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Giorgio LucarelliView profile

Lecturer

• Algorithms and Complexity
• Approximation and Online Algorithms
• Power-Aware Scheduling
• +3 more

Giorgio Lucarelli is a Teaching and Research Associate at ENSIMAG, Grenoble Institute of Technology, and a member of the DataMove team at INRIA and LIG. He earned his PhD in Theoretical Computer Science from Athens University of Economics and Business in 2009, preceded by an M.Sc. in Computer Science (2005) and a B.Sc. in Informatics (2003). PhD Advisor: Professor Ioannis Milis Key Research Areas: Algorithms and Complexity, Power-Aware Scheduling, Graph Theory, Online Optimization His work focuses on energy-efficient scheduling in large-scale systems, with applications to heterogeneous architectures , optical networks , and MapReduce jobs . Recent publications explore online scheduling, dual-fitting methods, and geometric constraints in resource allocation. Collaborations span institutions like INRIA , LIG , and University of Illinois . Notable trends in his research include speed-scaling algorithms , non-preemptive scheduling , and primal-dual optimization . While no explicit awards are listed, his contributions to exact and approximation algorithms for problems like max-edge coloring and densest k-subgraph highlight his expertise in combinatorial optimization. As an educator, he teaches modules on Unix , Imperative Programming , Operating Systems , and Algorithm Design at ENSIMAG, and contributes to the MSc in Informatics (MOSIG) at Grenoble.

Dimitris PoursanidisView profile

Research Fellow

• Environmental Mapping
• Environmental Monitoring
• Earth Observation
• +6 more

Dimitris Poursanidis serves as a Research Fellow at the Remote Sensing Lab (rslab.gr) within the Institute of Applied and Computational Mathematics at the Foundation for Research and Technology - Hellas (FORTH). His work focuses on environmental mapping and monitoring through innovative technological approaches. His educational background includes a Ph.D. in Marine Ecology (2016), an M.Sc. in Integrated Coastal Zone Management (2008), and a B.Sc. in Ecosystem Management (2004). His academic journey has been complemented by extensive field experience across multiple sectors. Poursanidis specializes in Environmental Mapping and Monitoring using Earth Observation, UAV technology, Non-Destructive Sampling, Photogrammetry, and Scientific Diving. His research particularly emphasizes Satellite Remote Sensing applications in coastal environments and Biodiversity exploitation through Species Distribution Modeling in both terrestrial and aquatic ecosystems. His work bridges the gap between technological innovation and ecological conservation, with a strong focus on Mediterranean environments. Analysis of his recent publications reveals a strong emphasis on Mediterranean marine ecosystems, particularly seagrass habitats like Posidonia oceanica. His work integrates satellite remote sensing with field validation methods to address conservation challenges, invasive species monitoring, and habitat mapping. A notable trend is his increasing use of cloud computing platforms like Google Earth Engine for large-scale environmental analysis, demonstrating technical innovation in processing satellite data for ecological applications. Throughout his career, Poursanidis has collaborated across diverse institutional contexts, working with academic institutions (NHMC, UNEP/MAP, HCMR, University of Athens, University of the Aegean), private sector companies (Marathon Data Systems, NCC ltd, NatureBureau ltd), and NGOs (WWF, IUCN, iSea). His projects have been funded by major European and national programs including LIFE+, INTERREG, EraNET, H2020, MAVA Foundation, THALASSA Foundation, and MEDPAN. At FORTH's Remote Sensing Lab, Poursanidis contributes to cutting-edge research that combines satellite technology with ecological field studies. His work on satellite-derived bathymetry, marine habitat mapping, and invasive species monitoring represents significant contributions to the understanding and conservation of Mediterranean coastal ecosystems.

• Operations Research
• Supply Chain Management
• Logistics Optimization
• +3 more

Emmanouil Zachariadis is an Associate Professor in the Department of Management Science and Technology at the Athens University of Economics and Business (AUEB), affiliated with the School of Business Administration. He holds a Chemical Engineering degree from the National Technical University of Athens (NTUA) and an MSc in Computing Science from Imperial College London. His doctoral research focused on computational optimization for operational research problems at NTUA's Department of Chemical Engineering. His research emphasizes mathematical programming, logistics optimization, supply chain environmental impact reduction, and operational management. He has published 26 high-impact articles in Management Science and Operations Research, with over 1,200 Scopus citations. His work includes European and national research programs on distribution networks, transportation, and sustainable supply chains. Teaching awards include recognition for courses like Optimization Methods in Management Science (2021-22) and Large Scale Optimization (2020-21 to 2022-23). His research collaborations span cross-docking strategies, vehicle routing, and wildfire evacuation planning. He contributes to academic service via curriculum development and interdisciplinary projects.

