جست‌وجوی استاد و پژوهشگر

Vangelis Karkaletsisمشاهده پروفایل

استاد پژوهشی

• Artificial Intelligence
• Content Analysis
• Big Data Management
• +۴ مورد دیگر

Dr. Vangelis Karkaletsis serves as Director of the Institute of Informatics & Telecommunications at NCSR Demokritos, Greece's premier research center. He represents Greece on the European Artificial Intelligence Board and advises the Greek Prime Minister as a member of the High Level Advisory Committee on AI. His career spans over 25 years in AI research and leadership, including founding and directing the Software & Knowledge Engineering Lab (SKEL | The AI Lab). Dr. Karkaletsis earned his education through: BSc in Computer Engineering & Informatics from University of Patras (1989) MSc in Artificial Intelligence from Queen Mary & Westfield College, University of London (1990) PhD in knowledge representation and information extraction from National and Kapodistrian University of Athens (1995) His research spans artificial intelligence with emphasis on content analysis, big data management, knowledge representation, and human-machine interaction. He has pioneered work in AI ethics, disinformation detection, and trustworthy AI systems, with recent focus on value embedding in AI design and European AI policy frameworks. His work bridges technical AI development with societal implications, particularly in healthcare, earth observation, and democratic processes. Analysis of his recent publications reveals strong emphasis on European AI infrastructure development, particularly through the European AI-on-Demand Platform. His work spans from foundational AI research to practical applications in earth observation, healthcare, and industrial digital transformation. A notable trend is the increasing focus on ethical AI implementation, with several publications addressing value embedding, trustworthy systems, and policy frameworks. As an educator and institution builder, Dr. Karkaletsis initiated the joint PhD scholarship program with international universities, launched MSc programs in Data Science and Artificial Intelligence at NCSR Demokritos, and established educational programs for primary and secondary schools. He has coordinated numerous European and national research projects including DeployAI, AI4Copernicus, AI4Europe, and several European Digital Innovation Hubs focused on Smart Attica, Smart Health, and AI ethics. Dr. Karkaletsis leads the SKEL | The AI Lab and coordinates multiple European Digital Innovation Hubs including Smart Attica EDIH (Greece's first European Digital Innovation Hub for AI), ahedd Digital Innovation Hub, and smartHEALTH. His teams work across three key sectors of the Greek economy: Energy & Environment, Supply Chain & Mobility, and Culture & Tourism, driving digital transformation through AI and big data technologies.

Yu Panمشاهده پروفایل

استاد پژوهشی

• Graph Learning
• Manifold Learning
• Representation Learning
• +۴ مورد دیگر

Yu Pan is a Research Assistant Professor in the Department of Biological Systems Engineering at the University of Nebraska-Lincoln (UNL). He holds a Ph.D. in Computer Science from UNL, an M.S. in Information Management from Illinois Institute of Technology, and a B.S. in Computer Science and Technology from the University of Electronic Science and Technology of China. His research focuses on Graph Learning, Manifold Learning, Representation Learning, and Data Visualization, with applications in interdisciplinary fields such as biological systems and meteorology. He designs and maintains the Scientific Data Management Infrastructure for the Institute of Agriculture and Natural Resources (IANR), implementing FAIR principles to standardize data pipelines and enhance accessibility for researchers. His work also involves High Performance Computing (HPC) frameworks like MPI and CUDA to optimize data processing and analysis. Education: Ph.D., Computer Science, University of Nebraska-Lincoln M.S., Information Management and Technology, Illinois Institute of Technology B.S., Computer Science and Technology, University of Electronic Science and Technology of China Research Interests: Dr. Pan’s work bridges computational methods with scientific data challenges. He explores techniques to extract meaningful insights from complex datasets using graph-based approaches and manifold learning, while advancing visualization tools to aid domain scientists. His infrastructure projects emphasize scalable, FAIR-compliant systems to manage heterogeneous data across disciplines. Current collaborations with biological and environmental researchers aim to enhance data-driven decision-making through advanced analytics. Advising and Infrastructure: While no formal advisees are listed, his role in IANR’s data infrastructure project underscores his leadership in collaborative scientific computing. The infrastructure facilitates interdisciplinary research by integrating spatially and temporally co-located datasets, enabling researchers to explore patterns in agriculture, climate, and natural resources. Labs/Teams: He is affiliated with the Department of Biological Systems Engineering and contributes to the IANR’s data management initiatives, fostering cross-disciplinary innovation through computational solutions.

