جستجوی استاد راهنمای دانشگاهی

• Management Information Systems
• Workflow Management
• Complex Event Processing
• +۷ مورد دیگر

Dr. Yiannis Verginadis is an Assistant Professor in Management Information Systems at the Department of Business Administration, Athens University of Economics and Business (AUEB). With over 15 years of experience in ICT projects, his research focuses on management of information systems workflow management complex event processing cloud computing and security adaptive systems context-aware access control He holds a Diploma and Ph.D. in Electrical and Computer Engineering from the National Technical University of Athens, specializing in inter-organizational workflow systems for e-Government. His career includes adjunct lecturer roles at the University of Thessaly and Technological Educational Institute of Kalamata. Research trends from his publications include: Cloud continuum brokerage and decentralized data management (2023-2025) Attribute-based access control for healthcare data (2021-2024) Workflow optimization in multi-cloud environments (2016-2023) Federated learning applications (2022-2024) Event-driven architectures and ontological modeling (2009-2021) Security-by-design frameworks (2013-2021) Dr. Verginadis has authored a book on Information Management Systems and contributed to works on e-Participation, adaptive web services, and ambient assisted living systems. He has served on over 25 journal and conference review committees and published 60+ peer-reviewed papers.

• Internet of Things
• Edge Computing
• Distributed Systems
• +۷ مورد دیگر

Andreas Menykhtas serves as an Assistant Professor at the Department of Digital Systems within the School of Information and Communication Technologies at the University of Piraeus. His academic journey began at the National Technical University of Athens where he completed his undergraduate studies in Electrical and Computer Engineering in 2004, followed by postgraduate and doctoral studies at the same institution, earning his PhD in Engineering in 2009 with a specialization in service-oriented infrastructure. His research spans the evolving landscape of modern information systems infrastructure, with particular emphasis on Internet of Things, Edge Computing, distributed systems, and cloud computing environments. Dr. Menykhtas has developed expertise in software development, artificial intelligence, machine learning applications, smart cities infrastructure, and e-health systems. His scholarly contributions include over 100 publications in scientific journals, international conferences, and book chapters that have collectively garnered more than 1,900 citations. His publication record reveals a strong trajectory in distributed computing architectures, with recent work focusing on the integration of AI techniques at the network edge, optimization of resource allocation in multi-tenant environments, and development of service-oriented frameworks for smart city applications. The interdisciplinary nature of his research bridges computer science, engineering, and practical applications in urban and healthcare contexts. Dr. Menykhtas has extensive teaching experience across multiple Greek institutions, having delivered undergraduate and graduate courses on distributed computing, programming, and information systems development at the University of West Attica, the Hellenic Open University, and the University of Piraeus. His research activities include participation as scientific manager, researcher, and engineer in numerous European and national research projects.

Sriram Krishnamoorthyمشاهده پروفایل

استاد پژوهش

• Parallel Programming Models
• Fault Tolerance
• High-Performance Computing
• +۶ مورد دیگر

Sriram Krishnamoorthy is a Research Professor at Washington State University's School of Electrical Engineering & Computer Science and a research scientist at Pacific Northwest National Laboratory (PNNL), where he serves as the System Software and Applications Team Leader in PNNL's High Performance Computing group. Dr. Krishnamoorthy earned his B.E. from the College of Engineering, Guindy in Chennai, India, and his M.S. and Ph.D. degrees from The Ohio State University. He is a senior member of the Institute of Electrical and Electronics Engineers. His research focuses on parallel programming models, fault tolerance, and compile-time/runtime optimizations for high-performance computing. He has made significant contributions in areas including: Fault tolerance techniques that minimize rollback during failures Dynamic load balancing for irregular parallel applications Compiler and runtime optimizations for HPC applications GPU programming and heterogeneous computing Quantum chemistry simulations and quantum computing Dr. Krishnamoorthy's publications span computational science, high-performance computing, and quantum chemistry. His recent work shows strong trends toward quantum computing applications, fault tolerance in large-scale systems, and optimization of computational chemistry methods. He has developed techniques for density matrix quantum circuit simulation, floating-point error analysis, and scalable execution of coupled-cluster models. His scientific achievements have been recognized with several prestigious awards: Best Paper Award at International Conference on High Performance Computing (HiPC'03) Best Paper Award at International Parallel and Distributed Processing Symposium (IPDPS'04) U.S. Department of Energy Early Career award (2013) PNNL's Ronald L. Brodzinski Award for Early Career Exceptional Achievement (2013) The Ohio State University's Outstanding Researcher award (2008) Dr. Krishnamoorthy has advised numerous graduate students and collaborated extensively with researchers across computational science domains. His work on the NWChem project demonstrates significant grant funding and large-scale collaborative research efforts in computational chemistry. He leads research efforts in PNNL's High Performance Computing group, focusing on system software and applications development for next-generation supercomputing platforms.

