جست‌وجوی استاد و پژوهشگر

Nikos Mamoulisمشاهده پروفایل

استاد

• Spatial Data Management
• Big Data Analytics
• Data Privacy
• +۵ مورد دیگر

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.

• Algorithmic Graph Theory
• Parallel Algorithms
• Graph Theory
• +۴ مورد دیگر

Stavros Nikolopoulos is a Professor in the Department of Computer Science & Engineering at the University of Ioannina, Greece. He serves as Director of the Algorithms Engineering Lab and holds a PhD in Computer Science (1991, University of Ioannina). His research focuses on Algorithmic Graph Theory, Parallel Algorithms, Malware Detection, and Software Watermarking. He has published extensively in top-tier journals and conferences, including Discrete Applied Mathematics and Theoretical Computer Science. Education: B.Sc. in Mathematics, University of Ioannina (1982) M.Sc. in Computer Science, University of Dundee (1985) Ph.D. in Computer Science, University of Ioannina (1991) Research Interests: Design and Analysis of Algorithms Graph Algorithms (e.g., permutation graphs, cographs) Malware Detection via System-call Group Analysis Graph-based Watermarking Systems Discrete Event Simulation Awards & Recognition: Best Student Paper Award (WEBIST'13) Best Paper Award (CompSysTech'13) Labs & Projects: Director, Algorithms Engineering Lab Principal Investigator in EU-funded projects (e.g., HRAKLITOS, PENED-05)

Laxman Dhulipalaمشاهده پروفایل

استادیار

• Parallel Algorithms
• Graph Processing
• Parallel Graph Algorithms
• +۷ مورد دیگر

Laxman Dhulipala serves as an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park, while also working as a research scientist at Google Research with the Graph Mining team. Dr. Dhulipala earned his Ph.D. from Carnegie Mellon University under Guy Blelloch's supervision and completed a postdoctoral fellowship at MIT with Julian Shun. His research centers on efficient parallel algorithms, particularly for parallel clustering and graph processing, along with developing computational models for emerging hardware technologies. His scholarly output demonstrates significant expertise across parallel computing domains, with particular emphasis on scalable graph algorithms, dynamic data structures, and computational geometry. Dr. Dhulipala's work bridges theoretical computer science with practical systems implementation, producing algorithms that achieve both theoretical optimality and real-world performance. His research group has made substantial contributions to benchmarking frameworks including the Graph Based Benchmark Suite (GBBS) and ParClusterers Benchmark Suite, establishing standardized evaluation methods for graph processing systems. The collective work shows progression from theoretical foundations to practical implementations that handle massive-scale datasets. Best Paper Award at SPAA 2022 Best Paper Runner Up at VLDB 2022 Distinguished Paper Award at PLDI 2019 Memorable Paper Award Finalist at NVMW'20 CMU's SCS Dissertation Award Honorable Mention As an educator, Dr. Dhulipala mentors numerous graduate students while teaching advanced courses in algorithm design and parallel computing. His research collaborations span multiple institutions including Carnegie Mellon University, MIT, and Google Research, reflecting his position at the intersection of academia and industry research.

Kostas Magoutisمشاهده پروفایل

استاد

• Distributed computer systems
• Cloud computing
• Scalable data stores and stream processing
• +۳ مورد دیگر