• Distributed Systems
• Cloud Computing
• Big Data Processing
• +۴ مورد دیگر

Ignacio Martín Llorente is a Full Professor (Catedrático) at the Universidad Complutense de Madrid (UCM), leading the Distributed Systems Architecture Research Group and the Data-intensive Cloud Lab . He holds visiting positions at Harvard University as a Visiting Professor in the John A. Paulson School of Engineering and Applied Sciences (SEAS) and as a Visiting Scholar in FAS Research Computing. He earned a Ph.D. in Computer Science from UCM and an Executive MBA from IE Business School. His research focuses on distributed systems, cloud computing architectures, big data processing, edge computing, and federated networking. He pioneered projects like OpenNebula (a cloud management system) and contributed to EU initiatives such as RESERVOIR (cloud virtualization), StratusLab (grid-to-cloud integration), and BEACON (federated networking). He has authored over 169 publications and serves on editorial boards for IEEE Transactions on Cloud Computing and Journal of Grid Computing . He has secured over €100M in EU grants, leading 10 major projects. His work emphasizes transferring research into open-source technologies and commercial products. He advises OpenNebula Systems and has been a consultant for governments and companies like Microsoft. His teaching spans 67 courses in distributed systems, cloud computing, and high-performance computing.

• High-performance Computing
• Parallel Algorithms
• Scientific Computing

Professor Tobias Weinzierl heads the Scientific Computing research group at Durham University's Department of Computer Science. He is Co-director of the Institute for Data Science (IDAS) with responsibility for Large-Scale Computing initiatives and serves as inaugural director of the Master in Scientific Computing and Data Analysis (MISCADA). Professor Weinzierl is a reviewer for the EuroHPC JU and principal investigator on multiple ExCALIBUR projects. His research focuses on parallel algorithms, high-performance computing, and scientific computing methodologies. Key areas include adaptive mesh refinement, task-based parallelism, and exascale computing frameworks. Professor Weinzierl develops the Peano software framework for parallel grid traversals and contributes to the ExaHyPE engine for hyperbolic PDEs. His publications span parallel computing paradigms, performance optimization, and applications in computational geophysics. Research trends include GPU offloading, fault tolerance in HPC, and SYCL programming models. Professor Weinzierl has authored books including Principles of Parallel Scientific Computing and edited volumes such as Advanced Computing . He regularly presents at major HPC conferences and workshops worldwide. He advises postgraduate students working on parallel algorithms, GPU acceleration, and computational science applications. Research groups include the Scientific Computing and Data Analysis initiatives at Durham.

Charlie Peckمشاهده پروفایل

استاد

• Parallel and distributed computing
• Computational science
• Bioinformatics
• +۶ مورد دیگر

Charlie Peck is a Professor of Computer Science and Associate Dean of Natural Sciences at Earlham College. He leads the Computer Science Department and oversees interdisciplinary initiatives in the natural sciences. His research focuses on parallel/distributed computing, bioinformatics, and expeditionary science, combining fieldwork with computational methods. Peck is co-PI of the LittleFe and Bootable Cluster CD (BCCD) projects, which develop affordable HPC education tools. He has conducted field research in Iceland, studying glacial microbiology and environmental data collection, often involving student collaborators. Education: Ph.D., Union Institute & University B.A., Earlham College Research interests include: High-performance computing education Environmental sensor networks Microbial community analysis Sustainable energy systems His fieldwork often integrates geology, chemistry, and computer science, as seen in collaborative Iceland expeditions. Peck also leads educational programs like Earlham’s England study abroad program, emphasizing place-based learning in London. Publications highlight HPC curricula, cluster management systems, and interdisciplinary environmental research. He actively contributes to organizations like ACM (SIGCSE) and IEEE (Computer Society), focusing on education and outreach in computational science.

João Tiago Pauloمشاهده پروفایل

استادیار

• Distributed Systems
• Storage Systems
• Database Systems
• +۶ مورد دیگر

João Tiago Paulo is an Assistant Professor at the University of Minho and a Senior Researcher at INESC TEC. He obtained his PhD in Computer Science from the MAP-i Doctoral Program, focusing on large-scale distributed systems, storage, and database scalability, performance, security, and dependability. His work addresses complex data management challenges in Cloud Computing and HPC environments. Research Interests: João specializes in Reliable Distributed Storage Systems Software-Defined Storage architectures Privacy and security in data management Fault injection for debugging storage systems Performance benchmarking and optimization Publication Trends: His recent work spans distributed systems, storage scalability, fault reproduction, and privacy-preserving machine learning. He has published in venues like Transportation Engineering , Proceedings of the VLDB Endowment , and SC24-W , with keywords intersecting computer science, HPC, and data security. Supervised Theses: João has supervised multiple MSc theses at INESCTEC, covering topics like edge time series database benchmarking, distributed storage optimizations, privacy-preserving ML frameworks, and fault injection in storage systems.