• Real-time computer resource management systems
• Heterogeneous memory systems
• Programming models for heterogeneous computing systems
• +۲ مورد دیگر

Lazaros Papadopoulos serves as an Assistant Professor in the Department of Electrical and Computer Engineering at Democritus University of Thrace since May 2024, focusing on computational resource management for embedded and high-performance systems. His work bridges hardware-software co-design with energy-efficient computing paradigms. His academic credentials include: Bachelor's in Electrical and Computer Engineering from Democritus University of Thrace (2005) Master's degree from the same institution (2008) PhD from the National Technical University of Athens (2016) Research centers on optimizing computing infrastructure through real-time resource management, heterogeneous memory architectures, and AI workload acceleration. His investigations target energy-performance tradeoffs in GPU systems, persistent memory integration, and edge-device neural network deployment, with direct applications in sustainable computing and high-performance data processing. Publication analysis (2018-2022) reveals consistent focus on heterogeneous system optimization, particularly energy-aware GPU acceleration frameworks, memory management for DRAM/NVM hybrids, and efficient CNN implementations for edge devices. Key thematic threads include sustainable computing practices, hardware-aware AI deployment, and memory hierarchy innovations. Scientific recognition includes: Two Best Paper Awards Research leadership spans major EU initiatives: Work Package Coordinator for LAGO (Horizon Europe, 2022-2024) and PRAETORIAN (H2020, 2022-2024); Technical Coordinator for EXA2PRO (H2020, 2018-2021); and Work Package Coordinator roles in SDK4ED (H2020, 2018-2020) and EXCESS (H2020, 2013-2016). These projects address embedded systems security, exascale programming models, and energy-efficient computing toolchains. He operates within the Computer Architecture and High Performance Systems laboratory, directing research on computational resource orchestration for next-generation heterogeneous platforms.

• Numerical Analysis
• Biomedical Modeling
• Applied Mathematics
• +۳ مورد دیگر

Marianna Papadomanolaki is a Researcher at the Applied Mathematics and Computers Laboratory within the School of Production Engineering and Management at the Technical University of Crete. She holds a BSc in Mathematics from the University of Crete, and both an MSc and PhD in Applied and Computational Mathematics from the Technical University of Crete. Her research focuses on Numerical Analysis, Biomedical Modeling, and Mathematical Biology, with notable contributions to tumor growth simulations and Vitamin D deficiency studies. Her work spans interdisciplinary areas including biomedical modeling of cancer tumors, numerical linear algebra techniques, and applications in health sciences. Key contributions include developing the Fokas transform method for tumor invasion models and investigating Vitamin D's role in thyroid disorders. She has published extensively in journals and conference proceedings since 2001, addressing topics ranging from mathematical modeling to clinical studies on thyroid diseases and nutritional interventions. Her research emphasizes computational methods for biomedical challenges, with a strong record of peer-reviewed publications and participation in national/international research programs. No scientific awards are explicitly noted in the provided materials. Marianna has been affiliated with the Technical University of Crete since 2001, contributing to both research and teaching activities within the Applied Mathematics Laboratory. She has collaborated on projects involving Spirulina supplementation's effects on metabolic health and thyroid dysfunction, demonstrating a commitment to bridging mathematical rigor with clinical applications.

