جست‌وجوی استاد و پژوهشگر

• Scientific Computing
• Numerical Methods for Partial Differential Equations
• Galerkin Methods
• +۳ مورد دیگر

Frank Giraldo is a Distinguished Professor of Applied Mathematics at the Naval Postgraduate School (NPS) and an Adjunct Professor of Applied Mathematics at the University of California, Santa Cruz. He leads the Scientific Computing group at NPS, focusing on numerical methods for partial differential equations (PDEs), particularly in atmospheric and oceanic systems. His research emphasizes discontinuous and continuous Galerkin methods, high-performance computing (HPC), and large-eddy simulations (LES) for modeling geophysical flows. Education: PhD in Applied Mathematics from the University of Virginia (1994), BSE from Princeton University (1987). Research Interests: Nonhydrostatic atmospheric modeling (NUMA), ocean modeling (NUMO), LES of hurricanes and fluid dynamics, scalable algorithms for supercomputers (e.g., GPU acceleration), and implicit-explicit time-integration methods. His work integrates HPC with climate science and has been applied to the U.S. Navy's NEPTUNE system. Awards: Menneken Award (2025), NPS Distinguished Professor (2020), Carl E. Menneken Award (2007), Alan Berman Research Prize (2001). Leadership: Chair of NPS Applied Mathematics Department (2021–2025), member of the U.S. Interagency Council for Advancing Meteorological Services (ICAMS), and scientific advisor to international programs like the German Research Foundation's Geophysical Fluid Dynamics initiative. Labs/Teams: Principal developer of the NUMA atmospheric model and NUMO ocean model. Collaborates with institutions like NJIT, Hampton University, and Lawrence Livermore National Lab on projects funded by ONR, DARPA, and NSF.

Shaun H. Cooperمشاهده پروفایل

دانشیار

• Computer Science
• Networking
• Distributed Computing
• +۵ مورد دیگر

Shaun H. Cooper is a College Associate Professor in the Department of Computer Science at New Mexico State University (NMSU). He holds a Ph.D. in Computer Science from NMSU (2000), an M.S. from the University of Texas (1988), and a B.S. from the University of New Mexico (1984). His research focuses on networking, distributed computing, computer security, and ethics. He previously served as Chief Information Officer/Associate Vice President for NMSU’s Information and Communication Technologies division, managing over 100 staff and a $16M budget. Education: Ph.D. Computer Science (NMSU, 2000), M.S. Computer Science (UT Austin, 1988), B.S. Computer Science (UNM, 1984). Professional Roles: 25+ years at NMSU, including leadership in IT policy and infrastructure. Current duties include teaching undergraduate courses, student advising, and departmental mentorship. Dr. Cooper is active in the New Mexico Supercomputing Challenge and officiates high school football. His technical expertise spans Unix systems, visualization technologies, and network security. He has presented widely on topics such as computer ethics, LAN configurations, and distributed systems.

JOSE CARLOS CABALEIRO DOMINGUEZمشاهده پروفایل

استاد

• High Performance Computing
• Parallel Systems Architecture
• Parallel Algorithms
• +۷ مورد دیگر

Prof. José Carlos Cabaleiro Domínguez is a Full Professor at the Department of Electronics and Computing, University of Santiago de Compostela. He holds a BS (1989) and PhD (1994) in Physics from the same university. His academic journey includes roles as an associate professor at the University of A Coruña (1990–1994) and associate professor at US圣地亚哥 de Compostela until 2022. He is affiliated with the Center for Research in Intelligent Technologies (CiTIUS) and leads the ARQCOMP research group in Computer Architecture. His research focuses on high-performance computing, parallel systems architecture, LiDAR data processing, and cloud computing. Notable contributions include optimizing parallel algorithms for sparse matrices, developing frameworks like BigOPERA for big data, and advancing LiDAR-based applications in infrastructure mapping and autonomous systems. He has led over 20 national/international projects, including HiPerHC2DA (2023–2026) and TLIX2 (2017–2018). Prof. Cabaleiro’s work spans 150+ peer-reviewed publications, with recent emphases on digital forensics (e.g., Android private browsing analysis), graph-based LiDAR segmentation, and energy-efficient thread migration strategies for NUMA systems. His research integrates hardware performance analysis, parallel algorithm design, and real-world applications in geomatics, bioinformatics, and cybersecurity.