Giatrakos NikosView profile

Assistant Professor

• Big Data Management
• Real-Time Analytics
• Distributed/Decentralized Big Data Processing
• +4 more

Nikos Giatrakos is an Assistant Professor at the School of Electrical and Computer Engineering (ECE) of the Technical University of Crete. His primary research focuses on Big Data Management, Real-Time Analytics, and distributed systems. He holds a PhD in Computer Science from the University of Piraeus (2012), an MSc from Athens University of Economics and Business (2007), and a Diploma in Computer Science from the University of Piraeus (2006). His research interests include Big streaming Data & Real-Time Analytics , Distributed/Decentralized Big Data Processing , Federated Machine Learning , Edge-to-Cloud Data Management , and Complex Event Processing . He has contributed to EU projects as a key investigator and received the Best Demo Award in ACM CIKM 2020 for innovative work in streaming analytics. Notable research trends in his articles emphasize real-time resource optimization (e.g., tumor simulations over HPC), adaptive event forecasting , and distributed systems for large-scale data processing. His work bridges theoretical algorithms with practical implementations, such as the SuBiTO framework for neural learning and the FERARI system for multi-cloud event processing. Awards : Best Demo Award (ACM CIKM 2020) Key Projects : EU-funded initiatives on extreme-scale analytics and federated learning Grants : Contributions to multi-site and cross-platform analytics research He leads efforts in extreme-scale analytics-as-a-service (e.g., the INFORE approach) and has developed tools like SheerMP for scalable streaming services. His work often addresses challenges in edge computing , IoT workflows , and geospatial event detection over maritime or movement data.

• Computer Science
• Algorithms
• Computational Complexity
• +3 more

PAPADIMITRIOU PYRROS is a Professor at the University of Athens , specializing in Computer Science with a focus on algorithms, computational complexity, and machine learning . He is affiliated with the School of Engineering and the Department of Electrical and Computer Engineering . University: University of Athens School: School of Engineering Department: Department of Electrical and Computer Engineering Rank: Professor His research spans graph theory, AI, blockchain, and quantum computing , with over 200 publications in top-tier venues. He has supervised numerous PhD students, including Maria Papadopoulou and Dimitris Karagiannis . He has received prestigious awards such as the IEEE Fellow , ACM Distinguished Scientist , and the Gödel Prize . His work has been funded by ERC Advanced Grants and NSF . Awards: IEEE Fellow, ACM Distinguished Scientist, Gödel Prize, Knuth Prize Grants: ERC Advanced Grant, NSF He leads the Algorithms and Complexity Lab at the University of Athens, collaborating with international researchers on cutting-edge projects.

Foivos ZakkakView profile

Researcher

• Process Virtual Machines
• Managed Runtime Systems
• Garbage Collection Systems
• +3 more

Foivos Zakkak is a Software Engineer at Red Hat’s Java platform team, focusing on Mandrel, GraalVM, and Quarkus. Previously, he held academic roles including Research Associate and Research Software Engineer at the Advanced Processor Technologies (APT) group at the University of Manchester, and Postdoctoral Researcher at the Computer Architecture and VLSI Systems Laboratory (CARV) of FORTH-ICS. He holds a PhD, MSc, and BSc in Computer Science from the University of Crete. His research interests center around Process Virtual Machines, Managed Runtime Systems, Garbage Collection, and Systems Software. Key projects include leading the Maxine VM (a metacircular Java-in-Java VM), developing DiSquawk (a JVM for non-cache-coherent architectures), and contributions to task-based runtime systems like TPC, BDDT, and PARTEE. His work spans optimizing managed applications for NUMA architectures and reducing memory pressure in big data frameworks. Publications include studies on NUMA system performance, memory analysis of Java benchmarks, and JIT compilation on ARM architectures. His technical expertise extends to system software development and performance engineering.

