جست‌وجوی استاد و پژوهشگر

Nina Herrmannمشاهده پروفایل

پژوهشگر

• Parallel Programming
• Domain-Specific Languages (DSL)
• Algorithmic Skeletons
• +۵ مورد دیگر

Dr. Nina Herrmann is a researcher at the Chair of Machine Learning and Data Engineering (School of Business and Economics, University of Münster). Her work focuses on parallel programming, domain-specific languages (DSL), and algorithmic skeletons for heterogeneous computing environments. Education: Bachelor of Information Systems (WWU Münster, 2017) Master of Science in Information Systems (WWU Münster, 2019) Semester abroad at University of Trento (Italy, 2018–2019) Research Interests revolve around optimizing high-performance computing through algorithmic skeletons, DSL design, and machine learning applications in resource-constrained systems. She has supervised multiple theses on topics including EdgeML methods, memory-efficient ML models, and parallel programming frameworks. Project Contributions include the ongoing Musket (Muenster Skeleton Tool) for DSL-based parallel code generation, and the completed Muesli (Muenster Skeleton Library), which simplified parallel application development using algorithmic skeletons. Teaching Activities span courses like Computer Science I/II , Parallel Programming , and specialized seminars on topics including graph machine learning and process mining. Contact: nina.herrmann@uni-muenster.de

• High-Performance Computing
• Parallel Computing
• Heterogeneous Computing
• +۷ مورد دیگر

Alberto José Gonçalves Carvalho Proença serves as Full Professor in the Department of Informatics at the School of Engineering, University of Minho, and holds the position of Senior Researcher with Dr. habil at Centro ALGORITMI. His academic career spans over 45 years, beginning at the University of Porto in 1976 before transitioning to the University of Minho in 1977 where he has remained ever since. His educational foundation includes: Licentiate in Electrical Engineering from Coimbra, Portugal (1976) MSc in Digital Electronics from UMIST, Manchester, UK (1979) PhD from Manchester, UK (1982) Dr. habil (Habilitation) from the University of Minho (1998) Professor Proença's research spans from digital electronics to cutting-edge high-performance computing, with core expertise in computer architecture, parallel computing, and heterogeneous computing systems. His work bridges theoretical computer science with practical applications across diverse domains including particle physics (LHC data analysis), forensic science (DNA library systems), materials science (crystallographic imaging), and cultural heritage preservation. Current research focuses on optimizing computational efficiency across heterogeneous resources for scientific applications, addressing challenges in big data processing, parallel random number generation, and scheduling algorithms for distributed environments. Throughout his career, Professor Proença has successfully supervised numerous MSc dissertations and PhD theses while contributing significantly to institutional computing infrastructure. He chaired the University Computer Centre for 17 years (1985-2002), establishing Portugal's first national parallel computing service. For 11 years (2006-2017), he led the Advanced Computing theme in the University of Texas at Austin-Portugal cooperation program. Since 2005, he has directed the university's SeARCH heterogeneous computing cluster, supporting research across multiple scientific disciplines. His research group has developed several influential frameworks including JaSkel (Java Skeleton-Based Framework for Cluster and Grid Computing), HEP-Frame (for LHC data analysis), Im2Cr (crystallographic imaging tool), and CROSS-Fire (risk management decision support system). These platforms demonstrate his commitment to translating theoretical advances into practical tools that address real-world scientific and societal challenges.

Vijay Narayananمشاهده پروفایل

استاد

• Power Aware Computing
• Computer Architecture
• Embedded Systems
• +۴ مورد دیگر

Vijay Narayanan is the Robert Noll Chair Professor in Computer Science & Engineering and Electrical Engineering at Pennsylvania State University. He co-directs the Microsystems Design Lab and leads research in embedded visual analytics, self-powered processors, and system design using emerging devices. Education: B.E in Computer Science and Engineering (1993) from University of Madras, India Ph.D. in Computer Science and Engineering (1998) from University of South Florida, USA His research spans Power Aware Computing , Computer Architecture , and Embedded Systems , with emphasis on Visual Cortex on Silicon and Self-Powered Processors . Current work includes Non-Volatile Processors and Design Automation under unreliable power conditions via NSF ERC ASSIST. Recent publications focus on GPU architecture (Tensor Cores, ACE), Memory Consistency Verification (QED), and Neural Radiance Fields (Disorf, Distwar) for robotics and rendering. Key collaborations include Tsinghua University and DARPA/SRC LEAST Center . Scientific Awards: IEEE Fellow ACM Fellow He leads the Architecture, Benchmarking and Circuits Thrust in the DARPA/SRC LEAST Center and contributes to NSF ERC ASSIST for self-powered systems. Grants and projects emphasize cross-layer optimizations and hardware-software co-design.