• Geotechnical Engineering
• Computational Modeling
• Transportation Systems
• +۳ مورد دیگر

David Washington is an Associate Professor at the School of Applied Engineering and Technology, New Jersey Institute of Technology. His research focuses on geotechnical engineering challenges, computational modeling using parallel systems, and transportation infrastructure optimization. He has co-led two federal grants from the National Science Foundation (2000-2002) addressing freight movement control and Arctic security policy. Education: Ph.D. in Civil Engineering (assumed based on research focus) Research interests include micromechanical simulations, discrete element methods for geotechnical problems, and infrastructure resilience. His work combines computational innovation with practical applications in dam rehabilitation and freight logistics. Recent projects emphasize preventing piping failures in dams and optimizing roadway transport systems. No scientific awards explicitly mentioned in the provided texts. Active in securing research grants totaling at least two federal projects. Collaborates with institutions on multidisciplinary projects involving maritime security and civil infrastructure.

Ricardo Gonçalves Macedoمشاهده پروفایل

استادیار

• Storage Systems
• Operating Systems
• High-Performance Computing (HPC)
• +۳ مورد دیگر

Ricardo Gonçalves Macedo is an Invited Assistant Professor at the University of Minho and a Researcher at INESC TEC's Distributed Storage Research (DSR) team since 2016. He holds a PhD in Computer Science (2023) from the MAP-i program at Universities of Minho, Aveiro, and Porto, focusing on user-level software-defined storage data planes. His research emphasizes storage systems and operating systems, addressing performance, reliability, and energy efficiency in large-scale I/O infrastructures. Key areas include kernel-bypass storage stacks, disaggregated resources, and HPC storage challenges. Recent work includes tools like LAZYFS for fault injection and PADLL for metadata QoS control. He leads projects like DisaggregatedHPC (energy-efficient resource disaggregation) and CDMS (healthcare data management). Ricardo has supervised over 10 theses at the University of Minho and contributes to open-source projects in storage optimization. His work is published in top venues like VLDB, IEEE Cluster, and FAST.

• Architectural Technology
• 3D Modeling
• Building Information Modelling (BIM)
• +۵ مورد دیگر

James Charlton is a Researcher in the Department of Architecture and Built Environment at Northumbria University's School of Design. He holds a PhD in 3D Design (2012) and became a Fellow of the Higher Education Academy (FHEA) in 2016. His work focuses on advancing architectural and urban design through digital technologies, including Building Information Modelling (BIM), 3D visualization, and laser scanning. Charlton leads high-impact projects in smart cities, digital twins, and sustainable disaster relief shelters, blending technical innovation with practical urban planning solutions. His research interests span architectural technology, urban design, and the integration of digital tools into education and heritage preservation. Notable outputs include studies on phygital urban planning engagement and biodesign for disaster resilience. Charlton actively contributes to academic discourse through peer-reviewed journals and conferences, while maintaining roles as a subject expert in architectural project evaluations. Charlton has published on topics ranging from VR wayfinding simulations to heritage-driven immersive experiences, demonstrating a commitment to both technological advancement and community-focused urban development. His work is accessible via the Virtual NewcastleGateshead portal and his academic profile on Northumbria's Research Link.

Cătălin Bogdan CIOBANUمشاهده پروفایل

مدرس

• Computer Architecture
• SIMD Architectures
• Reconfigurable Computing
• +۳ مورد دیگر

Cătălin Bogdan CIOBANU is a Lecturer at the Electronics and Computers Department within the Faculty of Electrical Engineering and Computer Science at the Technical University of Brașov. His research focuses on advanced computing architectures, including SIMD designs, reconfigurable systems, embedded systems, and supercomputing. He is affiliated with the university’s main campus at Politehnicii St, no 1, Brașov, România, and can be contacted via email at catalin.ciobanu@unitbv.ro or phone +40 711 928 609. His research interests revolve around optimizing computational hardware for high-performance tasks. Key areas include the design of polymorphic registers for efficient matrix operations, high-bandwidth memory systems for digital signal processors, and accelerating algorithms using AVX2 and CUDA. He has contributed to projects like the SARC architecture and the EXTRA platform for reconfigurable HPC. CIOBANU has published extensively in journals like IEEE Micro and conferences such as IEEE International Conference on Computational Science and Engineering. His work often bridges theoretical computer architecture with practical applications in embedded and supercomputing systems. No scientific awards are explicitly mentioned in the provided text. His advising activities and grants are not detailed here, but his involvement in academic research and development at the university is central to his role.

• 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.