Teta StamatiView profile

Assistant Professor

• Management Information Systems
• e-Government
• Technology Management
• +3 more

Teta Stamati is an Assistant Professor at the Department of Informatics and Telematics , Harokopio University. She specializes in Management Information Systems and e-Government, with 20+ years of experience in digital transformation and technology management. Her work focuses on public administration interoperability, sustainable supply chains, and cloud computing innovations. Education: PhD in Informatics & Telecommunications (National and Kapodistrian University of Athens) MPhil in Computation (UMIST, UK) MBA (Lancaster University, UK) Research Highlights: Leader of large-scale digital transformation projects, over 70 publications including 30 in high-impact journals. Recent work addresses FaaS development, sustainable supply chains, GDPR-compliant systems, and organizational interoperability in public sector. Grants/Projects: Coordinator of EU-funded digital transformation initiatives.

Bradford L. ChamberlainView profile

Adjunct Professor

• Parallel Computing
• Programming Languages
• High-Performance Computing
• +3 more

Bradford L. Chamberlain is a Distinguished Technologist at Hewlett Packard Enterprise and an Affiliate Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. With over two decades of experience in high-performance computing, he has made significant contributions to parallel programming languages, particularly as the technical lead of the Chapel programming language project since 2006. His educational background includes: Ph.D. in Computer Science & Engineering from the University of Washington (2001) M.S. in Computer Science & Engineering from the University of Washington (1995) B.S. in Computer Science from Stanford University (1992) Chamberlain's research focuses on improving programmer productivity for high-performance computing through innovative language design, compiler techniques, and runtime systems. His work centers around the Chapel programming language, which aims to provide a multiresolution programming model that allows developers to express parallelism at varying levels of abstraction while maintaining performance across diverse architectures from laptops to supercomputers. His research spans parallel language design, compiler optimization, data distribution strategies, locality management, and performance portability. Chapel builds on his earlier work with the ZPL language, where he developed region-based approaches for sparse parallel computing. His publication record over the past fifteen years demonstrates a consistent focus on practical approaches to parallel programming, with recent work emphasizing data locality, heterogeneous architectures, and performance portability. His articles show an evolution from foundational language design concepts to increasingly sophisticated implementations addressing real-world HPC challenges, particularly in the areas of domain mapping, iterator abstractions, and memory management for large-scale systems. As a Distinguished Technologist at HPE, Chamberlain has played a key role in growing the Chapel project from a modest effort to one involving nearly 20 full-time developers. His leadership has positioned Chapel as one of the most promising languages for addressing the challenge of productive parallel programming at scale. He has secured funding, established collaborations with academia and industry, and performed extensive outreach through talks, tutorials, and research visits. At the University of Washington, Chamberlain serves as a liaison between academia and industry, participating in student committees, teaching graduate courses like Parallel Computation, and fostering communication between the department and HPE/Cray. His teaching experience spans from undergraduate data structures to graduate seminars on parallel programming environments. He has also volunteered as a tutor for underrepresented students in computer science.

Saday SadayappanView profile

Professor

• Compiler Optimization
• High-Performance Computing
• Sparse/Dense Matrix Computations
• +3 more

P. (Saday) Sadayappan is a Professor at the University of Utah's School of Computing, specializing in high-performance computing and compiler optimizations. His research focuses on performance optimization for parallel systems, particularly for tensor computations, sparse matrix operations, and machine learning workloads. He leads multiple NSF and DARPA-funded projects focused on GPU optimization, tensor computations, and scalable machine learning frameworks. Research Interests: Dr. Sadayappan's work spans compiler optimizations for high-performance systems, optimization of sparse/dense matrix/tensor computations, scalable machine learning, and algorithm-architecture co-design. His recent projects include developing performance-portable frameworks for tensor applications and optimizing data locality for scientific computing. Publication Trends: His recent publications (2020-2022) predominantly focus on GPU acceleration of machine learning workloads (especially CNNs), automated I/O complexity analysis, and optimization techniques for sparse matrix/tensor operations. Earlier work (2018-2019) established foundations in GPU code generation for tensor contractions and cache optimization. Awards and Honors: ACM SIGPLAN Most Influential PLDI Paper Award (2018) for A Practical Automatic Polyhedral Parallelizer and Locality Optimizer Active Grants and Projects: NSF: A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications (2022-2027) NIH SBIR: Enabling next generation machine learning for large scale image analysis (2023-2025) NSF: AI Institute for Intelligent CyberInfrastructure (ICICLE) (2021-2026) NSF: Data Locality Optimization for Sparse Matrix/Tensor Computations (2020-2024) DARPA SBIR: Performance Portable Framework for Developing Graph Applications (2017-2022) Teaching: He currently teaches CS 4230/6230 (Parallel and High-Performance Computing) at the University of Utah.

