IEEE ICDM'09 Author of "Mobility Data Management and Exploration" monograph Principal researcher in EU projects including GeoPKDD and MODAP
Maria Ragousi is a faculty member in the Department of Electrical and Electronic Engineering at the University of West Attica, affiliated with the School of Engineering. She holds a PhD in Digital Signal Processing and Pattern Recognition from NTUA (1994), an MSc from the University of Virginia (1990), and a Diploma in Electrical Engineering from NTUA (1987). Her academic role is as a Lecturer, focusing on courses such as Digital Signal Processing, Biomedical Signal Processing, and Educational Technologies. Education: PhD (1994): Digital Signal Processing & Pattern Recognition, NTUA MSc (1990): Digital Signal Processing, University of Virginia Diploma (1987): Electrical Engineering, NTUA Her research interests span Digital Signal Processing (DSP), Spectral Analysis, Pattern Recognition (including neural networks and genetic algorithms), Biomedical Signal Processing, Wireless Sensor Networks for environmental monitoring, and E-Learning technologies. She has contributed to over 30 peer-reviewed publications, including work on EEG-based person identification, environmental sound classification, and educational data mining. Recent research emphasizes integrating machine learning into education and developing tools for environmental acoustic monitoring. She has collaborated on projects like the ESOUNDMAPS initiative, deploying wireless sensor networks for real-time environmental sound mapping. Her work also explores active learning methodologies in physics education and visualization of e-learning platform data. Maria advises courses at both undergraduate and postgraduate levels, including specialized programs in medical signal analysis and smart electronic systems. She actively participates in academic governance and has led several educational innovation projects funded by national and international grants.