Pavlos Petoumenosمشاهده پروفایل

استادیار

• Compiler Optimization
• Runtime Systems
• Energy Efficient Computing
• +۶ مورد دیگر

Pavlos Petoumenos is a Lecturer (Assistant Professor) in the Department of Computer Science at the University of Manchester and a Research Fellow of the Royal Academy of Engineering. He leads research within the Advanced Processor Technology (APT) group, focusing on compiler optimization and energy-efficient computing. Previously, he worked as a Research Associate and Senior Researcher at the University of Edinburgh under Hugh Leather. His research centers on compilers, runtime systems, and development tools designed to help programmers write fast, energy-efficient programs with minimal effort. With electronic systems consuming 10% of global electricity (projected to reach 20% by 2030), his work addresses the critical challenge of bridging the complexity gap between programmers and modern energy-efficient hardware. Key research areas include compiler optimization, machine learning applications in compilation, code size reduction, and quantum-classical programming integration. His recent publications reveal strong trends in applying machine learning to compiler optimization, particularly through active learning techniques and deep learning models. The research spans multiple domains including function merging, branch fusion, loop transformations, and energy profiling, with significant contributions to quantum programming language design. His work consistently targets real-world applications in multicore processors and interactive mobile environments. Award highlights include: Best Paper Award at CGO 2019 Distinguished Paper Award at ISSTA 2018 Best Paper Award at PACT 2017 Best Paper Award at CGO 2017 Best Paper Award at IISWC 2014 Royal Academy of Engineering Research Fellowship Petoumenos actively contributes to the research community through mentoring PhD students, organizing the biennial International Workshop on Code Optimisation for Multi and Many-Cores (COSMIC), and co-hosting the Compucast podcast where he serves as chief editor. His collaborations span multiple institutions including the University of Edinburgh, University of Lancaster, and University of St Andrews. He has secured significant research impact through tools like BenchPress and F3M that address practical challenges in compiler design and optimization. He leads the Advanced Processor Technology group and contributes to the Compucast podcast, which features interviews with computer science researchers, academic and industry news, and technical discussions. The podcast represents a collaboration between multiple UK universities and serves as an important outreach platform for computer science research.

Yiannis Saridakisمشاهده پروفایل

استاد

• Applied Mathematics
• Scientific Computing
• Parallel Algorithms
• +۸ مورد دیگر

Professor Yiannis Saridakis serves as a Professor in the Department of Sciences at the Technical University of Crete, leading the Applied Mathematics and Computers Laboratory. He holds a Mathematics degree from the University of Crete (1981) and M.Sc./Ph.D. in Applied Mathematics & Computer Science from Clarkson University (1983/1985). His research focuses on numerical methods, parallel algorithms, stochastic optimization, cellular automata, and AI applications. He has coordinated projects like the TU Crete Library portal and digitization initiatives. His work bridges computational mathematics with environmental, biomedical, and engineering challenges. Education B.Sc. Mathematics, University of Crete (1981) M.Sc. Applied Mathematics & Computer Science, Clarkson University (1983) Ph.D. Applied Mathematics & Computer Science, Clarkson University (1985) Research Interests His research spans Recursive Linear Systems Solving , Numerical Methods for PDEs , Parallel Computing Architectures , Stochastic Optimization (e.g., adaptive optics, aquifer management), Cellular Automata , and Artificial Intelligence applications like agent-based systems and ambient intelligence. Key Contributions Prof. Saridakis pioneered GPU-accelerated Hermite collocation methods, Fokas transform applications in tumor modeling, and ALOPEX stochastic optimization for water resource management. He has published >50 papers in top journals and conference proceedings. Labs & Leadership Director of the Applied Mathematics and Computers Laboratory . Former Department Chair (1997–1999) and Vice-Rector (1999–2005) at TU Crete.