• Computer Systems
• Soft Computing
• Combinatorial Optimization
• +۵ مورد دیگر

Kazushi Kawamura is an Assistant Professor at the Tokyo Institute of Technology 's AI Computing Research Unit , with prior roles at Waseda University and the Institute of Science Tokyo. His academic journey began at Waseda University, where he earned a Dr. Eng in 2016. 2025.04 - Now: Assistant Professor, School of Fundamental Science and Engineering, Tokyo Tech 2024.10 - 2025.03: Assistant Professor, School of Fundamental Science and Engineering, Waseda 2020.04 - 2025.03: Specially Appointed Assistant Professor, Institute of Integrated Research, Institute of Science Tokyo His research spans combinatorial optimization , annealing processors , Ising machines , and FPGA-based computing systems . He has contributed extensively to LSI design methodology and high-level synthesis . Key publication trends include neural network compression , parallel annealing algorithms , edge AI inference , and sparse matrix operations on FPGAs. His work often integrates theoretical optimization with practical hardware implementations . 2023 CS Achievement Award 2016 ISOCC Best Paper Multiple Algorithm Design Contest Prizes (2014-2019) IPSJ SLDM Outstanding Student Awards Kawamura teaches courses like Physical Electronics Laboratory and Machine Learning at Waseda, with recent projects such as Amorphica (metamorphic annealer) and Pianissimo (sub-mW DNN accelerator). He serves on committees for IEEE , IPSJ , and IEICE , focusing on system design and circuit optimization.

Lars Karlssonمشاهده پروفایل

دانشیار

• High-Performance Computing
• Numerical Linear Algebra
• Parallel Algorithms
• +۲ مورد دیگر

Lars Karlsson is an Associate Professor at the Department of Computing Science, Umeå University, where he serves as Assistant Head of Department with responsibilities for undergraduate education. His research focuses on developing efficient algorithms for matrix and tensor computations within high-performance computing environments. He is affiliated with the Parallel and Scientific Computing research group at Umeå. Karlsson's primary research areas include: Numerical Linear Algebra : Specializing in matrix factorizations and eigenvalue computations Parallel Algorithms : Designing scalable solutions for distributed and shared-memory systems Tensor Computations : Developing decomposition methods and completion algorithms Performance Optimization : Auto-tuning techniques for modern computing architectures His publications (2010-2025) demonstrate consistent focus on parallel algorithms for numerical problems, particularly matrix reductions, eigenvalue computations, and tensor decompositions. Recent work emphasizes auto-tuning, robustness in numerical methods, and efficient scheduling for high-performance systems. The majority of publications involve collaborative research within European computing consortia like NLAFET. Karlsson contributes to educational research, having explored mastery learning approaches in university settings. His administrative responsibilities include oversight of undergraduate programs at the Department of Computing Science.

• Scientific Visualization
• Computer Graphics
• Virtual Reality
• +۳ مورد دیگر

Guido Reina is a Senior Academic Councillor at the Visualization Institute of the University of Stuttgart (VISUS), affiliated with the Ertl Working Group. His research focuses on scientific visualization, computer graphics, and virtual reality, with a particular emphasis on rendering techniques for particle data, foveated visualization, and reproducibility in visualization workflows. His work spans interdisciplinary applications, including porous media analysis, energy-efficient rendering, and integration of visualizations into gaming consoles. He has contributed to frameworks like MegaMol and explored adaptive resolution scaling for high-performance 2D visualization. Guido is also a co-author of the EGPGV 2023 Best Paper Award. EGPGV 2023 Best Paper Award Guido collaborates with teams at VISUS and the University of Stuttgart, advancing immersive analytics and in situ visualization methodologies. His publications highlight the evolution of visualization research toward handling large-scale datasets, optimizing rendering pipelines, and enhancing user interaction in augmented/virtual reality environments.