Sanjukta Bhowmickمشاهده پروفایل

دانشیار

• Computer Science
• Network Analysis
• Algorithms
• +۵ مورد دیگر

Sanjukta Bhowmick is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas, part of the College of Engineering. Her research focuses on network analysis, graph algorithms, parallel computing, and scalable data systems. She is actively involved in collaborative projects such as LEGAS (Learning Evolving Graphs At Scale) and MLN-DIVE (Multilayer Network Data Infrastructure for Visualization and Exploration), emphasizing interdisciplinary applications like precision agriculture and cybersecurity. Her work bridges theoretical foundations with practical implementations, addressing challenges in dynamic networks, community detection, and high-performance computing. Notable contributions include efficient algorithms for motif counting, centrality disruption analysis, and checkpointing systems optimized for GPU acceleration. She has co-authored numerous papers presented at conferences like SIAM Data Mining and HPC Symposium, showcasing her commitment to advancing computational methods for complex systems. Grants & Collaborations: Collaborative Research projects funded by NSF (SHF: Small, CSSI: CANDY), ANACIN-X framework for nondeterminism analysis, and multilayer network infrastructure initiatives. Key Themes: Network robustness, scalable parallel algorithms, graph-based machine learning, and interdisciplinary data-driven solutions. Her research emphasizes real-world applications, such as optimizing variable rate spraying in agriculture and enhancing fault tolerance in distributed systems. She actively contributes to workshops and symposiums, promoting co-design approaches for deep learning accelerators and high-performance computing systems.

José R. Herreroمشاهده پروفایل

دانشیار

• High Performance Computing
• Parallel Computing
• Dense Linear Algebra Algorithms
• +۳ مورد دیگر

José R. Herrero is an Associate Professor in the Computer Architecture Department (DAC) at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB). His primary research area is High Performance Computing (HPC), focusing on parallel algorithms, dense linear algebra, and multicore processor optimization. He has held administrative roles including Vice-Dean for Academic Studies (2010–2013) and Vice-Dean for Institutional and International Relations (2013–2015) at FIB. Education: PhD in Computer Science from UPC (2006), Postgraduate in Training of University Professors (1997), and a Computer Science degree (1993). Teaching experience includes over 15 courses in Computer Architecture, Operating Systems, and Parallel Programming at both undergraduate and graduate levels. Research interests span HPC, parallel computing, and algorithm design. His work emphasizes optimizing matrix computations and linear algebra libraries for modern architectures. Notable contributions include advancements in sparse Cholesky factorization, task-parallel algorithms, and energy-efficient computing. Recognition includes UPC's Teaching Mention for Outstanding Quality (2009, 2014, 2019) and a Certificate of Merit for contributions to scientific computing workshops. He advises numerous PhD students and collaborates internationally, including visiting roles at GeorgiaTech and École Polytechnique de Montréal.

• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• Load Balancing
• +۴ مورد دیگر

Florina M. Ciorba is a Professor in the field of High-Performance Computing (HPC), affiliated with the University of Basel. Her research focuses on parallel algorithms, fault-tolerant systems, dynamic load balancing, and energy-efficient computing. She has contributed extensively to HPC workflows, scheduling techniques, and resilient numerical methods for extreme-scale simulations. Key research areas include: Parallel and distributed systems Load balancing and scheduling algorithms Fault tolerance mechanisms GPU and heterogeneous computing Energy-aware HPC applications Recent work emphasizes scalable particle simulations, application classification using ML, and optimizing astrophysics workflows. She collaborates with international teams on standards like SPEChpc 2021 benchmarks and HPC operational autonomy loops. Publications span top venues including IEEE Transactions on Parallel and Distributed Systems, Euro-Par, and Cluster conferences. Her work addresses both algorithmic innovations and software implementations for next-generation HPC platforms.

• High-Performance Computing
• Computational Fluid Dynamics
• Climate Modeling
• +۵ مورد دیگر

Henry M. Tufo is a Researcher affiliated with the University of Colorado, USA . His work spans High-Performance Computing (HPC) , Cloud Computing , and Computational Fluid Dynamics , with a focus on climate modeling, grid systems, and scalable algorithms. Tufo has collaborated extensively with institutions like IBM, Argonne National Laboratory, and researchers such as Paul Fischer, Kate Keahey, and Paul Marshall. His research interests include: Developing scalable HPC systems for climate and astrophysical simulations Integrating cloud computing with scientific workflows Optimizing spectral element methods for atmospheric models Trends in his publications highlight expertise in parallel computing , secure execution environments , and numerical methods for fluid dynamics. Tufo has contributed to frameworks like the FLASH code and GraphBLAS for large-scale simulations.