Nikolaos M. Missirlisمشاهده پروفایل

استاد

• Scientific Computing
• Parallel Computing
• Numerical Analysis
• +۲ مورد دیگر

Nikolaos M. Missirlis is a Professor at the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens (NKUA) since 1995. He earned his BSc in Applied Mathematics from the University of Ioannina, Greece in 1974 and his PhD in Numerical Analysis from Loughborough University of Technology, UK in 1978. He has held visiting positions at Rutgers University (1986-1987), Cyprus University (2006-2008), and SUNY Stony Brook (2013). His research focuses on Scientific Computing , particularly parallel solution of Partial Differential Equations and load balancing algorithms. His work bridges theoretical mathematics with high-performance computing applications in meteorology and fluid dynamics. His publications demonstrate consistent output in parallel algorithms and numerical methods, with recent work emphasizing GPU acceleration and diffusion-based load balancing techniques. Research trends show evolution from foundational iterative methods to applications in heterogeneous computing environments. Teaching activities include undergraduate courses: Numerical Analysis Numerical Linear Algebra Algorithms and Complexity Introduction to Programming Data Structures Graduate courses: Scientific Computing (Numerical Solution of PDEs) Parallel Algorithms He has advised five PhD students and led major projects including: PYTHAGORAS (Distributed iterative methods for weather prediction) SKIRON (Parallelization of Eta model for Hellenic Meteorology Service) Princeton Ocean Model parallelization Contact: nmis@di.uoa.gr | +30 210 7275103 | Department of Informatics and Telecommunications, NKUA, Panepistimiopolis 157 84, Athens, Greece.

Giorgio Lucarelliمشاهده پروفایل

مدرس

• Algorithms and Complexity
• Approximation and Online Algorithms
• Power-Aware Scheduling
• +۳ مورد دیگر

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.

• Computer Networks
• Telematics
• Multimedia Transmission
• +۵ مورد دیگر

Dr. Gkamas Apostolos is an Associate Professor of Computer Applications in the Department of Chemistry at the University of Ioannina, Greece. His research focuses on cutting-edge topics in computer networks, telematics, IoT, and cross-layer design, with a particular emphasis on multimedia transmission optimization and 5G MIMO systems. He holds a Diploma, Master’s Degree, and Ph.D. in Computer Engineering and Informatics from the University of Patras, Greece. Dr. Gkamas has contributed over 130 publications in international journals and conferences, co-authored three books on computer networks and specialized network topics, and actively participated in EU-funded projects such as FP6, FP7, and Intereg eLearning initiatives. His work spans energy efficiency in 5G networks, adaptive beamforming techniques, and latency reduction strategies for IoT applications. Recent studies include groundbreaking research on copolymer architecture for drug delivery systems and simulation-based optimization of network slicing in 5G environments. His research interests also extend to machine learning applications in network resource allocation, chatbot technology assessment (with 40 case studies in Greece), and agent-based energy-saving models for large ships. He has pioneered systems like GuideMe for indoor navigation and long-range IoT search-and-rescue solutions using LoRaWAN technology. Dr. Gkamas has collaborated on projects such as the EU's IST initiative and Greek national programs like PENED and EPEAEK, demonstrating a strong commitment to both academic research and applied technology development. His work bridges theoretical advancements with practical implementations in telecommunications and materials science.

• Pollutant transport in porous media
• Environmental systems modeling
• Biocolloid transport
• +۵ مورد دیگر

Konstantinos Chrysikopoulos is a Professor at the Technical University of Crete's School of Chemical and Environmental Engineering, leading the Environmental Technology Laboratory (TUCeeL). His research focuses on contaminant transport in porous media, biocolloid dynamics, and innovative remediation technologies. Ph.D., Civil and Environmental Engineering, Stanford University (1991) Engineer Degree, Civil Engineering (Geothermal Program), Stanford University (1986) M.Sc., Chemical Engineering, Stanford University (1984) B.Sc., Chemical Engineering, UC San Diego (1982) His experimental and theoretical work covers: biocolloid transport in groundwater, nanoparticle-soil interactions, solar-powered wastewater treatment, and acoustic wave soil remediation. Recent publications analyze graphene oxide nanoparticle behavior and colloid transport in fractured systems. Research trends include: biocolloid-aggregation dynamics, environmental nanotoxicology, reactive contaminant transport modeling, and coupled physical-chemical-biological processes in subsurface systems. His work bridges environmental engineering with computational hydrology and risk assessment. UCOWR Best Dissertation Award (Advisor for Youn Sim, 1998) UCOWR Best Dissertation Award (Advisor for Assem Abdel-Salam, 1996) As a Principal Investigator, he has coordinated major projects like ARISTEIA I (BioNFate, €500,000) and HERACLITOS II (€45,000). His team at TUCeeL develops technologies for groundwater protection and pollution mitigation.