Lixin Taoمشاهده پروفایل

استاد

• Cloud Computing
• Internet/Web Computing
• Intelligent Systems
• +۴ مورد دیگر

Dr. Lixin Tao is a tenured Professor and Chairperson of the Computer Science Department at Pace University's Seidenberg School of CSIS. He earned his PhD in Computer Science from the University of Pennsylvania in 1988 and has conducted research and teaching at Concordia University (1988-2001) before joining Pace University. Research Interests: Dr. Tao's work spans Internet Computing , Server Scalability , Component Technologies , Parallel Computing , Functional Simulation , and Combinatorial Optimization , with recent focus on Knowledge Graphs , Semantic Web , and Secure Mobile Cloud Systems . He has pioneered cloud computing research since the 1990s and developed industry standards like OWL extensions. Over 300 peer-reviewed publications Director of Pace University's PhD Computer Science Program (2013-2017) Developed MS in Enterprise Analytics (2015) and BS in Computer Engineering (2017) Scientific Awards: Recognized as IEEE Senior Member (2020), ABET Evaluator (2001-2012), and recipient of multiple research grants including a $12M NSERC Strategic Technology Program and NSF awards for server infrastructure. He received Pace University's Presidential Research Grant and collaborated with institutions like Stanford University on knowledge engineering projects. Professional Contributions: Directed ABET accreditation for Pace's BS-CS program (2010-2012), served as Editor-in-Chief for the Journal of Computer Science Research (2018-present), and supervised dozens of PhD/DPS students in areas ranging from Mobile Payment Protocols to Drug Side Effect Knowledge Graphs . His lab focuses on Knowledge-Empowered AI and Secure Software Development .

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۱۱ مورد دیگر

Fatima Boukari is an Associate Professor in Computer Science within the Division of Physics, Engineering, Mathematics and Computer Sciences at Delaware State University. Her research bridges artificial intelligence, deep learning, and mathematical modeling to develop robust solutions for biomedical engineering, cell biology, and agricultural technology challenges. Education: B.Sc. in Computer Science Engineering from University of Annaba, Algeria Dual M.Sc. degrees in Computer Systems Architectures and Parallel Computing from Algeria-Glasgow Ph.D. in Mathematics & Physics from Delaware State University Dr. Boukari's research centers on foundational Deep Learning architectures and mathematical modeling applied to biomedical diagnostics and cell dynamics analysis. Her work in reinforcement learning and transfer learning enhances decision-making for autonomous systems, while her cognitive modeling research decodes human perception using EEG data. She pioneers multi-modal distributed learning systems that maintain privacy across heterogeneous sensor networks, addressing critical gaps in military ISR applications. Her recent publications reveal a strong trajectory toward spectral data analysis for medical diagnostics and AI-driven cognitive modeling , with increasing emphasis on trustworthy AI solutions for healthcare and environmental sustainability. The consistent focus on cell segmentation/tracking algorithms demonstrates her commitment to advancing biomedical image analysis. Scientific Awards: No scientific awards, prizes, or fellowships listed in available information Dr. Boukari has mentored over 40 undergraduate and 2 graduate students from underrepresented STEM backgrounds. Her active research portfolio includes: NSF CISE grant for biomolecular detection using physics-informed machine learning Air Force RITA/UARC project on neuroscience computational modeling Air Force project building robust multi-modal distributed learning systems DE-CTR ACCEL project for COVID-19 respiratory disease diagnosis NSF grant for Delaware and Mid-Atlantic Data Science Corps Research scientist role in AI-CLIMATE National AI Research Institute She leads the Applied Interdisciplinary Data Science (AIDA) Laboratory and serves as Project Lead for the CAST E-IoT Center's four agricultural research thrusts. As Team Lead of the 1890 Working Group on Artificial Intelligence, she drives initiatives addressing climate change resilience and food security through responsible AI development.

• Programming Languages
• Compiler Construction
• Parallel Computing
• +۷ مورد دیگر

Wayne Kelly is an Associate Professor in the School of Computer Science at Queensland University of Technology (QUT), Faculty of Science. He has over 25 years of academic experience and serves as the Academic Lead for Teaching and Learning and Course Coordinator for the Bachelor of Information Technology degree. PhD in Computer Science, University of Maryland, College Park, 1996 BSc (Hons) in Computer Science, University of Queensland, 1989 His research expertise lies in Programming Languages, Compiler Construction, and Parallel Computing, with significant contributions to High Performance Computing, Big Data, and Bioinformatics. His work has led to collaborations with Microsoft Research and over $2 million in external funding. His recent publications reflect a strong trend in parallel and distributed systems, embedded computing, bioinformatics data analysis, and remote sensing. Key themes include optimization of computational systems, memory management, and scalable data processing. Wayne Kelly has made impactful contributions to both teaching and research, guiding numerous postgraduate students and leading curriculum development in information technology. Optimizing I/O cost and managing memory for bioinformatics A communication model for streaming applications on MPSoC Ruby.NET: a compiler for the Common Language Infrastructure He is actively engaged in real-world technology development, including a project with a vision-impaired student to improve public transportation accessibility, currently trialed by transport authorities in Australia and the US.