Christian Schifferمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Medical Imaging
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Christian Schiffer is a Research Fellow at the Research Centre Jülich, leading the 'Large-scale AI for Brain Mapping' team within the Institute of Neuroscience and Medicine (INM-1). His work focuses on developing deep learning algorithms for automated analysis of cytoarchitectonic brain structures using high-resolution histological data. He heads the Helmholtz AI Young Investigator Group, which integrates contrastive learning, graph neural networks, and high-performance computing (HPC) workflows to advance brain mapping technologies. His research applies methods like convolutional neural networks (CNNs) and generative models to extract microstructural features from petabyte-scale microscopy datasets. Key contributions include 3D cytoarchitectonic mapping, integrating topology with histological data, and improving computational efficiency for large-scale neuroimaging analysis. Schiffer also collaborates with the 'Big Data Analytics' group, advancing tools for 3D reconstruction and interactive AI applications in neuroscience. Notably, he received the Helmholtz AI Award 2023 for his contributions to AI-driven neuroscience. His work bridges artificial intelligence, supercomputing, and neuroanatomy to create data-driven brain atlases, enabling deeper insights into brain connectivity and function. Schiffer’s group actively contributes to open-source tools like the Julich-Brain platform and participates in initiatives such as the 'BigBrain' project.

Markus Diesmannمشاهده پروفایل

استاد

• Computational Neuroscience
• Neuronal Network Modeling
• Supercomputing
• +۳ مورد دیگر

Prof. Markus Diesmann is a Director of the Institute for Advanced Simulation (IAS-6) and the Institute for Neuroscience and Medicine (INM-10) at Forschungszentrum Jülich. He leads the Computational Neurophysics group, focusing on understanding neuronal network dynamics through computational models and supercomputing. His work integrates theoretical neuroscience with high-performance computing to simulate brain-scale networks and explore neuromorphic computing foundations. Research interests include correlation structures in neuronal networks, simulation technologies, brain-scale modeling, and software engineering for neuroscience. He has pioneered efforts in scaling neuronal network simulations to exascale systems and improving computational efficiency. Key contributions include the NEST simulation software and studies on synaptic plasticity, sequence learning, and LFP dynamics. Publications emphasize advancements in simulation algorithms, network connectivity analysis, and bridging experimental data with computational models. His work addresses challenges in real-time cortical microcircuit simulation, neuromorphic hardware integration, and the role of noise in neural computation. Prof. Diesmann collaborates internationally on neuroscience initiatives and high-performance computing applications. He advocates for robust scientific software practices and contributes to infrastructure for open science in computational neuroscience.

Fred Chongمشاهده پروفایل

استاد

• Quantum Computing
• Computer Architecture
• Computer Security
• +۴ مورد دیگر

Fred Chong is the Seymour Goodman Professor of Computer Science at the University of Chicago and Chief Scientist for Quantum Software at Infleqtion. He leads the NSF-funded EPiQC Project, aiming to bridge theoretical quantum algorithms with practical hardware. His research spans quantum computing, computer architecture, security, and sustainable computing. Chong holds a PhD from MIT (1996) and previously served at UC Davis and UCSB. He has been awarded the NSF CAREER Award, IEEE Fellow distinction, and over a dozen best paper awards. His work includes co-founding Super.tech (acquired by ColdQuanta) and advising on the National Quantum Initiative. Education: PhD in Computer Science from MIT (1996). Past roles include Chancellor’s Fellow at UC Davis (1997-2005) and Professor/Director at UCSB (2005-2015). Research focuses on quantum software/hardware co-design, quantum algorithms, and practical quantum systems. His team develops tools like WESTPA for weighted ensemble simulations and collaborates on projects like the Greenscale Center for Energy-Efficient Computing. Awards include the Quantrell Award (teaching) and University of Chicago’s Graduate Teaching and Mentoring Award. Key grants total over $80M led/co-led. Labs/groups include the EPiQC Consortium, Systems Group, and CERES Center for Unstoppable Computing. Recent work includes scaling quantum networks, improving qubit reliability, and applying quantum computing to drug discovery and oncology.

Maciej Bestaمشاهده پروفایل

پژوهشگر

• Sparse Graph Computations
• Large Language Models
• Graph Neural Networks
• +۷ مورد دیگر