Kostas Magoutis is Professor and Chair of the Computer Science Department at the University of Crete , and a collaborating researcher at FORTH-ICS . His research focuses on scalable distributed systems, cloud computing, IoT, and quantum-enhanced control. Education and Career: Ph.D. in Computer Science, Harvard University (2003) Research Staff Member, IBM T. J. Watson Research Center (2003-2009) Assistant Professor (2014-2019, tenured 2017) and Associate Professor (2020-2024), University of Crete Professor and Chair, University of Crete (2024-present) Research Interests: His work spans distributed computer systems , cloud computing , scalable data stores and stream-processing engines , Internet of Things , and the emerging area of quantum-enhanced control . Representative projects include the H.F.R.I.-funded QUADS (2025-2028) on quantum-enhanced adaptive systems, STREAMSTORE (2020-2023) on elastic stream processing, SmartCityBus on IoT-driven public transport, and the EU FP7 PaaSage project on model-based cloud lifecycle management. Awards and Honors: Best Paper Awards: USENIX ATC 2002, USENIX BSDCon 2002, IEEE SRDS 2014 (Best Student Paper), IoT 2024 (Runner-up) Grand Challenge Audience Award, ACM DEBS 2022 Best Poster Award, ACM EuroSys 2022 EU Marie Curie IEF Fellow (2009-2011) Alexander S. Onassis Fellow (1994-1995) and J. William Fulbright Scholar (1993-1994) Students and Mentoring: He has supervised or co-supervised more than 30 Ph.D., M.Sc. and undergraduate students, including Antonis Papaioannou (Ph.D. 2021), Efthimios Papageorgiou (current Ph.D.), and numerous M.Sc. graduates now in industry and academia. Labs and Teams: At FORTH-ICS he leads activities within the Distributed Systems and Storage Laboratory, coordinating research on scalable storage, stream processing, and IoT data management. The lab collaborates closely with European and national initiatives, hosting visiting researchers and industry partners.

Kostas Magoutisمشاهده پروفایل

استاد

• Distributed computer systems
• Scalable data stream processing
• Internet of Things
• +۳ مورد دیگر

Kostas Magoutis is Professor and Chair of the Computer Science Department at the University of Crete and collaborating researcher with FORTH-ICS. His research focuses on distributed systems, scalable data processing, IoT, and cloud computing, with projects including GreenInCities for urban regeneration and STREAMSTORE for stateful stream processing systems. Research interests include: Distributed computer systems architecture Elastic stream processing platforms Quantum-enhanced computing applications Multi-cloud application lifecycle management Recent publications demonstrate strong focus on federated data systems, quantum computing applications, and IoT-enhanced infrastructure, with consistent output in high-impact conferences and journals. Awards and distinctions: Multiple best paper awards from USENIX conferences Grand Challenge Audience Award at DEBS 2022 Marie Curie Fellowship and IBM Research awards Advises over 20 PhD and MSc students in distributed systems research. Leads multiple EU-funded projects and serves on program committees for top conferences including SOSP, EuroSys, and IEEE BigData. Directs research groups in distributed systems and cloud computing at FORTH-ICS.

• Artificial Intelligence
• Geospatial Data Science
• Knowledge Graphs
• +۵ مورد دیگر

Professor Manolis Koubarakis is a faculty member at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His research focuses on geospatial data science, knowledge graphs, entity resolution, and AI applications in Earth observation. He leads projects like ExtremeEarth and Plato, advancing semantic data cube systems and geospatial question answering engines. His work bridges AI and geospatial technologies, with contributions to frameworks like pyJedAI and Strabo 2 for managing big geospatial data. Research Interests include: Geospatial Question Answering Systems Entity Resolution and Entity Linking Ontology-Based Data Access (OBDA) Knowledge Graph Construction and Applications Earth Observation Data Analytics AI for Geoinformatics Notable Projects: ExtremeEarth: Combines Copernicus satellite data with machine learning for environmental analytics. Plato: Semantic data cube system enabling advanced querying of multidimensional datasets. GeoQA2: A geospatial question answering engine evaluated on large benchmarks. JedAI Family: Tools for scalable entity resolution in structured/semi-structured data. His publications span 20+ years, with recent emphasis on: AI-driven Earth observation systems Geospatial RDF benchmarking 3D geospatial interlinking Large language models for legal and health content delivery

Nikos Giatrakosمشاهده پروفایل

استادیار

• Big Data Management
• Internet of Things (IoT)
• Distributed Systems
• +۳ مورد دیگر

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.

