جستجوی استاد راهنمای دانشگاهی

Angelos Bilas is a Professor in the Department of Computer Science at the University of Crete and a collaborating researcher at FORTH-ICS. He holds a B.Eng. from the University of Patras (1993), and M.A. and Ph.D. from Princeton University (1995, 1998). His research focuses on computer systems, storage systems, and computer architecture, with recent emphasis on optimizing memory management and storage efficiency. Bilas has held roles such as Chair of the Department of Computer Science (2016–2020) and coordinated the FP7 EU project IOLanes (2010–2013). He serves on editorial boards, including ACM Transactions on Storage since 2016. His work has been recognized with awards like the Marie Curie Excellent Teams Award (2005–2009) and patents in storage and networking. Current affiliations: University of Crete (Professor), FORTH-ICS (Researcher) Educations: Ph.D. (Princeton, 1998), M.A. (Princeton, 1995), B.Eng. (Patras, 1993) His research interests span storage systems, parallel architectures, and runtime systems. Recent work includes TeraHeap (ASPLOS'23) for big data frameworks and Tebis (EuroSys'22) for LSM key-value stores. His publications emphasize optimization in memory-mapped I/O, storage efficiency, and heterogeneous acceleration. Awards include the Marie Curie Excellent Teams Award and four patents. He has supervised 35+ master's and 8+ Ph.D. students, contributing to over 45 EU/nationally funded projects. Current initiatives include the EVOLVE H2020 project (2019–2021) for HPC and big data integration. Labs/Teams: Active in FORTH-ICS and collaborations with industry on storage and cloud-HPC convergence.

• Computer Systems
• Computer Architecture
• Storage Systems
• +۲ مورد دیگر

Dr. George Xylomenos is a Professor at the Department of Informatics of Athens University of Economics and Business (AUEB), affiliated with the Mobile Multimedia Laboratory (MMLab). He obtained his Diploma in Informatics from AUEB (1993), followed by M.S. and Ph.D. degrees in Computer Science from UC San Diego (1996, 1999). His research focuses on Future Internet design, Quality of Service, wireless networks, and multimedia communication protocols. He has led numerous national and European projects, including roles as Deputy Rector for Financial Planning (2017–2020) and Deputy Chair of the Department of Informatics (2021–2024). He has published over 100 articles with >4000 citations and received awards such as the Future Internet Award (2013) and Best Demo Award (ACM ICN 2017). Education: B.S., Informatics, AUEB (1993) M.S. & Ph.D., Computer Science, UC San Diego (1996–1999) Research Interests: Future Internet architectures, network performance optimization, wireless protocols, QoS, and multimedia systems. His work explores latency-critical applications like Network Music Performance and secure data management in distributed systems. Key Projects: Leader of projects AViD-NMP, SeEDS, and TENeMP (2020–present) Co-investigator in FP7 PURSUIT and H2020 SOFIE Grants & Awards: Over 4000 citations across 100+ publications Editorial roles: IEEE Transactions on Mobile Computing Best Paper Awards: ACM ICN 2017, IEEE EWS 2007 Labs & Teams: Mobile Multimedia Lab (MMLab) at AUEB, collaborating on ICN, IoT, and network music performance systems.

• Future Internet design
• Quality of Service provisioning
• Wireless and mobile networks
• +۳ مورد دیگر

Alexander Artikis is an Associate Professor at the Department of Maritime Studies, University of Piraeus. His academic work bridges artificial intelligence, distributed systems, and maritime informatics, with a focus on complex event recognition and temporal reasoning. PhD in Multi-Agent Systems from Imperial College London Research areas include: Artificial Intelligence and Machine Learning Distributed and Real-Time Systems Event Calculus and Temporal Reasoning Maritime Data Analytics Recent publications highlight advancements in: Knowledge Graph Inconsistency Detection and Resolution Tensor-Based and Symbolic Register Transducer Formalisms Human-in-the-Loop Visual Analytics Temporal Specification Optimization Large Language Model Applications in Event Recognition Scalable Event Processing for Maritime Mobility He contributes to the academic community through program committee roles at premier conferences like AAMAS, IJCAI, and AAAI.