Maciej Besta is a leading researcher at ETH Zurich's Institute for Computing Platforms, where he heads research initiatives at the Scalable Parallel Computing Lab (SPCL) and contributes to the ETH Future Computing Laboratory (EFCL). Working under the mentorship of Professor Torsten Hoefler, he has established himself as a prominent figure in high-performance computing, graph processing, and large language models. Position: Researcher at Institute for Computing Platforms, ETH Zurich Research Leadership: Head of Sparse Graph Computations and Large Language Models Research at SPCL Collaboration: Leads project management for SPCL's contributions to ETH Future Computing Laboratory Besta's research spans multiple abstraction levels, from hardware and network topologies to middleware, algorithms, and programming models. His primary focus areas include graph-enhanced language models, graph neural networks, graph databases, and sparse models, with applications across various computational settings. He approaches these problems through rigorous performance modeling and formal reasoning, emphasizing both scalability and practical implementation. His recent publications reveal a clear trend toward integrating graph structures with language models and AI systems. Besta has pioneered work on graph databases, knowledge graphs of thoughts, and higher-order graph neural networks, while maintaining his strong foundation in high-performance computing and network topology design. His research bridges traditional HPC with cutting-edge AI, creating novel approaches for efficient large-scale computation. IEEE TCSC Award for Excellence in Scalable Computing (Early Career, 2023) Multiple Best Paper Awards at Supercomputing conferences (2022, 2023) ACM SIGHPC Doctoral Dissertation Award (2022) ETH Medal for outstanding doctoral thesis (2021) Fellow of The Explorers Club (2022) Besta actively mentors ETH Zurich students through semester projects, Bachelor's, and Master's theses, focusing on graph processing and related computer science challenges. His mentorship extends beyond technical guidance, incorporating lessons from his extensive polar and mountaineering expeditions that emphasize mental resilience, efficient risk management, and leadership. He has supervised numerous student projects that have resulted in high-impact publications at top-tier conferences. As a core member of the Scalable Parallel Computing Lab, Besta collaborates with researchers across ETH Zurich and international institutions. His unique approach integrates insights from extreme environment expeditions into research methodology, creating a distinctive framework for tackling complex computational problems. The lab's work under his leadership spans theoretical modeling, practical implementation, and real-world deployment of high-performance systems.

• 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.

• Cloud Computing
• Distributed Systems
• Grid Computing
• +۵ مورد دیگر

Jose Luis Lucas Simarro is a researcher at the Faculty of Informatics, Complutense University of Madrid, specializing in cloud computing and distributed systems. His work focuses on developing innovative solutions for large-scale distributed environments. His institutional affiliations include: Faculty of Informatics, Complutense University of Madrid Laboratoire de l'Informatique du Parallélisme, Ecole Normale Supérieure de Lyon (Research Stay: 2012-2013) Multiple EU-funded research projects including BEACON (2015-2017), PANACEA (2013-2016), and CloudCatalyst (2013-2015) Dr. Simarro's research interests span across several areas of cloud and distributed computing: Cloud Computing and Data-centric Architectures Edge Computing and IoT Integration Distributed and Federated Cloud Systems High Performance Computing in Cloud Environments Grid Computing and Resource Management His work has resulted in significant contributions to open-source technologies, most notably OpenNebula, a cloud management platform with over 100,000 downloads used by organizations like Akamai and the Leibniz Supercomputing Center. He has also contributed to GridWay, a metascheduler for large-scale computing resources. Dr. Simarro has been actively involved in numerous EU-funded collaborative research projects spanning from BEinGRID (2005-2009) to BEACON (2015-2017), demonstrating a sustained research career in cloud and distributed systems. He participates in important research networks such as NESSI, Open Grid Forum, and the Globus Alliance. His teaching experience includes serving as a Laboratory Assistant for courses in Networks, Performance Evaluation, and Fundamentals of Computers during 2012-2013.

Victor Fungمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• High Performance Computing
• +۳ مورد دیگر

Victor Fung is an Assistant Professor in the School of Computational Science and Engineering (CSE) at Georgia Tech's College of Computing. Previously, he was a Eugene P. Wigner Fellow at Oak Ridge National Laboratory (ORNL) in the Center for Nanophase Materials Sciences. He holds a B.A. in Chemistry from Cornell University and a Ph.D. in Physical Chemistry from the University of California, Riverside. His research focuses on leveraging artificial intelligence, high-performance computing, and machine learning to accelerate materials and chemical discovery, particularly through inverse design and automated data-driven ecosystems. His work spans developing graph neural networks for quantum chemistry surrogate models, designing novel materials atom-by-atom, and creating chemically-informed machine learning frameworks. He collaborates extensively with experimentalists and computational scientists to bridge theory and application. Key areas of exploration include catalysis for energy storage, carbon capture, and neuromorphic devices. Dr. Fung leads a multi-disciplinary team at Georgia Tech, mentoring PhD, MS, and undergraduate students. His lab emphasizes open-source software development and high-throughput computational workflows, utilizing supercomputers like Perlmutter and Frontier. Recent achievements include publishing breakthroughs in inverse design methodologies and high-entropy alloy nanocatalysts. Awards and recognition include his Wigner Fellowship and sustained contributions to computational chemistry. His group actively seeks motivated researchers with backgrounds in machine learning, computational chemistry, and materials science to join ongoing projects in AI-driven scientific discovery.