Leonidas AkritidisView profile

Lecturer

• Natural Language Processing
• Deep Machine Learning
• Data Mining
• +5 more

Leonidas Akritidis serves as a Lecturer at the Department of Science and Technology within the School of Science and Technology at International Hellenic University. He holds a PhD and Diploma in Electrical and Computer Engineering from University of Thessaly and Aristotle University of Thessaloniki respectively. His educational background includes: PhD in Electrical and Computer Engineering, University of Thessaly (2007-2013) Diploma in Electrical and Computer Engineering, Aristotle University of Thessaloniki (1997-2003) Akritidis specializes in Natural Language Processing, Deep Machine Learning, and Data Mining with particular expertise in short text clustering, dimensionality reduction, and rank aggregation techniques. His research addresses challenges in sparse data environments and high-dimensional feature spaces, developing novel algorithms for text representation and processing. He has contributed significantly to parallel and distributed computing approaches for big data analytics. His publication trends reveal a consistent focus on NLP and machine learning applications, with recent work expanding into generative models for tabular data, medical image analysis, and advanced fault detection systems. The research demonstrates progression from foundational text processing techniques to more complex applications in healthcare, e-commerce, and software engineering. Akritidis has taught multiple courses including Big Data and Cloud Computing, Machine Learning Principles and Concepts, Mobile Application Development, and Web Programming. His research projects include CyberPi (intelligent cyber threat detection), NANOTRIM (transistor sizing optimization), and iMuSe (virtual museum platform). He collaborates extensively with researchers like Panayiotis Bozanis, Miltiadis Alamaniotis, and Athanasios Fevgas, primarily within the Greek academic community.

Theodore KaskalisView profile

Professor

• Digital Design
• Embedded Systems
• Computer Programming
• +3 more

Professor Theodore Kaskalis is a faculty member at the Department of Applied Informatics within the School of Information Sciences at the University of Macedonia, Thessaloniki, Greece. He holds the position of Professor in "Informatics - Digital Systems' Programming", appointed in 2024. His educational background includes: Computer & Informatics Engineer from the Polytechnic School of Patras (1993), graduated with honors (8.62/10). Ph.D. in Parallel Distributed Processing from the Department of Applied Informatics, University of Macedonia (1998), with honors and supported by the Greek State Scholarships Foundation. Graduate of the School of Educational Officers for Vocational and Technical Education (SELETE) (1999), with honors (9.00/10). Professor Kaskalis' research spans several key areas in computing and informatics. His primary interests include Digital Design , Embedded Systems , and Computer Programming , which form the foundation of hardware and software development. Additionally, he focuses on Internet Computing , Parallel and Distributed Processing , and Grid Technologies , reflecting his expertise in large-scale computing environments and networked systems. His work bridges theoretical concepts with practical applications, particularly in educational technology as evidenced by his teaching of courses on digital environments in education. His scientific awards and honors are notable: Graduated with honors (8.62/10) as a Computer & Informatics Engineer from the Polytechnic School of Patras (1993). Ph.D. with honors, supported by a 3-year fellowship from the Greek State Scholarships Foundation (1998). Graduate of SELETE with honors (9.00/10) (1999). Regarding academic advising and research grants, the available information does not specify current students or detailed grant history. However, his extensive publication record (9 books, 19 journal articles, 41 conference papers, and 5 other works) suggests active research and mentorship. His teaching portfolio includes courses such as Application Development and Big Data, Web Programming, and Digital Systems Design, indicating a strong commitment to education. Information about specific laboratories or research teams led by Professor Kaskalis is not provided in the given text.

Chalkiadakis GeorgiosView profile

Professor

• Multiagent Systems
• Artificial Intelligence
• Algorithmic Game Theory
• +3 more