• High-Performance Computing
• Performance Modeling
• Parallel Computing
• +۳ مورد دیگر

Alexandru Calotoiu is a Researcher in the Department of Computer Science at ETH Zürich, affiliated with the Professorship for Scalable Parallel Computing. His work focuses on performance modeling, high-performance computing (HPC), serverless systems, and cloud computing. He leads research in empirical performance modeling for complex applications, optimization of parallel algorithms, and scalable cloud architectures. Key research areas include noise-resilient performance models, serverless computing frameworks, and compositional parallel programming. He has contributed to benchmarking tools like SeBS and developed techniques for loop scheduling, static analysis, and resource disaggregation in HPC environments. His publications from 2023–2025 emphasize serverless systems (e.g., FaaSKeeper, Cppless), performance embeddings for optimization, and specialized supercomputing for climate science. These studies address scalability, reproducibility, and cross-platform performance portability in data-centric workloads. No scientific awards are explicitly listed, but his work has been presented at leading conferences such as ISCA and IEEE/ACM events. He collaborates on projects like rFaaS (RDMA-enabled serverless platforms) and Process-as-a-Service frameworks. His research bridges theoretical models with practical implementations in distributed systems and cloud infrastructure.

Philipp Neumannمشاهده پروفایل

استاد

• High Performance Computing
• Molecular Dynamics Simulations
• Multiscale Simulations in Fluid Dynamics
• +۵ مورد دیگر

Philipp Neumann is a Professor of Informatics with a focus on High Performance Computing and Data Science, holding a joint appointment at DESY and the University of Hamburg. As a Leading Scientist and Head of IT at DESY, he coordinates the Helmholtz Federated IT Services (HIFIS) and specializes in HPC for molecular and multiscale simulations. Diploma in Engineering Mathematics (2008), Friedrich-Alexander-University Erlangen-Nuremberg Doctorate in Scientific Computing (2013), Technical University of Munich Habilitation in Scientific Computing (2019), University of Hamburg Neumann's research spans high-performance computing, molecular dynamics, machine learning, and exascale applications. He develops tools like MaMiCo and AutoPas for multiscale and particle simulations, with applications in climate modeling, proteomics, and industrial use cases. His work emphasizes energy efficiency, fault tolerance, and automated algorithm selection. Recent publications highlight advancements in HPC software (e.g., xbat ), data harmonization (e.g., HarmonizR ), and simulation methodologies (e.g., aerodynamic lens systems, multiscale fluid dynamics). These reflect his expertise in bridging molecular and continuum-scale simulations with data-driven techniques. At DESY, Neumann leads IT infrastructure initiatives, including federated IT services for the Helmholtz Association. He contributes to projects like hpc.bw and serves on committees for international HPC conferences, demonstrating leadership in software development and computational science education.

• Computational Sustainability
• Cloud Computing
• Edge Computing
• +۳ مورد دیگر

Ivona Brandic is a Full Professor of High Performance Computing Systems at TU Wien's Institute of Information Systems Engineering, leading the HPC Research Group. She specializes in Computational Sustainability, Cloud/Edge Computing, and Quantum-Classical Systems. Her roles include Head of the Computational Sustainability Research Unit and membership in TU Wien's Faculty Council. She teaches courses such as AI/ML in Climate Change and Hybrid Quantum-Classical Systems. Her research focuses on sustainable IT, energy-efficient systems, and hybrid quantum-classical workflows. Projects include computational sustainability initiatives funded by the Austrian Science Fund (FWF) and industry partnerships like the Virtual Shepherd project. She has contributed to over 50 publications, emphasizing edge computing, quantum algorithms, and HPC optimization. Notable contributions include developing frameworks like RIGOLETTO for hybrid scientific workflows and FRESCO for edge offloading. Her work bridges theoretical advancements with practical applications in environmental monitoring and energy efficiency.