• Artificial Intelligence
• Distributed Systems
• Multi-Agent Systems
• +۳ مورد دیگر

Kotidis Ioannis serves as an Associate Professor in the Department of Informatics at the Athens University of Economics and Business (AUEB). His academic journey began with a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, followed by Master's and Ph.D. degrees from the University of Maryland. His research focuses on several critical areas of database systems and data management, including On Line Analytical Processing (OLAP) and data warehousing, data mining, Sensor/P2P networks, mobile data management, data fusion & dissemination, data streams, RFID data management, approximate query answering, ranking, and database preservation and integration. His work bridges theoretical database concepts with practical applications in emerging technologies. Professor Kotidis' publication record demonstrates consistent contributions to database research over two decades, with notable work in dynamic view management, stream processing, sensor network data management, and more recently, blockchain-based OLAP view management and complex event processing. His research has evolved from traditional data warehousing to address challenges in modern data-intensive applications. best paper award at ACM SIGMOD International Conference on Management of Data for DynaMat paper Throughout his career, Professor Kotidis has supervised numerous students on projects related to database systems, including work on complex event processing, decentralized OLAP view management using blockchains, and graph similarity learning. His research projects include DeLorean (time-series management), RECOST (complex stream management), DBSENSE (wireless sensor networks), and INFORE (extreme-scale analytics). His laboratory work focuses on creating efficient, scalable solutions for data management challenges across various domains, with particular emphasis on making advanced data processing techniques accessible and practical for real-world applications.

• OLAP and data warehousing
• data mining
• Sensor/P2P networks
• +۸ مورد دیگر

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

• Data Mining
• Spatiotemporal Databases
• Mobility Data Analysis
• +۲ مورد دیگر

Rajiv Gupta is a Distinguished Professor and the Amrik Singh Poonian Professor of Computer Science at the University of California, Riverside (UCR), where he serves as Associate Dean for Academic Personnel in the Bourns College of Engineering (BCOE). He is a member of the RIPLE research group and has co-authored 327 papers with an h-index of 69 and over 16,600 citations. His extensive service includes chairing major conferences such as FCRC 2015, PPoPP 2020, ASPLOS 2011, and PLDI 2008. Professor Gupta's research focuses on Programming, Compiler, Runtime & Architectural Support for Parallel & Distributed Heterogeneous Systems and Software Tools for Monitoring and Managing Runtime Behavior . His work spans graph analytics with scalability and performance, understanding and managing the dynamic behavior of parallel programs, software speculation for irregular parallelism, dynamic program analysis for secure and reliable computing, and compiler optimizations with architectural support. His research has significant applications in high-performance computing, GPU programming, and distributed systems. Analysis of his recent publications reveals a strong focus on graph processing systems, with particular emphasis on evolving and streaming graph analytics. His work addresses critical challenges in memory management for large-scale graph processing, hardware acceleration for graph algorithms, and optimization techniques for concurrent and distributed graph computations. The research demonstrates a progression from foundational compiler and architecture work to increasingly sophisticated systems for handling modern data-intensive computing challenges. Fellow of the ACM (2009) Fellow of the IEEE (2008) Fellow of the AAAS (2011) NSF Presidential Young Investigator Award (1991) UCR Doctoral Dissertation Advisor/Mentor Award (2012) Multiple best paper awards across major conferences Two students won ACM SIGPLAN Outstanding Doctoral Dissertation Award Five advisees received NSF CAREER Award Professor Gupta has supervised 42 PhD students to completion and currently advises several doctoral candidates. His advising success is reflected in his students' achievements, including multiple award-winning dissertations and significant career accomplishments in academia and industry. His research has been supported by numerous grants from NSF, DARPA, and industry partners, enabling sustained investigation into parallel computing systems. The RIPLE research group under his leadership has produced influential work that bridges theoretical foundations with practical system implementations. As the leader of the RIPLE research group at UC Riverside, Professor Gupta oversees a vibrant team focused on innovative approaches to parallel and distributed computing. The group maintains strong collaborations with industry partners and other academic institutions, contributing to the development of next-generation computing systems. Current projects include GRASP (Graph Analytics with Scalability & Performance) and research on understanding and managing the dynamic behavior of parallel programs, reflecting the group's continued focus on cutting-edge computing challenges.