Nick Bassiliadesمشاهده پروفایل

استاد

• Semantic Web
• Ontologies
• Linked Open Data
• +۷ مورد دیگر

Nick Bassiliades is a Professor at the School of Informatics , Aristotle University of Thessaloniki , Greece. His academic roles include serving as President of the Digital Governance Committee and the Digital Transformation of Greek Universities Committee, as well as Director of the Web, Data, and Knowledge Engineering Sector. Education: B.Sc. in Physics, Aristotle University of Thessaloniki (1991) M.Sc. in Applied Artificial Intelligence, University of Aberdeen (1992) Ph.D. in Parallel Knowledge Base Systems, Aristotle University of Thessaloniki (1998) His research focuses on Semantic Web , Ontologies , Knowledge Graphs , and applications in Artificial Intelligence , eGovernment , and Intelligent Agents . Recent publications emphasize ontological frameworks for requirements engineering, explainable AI, and electric vehicle knowledge graphs. He actively contributes to scientific communities as a Senior Member of IEEE and ACM , and serves as Co-Editor-in-Chief for the International Journal of Artificial Intelligence in Business and Management . His work involves collaborations with the Intelligent Systems laboratory and projects like XR4DRAMA for disaster management.

Stefan K. Mullerمشاهده پروفایل

استادیار

• Type Systems
• Parallel Computing
• Resource Analysis
• +۴ مورد دیگر

Stefan K. Muller is the Gladwin Development Chair Assistant Professor in the Computer Science Department at Illinois Institute of Technology. He previously served as a Postdoctoral Researcher at Carnegie Mellon University from 2018-2020 following completion of his PhD there under advisor Umut A. Acar. His academic journey began with an AB from Harvard University in 2012 under Stephen Chong. Dr. Muller's research centers on programming language techniques to improve correctness and efficiency of software, particularly in parallel computing domains. His work spans language and type system design, static resource analysis, and parallel computing methodologies. He leads the Responsive Parallelism research project which extends implicit parallelism models to handle features of consumer software like user interaction and responsiveness requirements. His publication record shows consistent output in top-tier venues including PLDI, POPL, ICFP, and SPAA, with recent work focusing on graph types, responsive parallelism, and resource-aware GPU programming. His research has been supported by NSF grant CCF-2107289. Current advisees include Marelle León (BS), Godha Pallavi Bhogadi (MS), and Alex Friedman (PhD) Former students have gone on to positions at Apple, Amazon, Bloomberg, American Express, and PhD programs at UPenn Teaching responsibilities at Illinois Tech include graduate courses CS534 (Types and Programming Languages), CS536 (Science of Programming), CS440 (Programming Languages and Translators), and CS443 (Compiler Construction). Previously at CMU, he taught Principles of Functional Programming.

Diomidis Spinellisمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Computer Science
• +۵ مورد دیگر

Professor Diomidis Spinellis is a renowned academic in Software Technology at Athens University of Economics and Business (AUEB). He specializes in software engineering practices, code quality, AI ethics, and system architecture. His work bridges theoretical advancements with practical applications in industry, emphasizing reproducibility and empirical methods. Recipient of the IEEE Computer Society's prestigious 'Distinguished Contributor Recognition,' Spinellis is the sole Greek scientist to achieve this honor. His research spans software evolution, security, and open-source ecosystems, with a focus on methodologies like refactoring, static analysis, and debugging strategies. Key research interests include AI-generated content detection, modular data analytics, and incident management systems. His studies often leverage large-scale datasets (e.g., Unix evolution, Linux supercomputing analysis) to uncover patterns in software behavior and development practices. Publications frequently address emerging technologies' societal impacts, such as energy-efficient computing and ethical AI deployment. He advocates for reproducible research through tools like the Alexandria3k framework and contributes to open-source initiatives.