Barton P. Millerمشاهده پروفایل

استاد

• Binary Code Analysis
• Software Security
• Distributed Systems
• +۵ مورد دیگر

Barton P. Miller is a Vilas Distinguished Achievement Professor and the Amar & Balinder Sohi Professor of Computer Sciences at the University of Wisconsin, Madison, where he has made significant contributions to computer science research and education. As a faculty member in the Computer Sciences Department within the College of Engineering, he directs major research initiatives including the Paradyn Tools project and leads cybersecurity efforts as Chief Scientist of the DHS-funded Software Assurance Marketplace (SWAMP) research center. Miller received his B.A. degree from the University of California, San Diego in 1977, and M.S. and Ph.D. degrees in Computer Science from the University of California, Berkeley in 1980 and 1984. His educational background laid the foundation for his pioneering work in software testing and analysis. His research spans multiple critical areas of computer science, with a primary focus on binary code analysis and instrumentation , software security , and distributed and parallel program performance . Miller is particularly renowned for founding the field of Fuzz random software testing in 1988 and dynamic binary code instrumentation in 1992. His work bridges theoretical computer science with practical applications, addressing real-world challenges in high-performance computing systems, cybersecurity, and scalable distributed systems. His research has direct applications in national security through his work with DHS and TrustedCI, the NSF Cybersecurity Center of Excellence. Miller's publications reveal a consistent focus on binary analysis tools, security vulnerability detection, and performance optimization techniques. His recent work shows increasing emphasis on ransomware analysis, GPU performance optimization, and practical security tool development for developers. The trajectory of his research demonstrates how foundational work in binary instrumentation has evolved to address contemporary cybersecurity challenges while maintaining relevance to high-performance computing environments. Among his notable honors, Miller is a Fellow of the ACM and recipient of the prestigious Jean-Claude Laprie Award in Dependable Computing , an R&D 100 Award , and multiple best paper awards including at HPDC 2009 and CCGrid 2018. His contributions to the field have been recognized through distinguished lectureships at institutions including Ben Gurion University and IBM T.J. Watson Research Center. As an educator, Miller has mentored numerous students and taught courses spanning operating systems, software security, and distributed systems. He has received teaching awards and developed educational resources including free and open software security training materials. His work with TrustedCI has focused on cybersecurity for scientific communities, helping researchers secure their computational infrastructure while maintaining research productivity. Miller also co-directs the MIST software vulnerability assessment project in collaboration with colleagues at the Autonomous University of Barcelona. Miller directs the Paradyn Tools project, which investigates program scalability and binary program analysis technologies for use in HPC, systems design, and cyber-security. His work extends to practical applications through collaborations with government agencies including DHS and the U.S. Secret Service Electronic Crimes Task Force. His research group has developed tools like Dyninst, MRNet, and the Wisconsin Safety Analyzer that have become foundational in their respective domains.

Thomas Zeiserمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Lattice Boltzmann Methods
• Computational Fluid Dynamics
• +۳ مورد دیگر

Dr. Thomas Zeiser serves as Head of Systems & Services and Chief Operating Officer HPC at NHR@FAU (Center for National High Performance Computing Erlangen) at Friedrich Alexander University Erlangen-Nuremberg. He leads the Systems & Services group of NHR@FAU and HPC4FAU since the end of 2020 and serves as deputy for NHR@FAU in the NHR Betreiberausschuss. His work focuses on transitioning from serving FAU only to national center operations, managing HPC systems, procuring new infrastructure, financial controlling of NHR@FAU's budgets, and supporting planning for a new data center building. Dr. Zeiser's research interests center around High-Performance Computing with specific expertise in Lattice Boltzmann Methods , large-scale simulations, evaluation of HPC hardware and software, and efficient operation of HPC systems. His work bridges the gap between theoretical computational methods and practical implementation in high-performance environments. He has implemented the first job-based job and performance monitoring for RRZE's HPC systems and has extensive experience in procurement of HPC infrastructure. Analysis of Dr. Zeiser's publication record reveals a consistent focus on practical applications of computational methods in HPC environments. His research demonstrates a progression from fundamental lattice Boltzmann method development toward increasingly complex system-level concerns including fault tolerance, performance monitoring, energy efficiency, and scalable implementations. The publications show strong collaboration patterns with researchers at FAU and other German institutions, particularly in the areas of computational fluid dynamics and parallel computing. Dr. Zeiser regularly serves as a reviewer for various journals and compute time commissions of different HPC centers. He is also one of the local organizers for the Ferienakademie of TUM, FAU, and Universität Stuttgart held in Sarntal. His work with NHR@FAU positions him at the forefront of national high-performance computing infrastructure in Germany. At NHR@FAU and RRZE (Regional Computing Center Erlangen), Dr. Zeiser leads teams responsible for operating some of Germany's most powerful academic supercomputing resources. His group plays a critical role in supporting computational research across multiple disciplines at FAU and increasingly at the national level through the NHR initiative.