• Parallel Computing
• Compilers
• Computer Architectures
• +۴ مورد دیگر

Kunle Olukotun is a Professor of Electrical Engineering and Computer Science at Stanford University's School of Engineering, where he has been faculty since 1991. He directs the Stanford Pervasive Parallelism Lab (PPL) and co-leads the Transactional Coherence and Consistency (TCC) project. His research focuses on computer architecture, parallel programming environments, and scalable parallel systems. Key areas include chip multiprocessors (CMPs), transactional memory systems, domain-specific languages (DSLs) for heterogeneous computing, and hardware-software co-design for machine learning workloads. His work bridges theoretical foundations with practical systems implementation. Notable contributions include the Stanford Hydra research project (one of the first chip multiprocessors with thread-level speculation), founding Afara Websystems (acquired by Sun Microsystems), and developing the Niagara processor architecture. His DSL frameworks like Green-Marl and Spatial enable efficient graph analysis and hardware acceleration. His publications reveal strong trends in parallel systems evolution: from foundational CMP research (2000s) to transactional memory (2004-2010), then DSLs for heterogeneous computing (2010-2015), and currently foundation model systems (2023-2025). Subfield analysis shows consistent focus on hardware-software co-design, sparse computation, and compiler techniques across decades. ACM Fellow (2006) for contributions to multiprocessors on a chip and multi-threaded processor design Best Paper Award at IEEE International Symposium on Workload Characteristics (IISWC '10) for EigenBench Olukotun actively mentors researchers through the Stanford Pervasive Parallelism Lab (PPL), which seeks to proliferate parallelism across application domains. His projects have secured significant industry partnerships, including the acquisition of his startup Afara Websystems by Sun Microsystems. Current research focuses on compiler frameworks for foundation model systems and hardware acceleration for sparse machine learning workloads, supported by collaborations with major tech companies. He leads the Stanford Pervasive Parallelism Lab (PPL), which develops compiler and runtime systems for heterogeneous architectures. The lab's work spans DSLs, hardware acceleration, and parallel programming models, with strong industry ties to companies like NVIDIA and Google. Current initiatives include the Mosaic compiler framework and Stardust architecture for sparse tensor computation.

• Computer Architecture
• Parallel Programming
• Chip Multiprocessors
• +۴ مورد دیگر

Maria Halkidi is an Associate Professor in the Department of Digital Systems at the University of Piraeus, Greece, where she conducts cutting-edge research in data mining and machine learning with over two decades of academic experience. Her educational background includes: Bachelor's degree in Informatics from the University of Piraeus (1997) Master's degree from Athens University of Economics and Business (1999) Ph.D. from Athens University of Economics and Business (2003) Dr. Halkidi's research focuses on fundamental and applied aspects of data mining, particularly recommender systems, graph data mining, cluster validity assessment, and distributed data mining. Her work bridges theoretical frameworks with practical implementations in sensor networks, social media, and real-time analytics, contributing significantly to algorithmic development in these domains. Analysis of her recent publications reveals a strong trajectory toward fairness and diversity optimization in recommender systems, alongside innovative approaches to graph clustering quality assessment and scalable sentiment analysis. Her research consistently addresses real-world challenges in dynamic data environments, with increasing emphasis on multi-stakeholder optimization and privacy-aware recommendation frameworks. Scientific awards: No specific awards or honors were documented in the provided materials. Dr. Halkidi has participated extensively in National and European-funded research projects, including a prestigious Marie-Curie fellowship at the University of California, Riverside. She serves on program committees for major international conferences in data mining and machine learning, demonstrating active community engagement. While specific graduate students aren't listed in the source materials, her professorial role indicates ongoing mentorship of Master's and Ph.D. candidates. She maintains active research affiliations with the Network oriented systems & services lab and the DataStories research group at the University of Piraeus, where she collaborates on interdisciplinary projects involving big data analytics, social network analysis, and distributed systems.

• Data Mining
• Machine Learning
• Recommender Systems
• +۶ مورد دیگر

Dimitrios Karapiperis serves as an Academic Scholar at the School of Science and Technology, International Hellenic University (IHU), specializing in Entity Resolution and Privacy-Preserving Record Linkage. Previously, he held a post-doctoral position at the Hellenic Open University. He earned his PhD from the Hellenic Open University and his MSc from the University of York (UK). His doctoral thesis was featured in the IEEE Intelligent Informatics Bulletin of August 2017, highlighting its significance in the field. Dr. Karapiperis' research centers on developing advanced algorithms for entity resolution, including similarity measures, data structures, and scalable distributed solutions using randomization techniques. His work extends to privacy-preserving methods for record linkage with applications in electronic health records, cryptocurrency analysis, and social media sentiment. He has made significant contributions to efficient record linkage in data streams and spatio-temporal data through innovative blocking techniques and approximation schemes. His recent publications (2020-2022) reveal a consistent focus on scalable and privacy-aware record linkage, with increasing applications in financial technology and affective computing. Collaborations with prominent researchers like V.S. Verykios have resulted in numerous publications in top venues including IEEE Big Data and IEEE TIFS, demonstrating expertise in both theoretical foundations and practical implementations. Scientific Recognition Doctoral thesis featured in IEEE Intelligent Informatics Bulletin (2017) Research Projects University of York: Development of Java servlets for converting VisioXML into GSML within the High Integrity Systems Engineering research group University of Macedonia: Standardization of distance learning systems University of Macedonia: Establishment of a data bank for the fur sector in Kastoria University of Macedonia: System for organizing business processes of the Ministry of Macedonia and Thrace Dr. Karapiperis has collaborated with research teams across multiple institutions, contributing to diverse projects from healthcare data integration to government business process optimization, while maintaining active research in scalable entity resolution methodologies.

• Entity Resolution
• Record Linkage
• Privacy-Preserving Record Linkage
• +۳ مورد دیگر