Dimitrios Tsoumakosمشاهده پروفایل

دانشیار

• Big Data Management
• Distributed Systems
• Cloud Computing
• +۵ مورد دیگر

Dimitrios Tsoumakos serves as an Associate Professor of Big Data Management Systems at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA) where he also directs DBLab, the Knowledge and Database Systems Laboratory. His academic career spans over two decades with significant contributions to large-scale data management and distributed systems. Diploma in Electrical and Computer Engineering from NTUA (1999) M.Sc. in Computer Sciences from University of Maryland (2002) Ph.D. in Computer Sciences from University of Maryland (2006) Professor Tsoumakos' research focuses on the intersection of big data management, cloud computing, and distributed systems. His work addresses fundamental challenges in large-scale data processing, including wavelet synopses for data summarization, vector embedding frameworks for analytics operators, multi-engine analytics systems, and cloud application deployment with failure recovery mechanisms. His research consistently bridges theoretical algorithms with practical implementations for real-world data challenges. His publication record demonstrates consistent innovation from early work on P2P data management systems through to current research on vector embeddings and deep reinforcement learning for cloud autoscaling. Recent work shows a clear progression toward content-based analytics, multi-dataset integration, and intelligent resource management in heterogeneous environments. Best Paper Runner Up Award at SSDBM 2019 Best Paper Award at CCGrid 2013 Professor Tsoumakos has secured substantial research funding through multiple European projects including RELAX (2023-2027), HiDALGO2 (2023-2026), DAPHNE (2021-2024), and previous initiatives like HiDALGO, TraMOOC, ASAP, CELAR, ARCOMEM, and GREDIA. These projects reflect his leadership in big data analytics, cloud computing, and distributed systems research. As director of DBLab, Professor Tsoumakos oversees research on the Knowledge and Database Systems Laboratory, which has produced significant work on analytics operators, multi-engine resource scheduling (IReS platform), cloud elasticity (TIRAMOLA), and RDF data management (H2RDF+). The lab maintains strong industry connections and has developed multiple open-source tools for big data analytics.

Zhenjiang Huمشاهده پروفایل

استاد

• Programming Languages
• Software Engineering
• Functional Programming
• +۷ مورد دیگر

Zhenjiang Hu is a Chair Professor and Dean of the School of Computer Science at Peking University. He serves as Director of the Programming Languages Laboratory and has held significant academic positions including Professor at the National Institute of Informatics and University of Tokyo. BS and MS from Shanghai Jiaotong University (1988, 1991) PhD from University of Tokyo (1996) Lecturer/Assistant Professor at University of Tokyo (1997) Associate Professor at University of Tokyo (2000) Full Professor at National Institute of Informatics (2008) Full Professor at University of Tokyo (2018-2019) Professor Hu's research primarily focuses on programming languages and software engineering, with special emphasis on functional programming, bidirectional transformation, and software adaptation. His work explores transformational programming approaches for automatic program optimization, systematic parallelization of sequential programs, efficient manipulation of structured documents, and bidirectional model transformation for software development. His research has significantly advanced the field of bidirectional programming, developing foundational theories and practical applications that enable more reliable and maintainable software systems. His recent publications demonstrate a strong trajectory in bidirectional programming, program synthesis, and graph processing. The research shows increasing sophistication in handling program transformations, with growing emphasis on practical applications in software engineering contexts. His work increasingly integrates formal methods with practical programming language design, creating systems that maintain theoretical soundness while addressing real-world software development challenges. The research spans multiple venues including top conferences like PLDI, POPL, ICFP, and OOPSLA, reflecting its broad impact across programming language research. Fellow of JFES (Japan Federation of Engineering Society, 2016) ACM Distinguished Scientist (2016) Member of Academia Europaea (2019) IEEE Fellow (2020) Member of Engineering Academy of Japan (2020) Professor Hu actively mentors students and has welcomed excellent candidates to join his group through Peking University's International Elite PhD Program and Boya Postdoctoral Fellowship Program. He serves on numerous program committees for major conferences including PLDI, POPL, ICFP, and OOPSLA, and holds editorial positions for prestigious journals such as Journal of Functional Programming and Science of Computer Programming. His leadership extends to conference organization, having served as PC Chair for CNCC 2024 and General Co-Chair for SoICT 2019. As Director of the Programming Languages Laboratory at Peking University, Professor Hu leads a research team focused on advancing programming language theory and practice. His lab has developed influential frameworks like BiGUL for bidirectional programming and Fregel for graph processing. The laboratory maintains strong international collaborations and contributes to both theoretical foundations and practical implementations in programming languages and software engineering.