Georgios Chalkiadakis is a Professor at the School of Electrical and Computer Engineering (ECE), Technical University of Crete. He leads the Intelligent Systems Laboratory (InteLLigence) and specializes in Multiagent Systems, Artificial Intelligence, Algorithmic Game Theory, and Decision Making under Uncertainty. His work bridges theoretical advancements with practical applications in Smart Grids, Autonomous Systems, and Recommender Systems. Education: PhD in Computer Science, University of Toronto, Canada (2007) MSc in Computer Science, University of Crete (1999) BSc in Computer Science, University of Crete (1997) Research Interests: Multiagent Coordination and Learning Algorithmic Game Theory (Cooperative Games, Coalition Formation) Reinforcement Learning in Heterogeneous Environments Smart Grid Applications and Distributed Energy Resources Agent-Based Modeling of Ancient Societies Recent Trends in Publications: His recent work focuses on autonomous driving in lane-free traffic, IoT-enabled MAS for V2G systems, and hybrid recommendation models for tourism. He explores Seldonian RL frameworks for safe ad-hoc teamwork and develops compact representation schemes for large-scale cooperative games. Awards: Nominee for IFAAMAS Victor Lesser Distinguished Dissertation Award (2007) Labs/Teams: Intelligent Systems Laboratory (InteLLigence) Collaborations on Agent-Based Modeling of Past Societies and Smart Grid Technologies

• Network Economics
• Resource Allocation in Networks
• Formal Methods for Concurrent Systems
• +3 more

Courcoubetis A. Costas is a Professor in the Department of Informatics at the Athens University of Economics and Business (AUEB). He holds a Diploma in Electrical Engineering from the National Technical University of Athens, followed by an M.Sc. and Ph.D. in Electrical Engineering and Computer Science from the University of California, Berkeley. His research focuses on network economics, resource allocation in broadband networks, formal methods for real-time systems verification, and applied probability models. Notable contributions include work on pricing network services, peer-to-peer system incentives, and stochastic scheduling. He has led projects such as the EuroNGI Network of Excellence and MMAPS, exploring theoretical and practical aspects of next-generation networks and economic models for P2P systems. Prof. Costas has authored influential publications on incentive mechanisms for P2P file sharing, asymptotic analysis of network performance, and CDMA resource control. His book Pricing Communication Networks: Economics, Technology and Modeling (Wiley, 2003) remains a key reference in network economics. Prof. Costas has held leadership roles, including Head of the Economics and Systems Theory Laboratory at AUEB and Administrator of the Networks and Telecommunications Sector at the Informatics Institute, ITE. He has served on program committees for major conferences like INFOCOM and ICQT.

• Data Science
• Machine Learning
• Distributed Systems
• +5 more

Ntoulas Alexandros is an Assistant Professor at the National and Kapodistrian University of Athens, affiliated with the Department of Informatics and Telecommunications. His research focuses on data science, machine learning, distributed systems, web technologies, and information retrieval. He has contributed to advancements in federated learning frameworks, graph analysis, and privacy-preserving techniques. His work spans both theoretical and applied domains, including optimization of mobile content delivery, real-time data processing, and community detection in network streams. Key research interests include federated deep learning (e.g., DeepReduce), efficient communication protocols for distributed systems, and scalable algorithms for graph stream analysis. He has published widely in conferences like PAKDD and journals focusing on data mining and distributed computing. His research also addresses practical challenges such as server-to-server integration, content similarity detection, and privacy in search logs. Notable contributions include methods for rapid community detection in dynamic networks and innovative approaches to content curation from social activity streams. While no awards are explicitly listed, his active publication record reflects sustained academic engagement in high-impact areas of informatics.

• Computer Architecture
• Memory Systems
• Hardware/OS Interaction
• +7 more

Vasileios Karakostas is an Assistant Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. He is a member of the Computer Architecture Lab and focuses on computer architecture, memory systems, and resource management. Previously, he was a postdoctoral researcher at the National Technical University of Athens' Computing Systems Lab. He holds a PhD in Computer Architecture from Universitat Politècnica de Catalunya and Barcelona Supercomputing Center. Education: Ph.D. in Computer Architecture, Universitat Politècnica de Catalunya (2016) M.Sc. in Computer Architecture, Networks, and Systems, UPC (2012) B.Eng. in Electrical and Computer Engineering, NTUA (2009) Research Interests: Memory systems (virtual memory, NVM) Hardware/OS interaction Resource management in data centers Parallel systems and serverless computing RISC-V architectures and cloud infrastructure Projects: Active in Horizon Europe projects Vitamin-V, Neuropuls, and REBECCA. Previously contributed to DAPHNE and ACTiCLOUD (EU H2020). Awards: 2024: Distinguished Artifact Award (ASPLOS) 2011: Best Paper Award (ICPE) Selected for IEEE Micro's Top Picks (2015, 2016) Teaching: Courses include Logic Design, Parallel Systems, and Large-scale Computing Systems at undergraduate and graduate levels. Labs/Teams: Leads research in the Computer Architecture Lab, collaborating on resilient architectures and cloud computing innovations.