Bradford L. Chamberlainمشاهده پروفایل

استاد مدعو

• Parallel Computing
• Programming Languages
• High-Performance Computing
• +۳ مورد دیگر

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.

Santosh Pandeمشاهده پروفایل

استاد

• Program Analysis
• Software Security
• Compiler Optimizations
• +۳ مورد دیگر

Santosh Pande is a Professor and Associate Chair for Graduate Studies in the School of Computer Science at Georgia Institute of Technology. His research focuses on compiler analysis and optimizations, providing deep insights into software behavior to improve various system properties through automated methods. His research interests include: Program Analysis for Security Compiler Optimizations and security implications Compiler-OS interactions for resource management Real-time software analysis Machine learning systems performance optimization Dr. Pande's recent publications demonstrate a strong focus on security-oriented compiler techniques, particularly in program debloating to reduce attack surfaces. His work addresses the critical intersection between compiler optimizations and security vulnerabilities, as well as innovative approaches to scheduling and resource allocation through compiler-OS collaboration. Research results show significant performance improvements (2x to 4x) for batch-oriented execution of modern workloads in data-center environments. His research has been consistently funded by: National Science Foundation (NSF) Office of Naval Research (ONR) Defense Advanced Research Projects Agency (DARPA) Air Force Industry partners including Sony, Toshiba, IBM, Motorola, and Infineon Dr. Pande holds a patent on smartcards developed in collaboration with Infineon. He actively mentors PhD students and seeks "smart and motivated PhD students" to join his research group. His work has resulted in over 100 publications and open-source software contributions. He is affiliated with multiple research centers at Georgia Tech including the Center for Experimental Research in Computer Systems (CERCS) and contributes to the Online Master of Science in Computer Science (OMSCS) program.

Saday Sadayappanمشاهده پروفایل

استاد

• Compiler Optimization
• High-Performance Computing
• Sparse/Dense Matrix Computations
• +۳ مورد دیگر

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.

Hui Liمشاهده پروفایل

دانشیار

• Information Retrieval
• Data Mining
• Data Management
• +۸ مورد دیگر

Hui Li is an Associate Professor in the Department of Computer Science and Technology at the School of Informatics, Xiamen University. He is affiliated with the Media Analytics & Computing Laboratory (MAC Lab) and holds a Ph.D. in Computer Science from the University of Hong Kong. Ph.D., Computer Science, University of Hong Kong His research centers on Information Retrieval, Data Mining, and Data Management, with a strong emphasis on Recommender Systems. Key areas include shilling attacks and defenses, bias analysis and debiasing, large language model-based recommendation, generative information retrieval, code retrieval, and multi-modal large language models. His work bridges theoretical innovation with practical applications in real-world platforms. His recent publications span top-tier journals and conferences such as IEEE TKDE, TNNLS, TOIS, KDD, SIGIR, AAAI, ACL, and ICLR. The publications demonstrate a consistent focus on robustness, fairness, and efficiency in recommender systems, increasingly integrating large language models and graph-based methods. Themes include adversarial attacks, self-supervised learning, and data augmentation using LLMs. Hui Li actively supervises graduate students, many of whom have published in CCF-A and CCF-B ranked venues. Notable students include Zhang Sheng, Liu Hao, and Li Lianyun. He has contributed to open science by releasing datasets like the Dianping datasets for sequential and social recommendation research. His work has practical implications in e-commerce, social platforms, and software engineering tools. He collaborates with industry partners such as Alibaba, Tencent, and Pinduoduo, where several of his students have been placed. His research is supported by publications in high-impact journals and participation in leading AI conferences.