• Algorithms
• Complexity Theory
• Optimization
• +۵ مورد دیگر

Ioannis Milis is a Professor at the Department of Informatics, Athens University of Economics and Business (AUEB), part of the School of Information Sciences and Technology. He holds a BS in Electrical Engineering from Democritus University of Thrace (1983) and a PhD in Computer Science from AUEB (1989). His research focuses on algorithms, computational complexity, and optimization for computer/communication networks, combinatorial optimization, graph theory, and game theory. He has conducted postdoctoral research at LRI (1992-94), INRIA-Sophia Antipolis (1994-95), and NTUA as a Marie Curie fellow (1995-96). His teaching includes courses on Algorithms, Advanced Algorithms, and Topics in Algorithms at both undergraduate and graduate levels. He co-authored a textbook on Distributed Systems with Java (2005). His conference involvement includes organizing the Athens Colloquium on Algorithms and Complexity (ACAC) since 2006, the Euro-Par 2012 conference, and the ISCO 2012 symposium. His research has addressed scheduling algorithms, energy-efficient computing, and combinatorial optimization problems in networks.

Michael Eichbergمشاهده پروفایل

استاد

• Software Engineering
• Static Analysis
• Software Development Tools
• +۳ مورد دیگر

Michael Eichberg is a Professor at Technische Universität Darmstadt, Germany, where his work centers on software engineering, static analysis, programming languages, and secure software development tools. He is the principal architect of the OPAL framework for Java bytecode analysis and has an extensive publication record spanning PLDI, ICSE, ESEC/FSE, ISSTA, ASE, FSE, SOAP, and other premier venues. Research Interests: Static program analysis and its scalability to real-world code bases Software security, particularly cryptographic API misuse and Android app repackaging detection Concurrent and parallel programming models, including deterministic concurrency in Scala Software architecture conformance, drift and erosion detection, and rule reuse Development of open extensible tools and frameworks (OPAL, LectureDoc, QScope, Sextant, XIRC, IRC) Publication Trends: His recent work (2015-2022) demonstrates a strong focus on empirical evaluation of static analysis techniques, modular composition of analyses, and security-related program understanding. Key themes include unsoundness in call graph construction, purity and immutability analyses, parallelization of static analyses, and large-scale studies of cryptographic API misuse. Tools & Frameworks: OPAL – A flexible Java bytecode analysis and manipulation framework (core developer until 2019) LectureDoc 2 – Web-based lecture material authoring and presentation system QScope – Open extensible metrics framework for modern software projects Sextant – Eclipse-integrated software exploration tool XIRC/IRC – Frameworks for enforcing system-wide properties and architectural constraints

• Programming Languages
• Static Analysis
• Abstract Interpretation
• +۶ مورد دیگر

Florian Kübler is a researcher at Technische Universitat Darmstadt, Germany, specializing in Programming Languages and Static Analysis . He has contributed to major conferences including PLDI, ISSTA, ESEC/FSE, and SOAP, focusing on topics like abstract interpretation, call graph construction, and modular program analysis. His work addresses challenges in code optimization, soundness evaluation, and runtime reusability in static analysis frameworks. Research Interests: Florian's research spans Abstract Interpretation for static analysis Modularization techniques in program analysis Call graph algorithms for Java Parallelization of static analyses Intermediate representation design Compiler and toolchain optimization Conference Contributions: His publications highlight expertise in static analysis frameworks (OPAL, SootKeeper), semi-implicit parallelization, and systematic evaluation of analysis soundness. Recent work (2022) explores collaborative program analysis, while earlier studies (2018-2020) focus on lattice-based modularization and call graph algorithms.