Georg Hagerمشاهده پروفایل

پژوهشگر

• High Performance Computing
• Performance Engineering
• Computational Physics
• +۷ مورد دیگر

Dr. Georg Hager is Head of Research at the Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg, and an associate lecturer at the Institute of Physics, University of Greifswald. His work focuses on performance engineering, node-level optimization, and analytic modeling in high performance computing. Research Interests: High Performance Computing (HPC) Performance Engineering and Modeling Architecture-Specific Optimization Energy Efficiency in Computing Scientific Code Optimization Execution-Cache-Memory (ECM) Model Development Computer Architecture for HPC His recent publications and tutorials emphasize performance modeling, node-level engineering, hybrid programming (MPI+OpenMP), and benchmarking on modern architectures such as Ice Lake, Sapphire Rapids, and A64FX. He has contributed to the development of the LIKWID tool suite and promotes best practices in HPC education. Scientific Awards: ISC Gauss Award (2018) Informatics Europe Curriculum Best Practices Award (2011) Georg Hager has been instrumental in developing and teaching international tutorials on performance engineering and hybrid programming, often in collaboration with HLRS Stuttgart and TU Wien. He is the co-author of the widely used textbook Introduction to High Performance Computing for Scientists and Engineers . His work bridges theoretical modeling and practical application, aiming to improve time-to-solution and resource efficiency in large-scale scientific computing.

Daniel Sonntagمشاهده پروفایل

استاد

• Intelligent User Interfaces
• Natural Language Processing
• Information Retrieval
• +۷ مورد دیگر

Prof. Dr.-Ing. Daniel Sonntag serves as Scientific Director of Interactive Machine Learning at the German Research Center for Artificial Intelligence (DFKI) and holds an Endowed Chair of Applied Artificial Intelligence at University of Oldenburg. He leads multiple significant research initiatives including No-IDLE (Interactive Deep Learning Enterprise), MASTER (Mixed reality ecosystem for teaching robotics), Ophthalmo-AI (intelligent eye care diagnostics), pAItient (AI applications in medicine), and XAINES (explainable AI with narratives). His research spans intelligent user interfaces, natural language processing, information retrieval, dialogue systems, common-sense modeling, and explainable machine learning methods for cognitive computing. His work particularly focuses on multimodal-multisensor interfaces for medical and health systems and Industry 4.0 applications. His research has had industrial impact, with SmartWeb's multimodal interaction design forming the technical infrastructure for Apple's Siri. His recent publications demonstrate strong trends in medical AI applications, particularly in eye tracking, radiology report generation with enhanced interpretability, and interactive federated learning frameworks. His work consistently bridges theoretical AI advances with practical medical and industrial applications. German High Tech Champion Award (2011) As a principal investigator, Sonntag leads national and European projects funded by the Federal Ministry of Education and Research, Federal Ministry for Economic Affairs and Energy, Federal Ministry of Health, and Horizon Europe. He has supervised numerous researchers including Michael Barz, Jan Zacharias, and Alexander Prange across multiple projects. His research group has successfully transitioned technologies to industry, with former researchers joining companies like Apple, Wacom, and Semvox. Sonntag's laboratory work centers on the Interactive Machine Learning department at DFKI, with strong connections to medical institutions and industry partners. His team develops cognitive assistance systems, mixed reality applications for smart factories, and AI-based diagnostic support tools, particularly in ophthalmology and radiology.

Dario Colazzoمشاهده پروفایل

استاد

• Databases
• Programming Languages
• Cloud Computing
• +۷ مورد دیگر

Dario Colazzo is a Professor at Université Paris-Dauphine, affiliated with the LAMSADE laboratory. His research focuses on database systems and programming languages, particularly in cloud databases, type systems for semi-structured data, and query processing optimizations. He has contributed extensively to XML and XQuery research, including parallel query execution and static analysis techniques. His teaching includes courses on programming (Java), UML, database systems, and semantic web technologies at both undergraduate and graduate levels (in French). Key research interests span XML/XQuery optimization, JSON processing, distributed systems, and type systems for safe and efficient data handling. His work emphasizes scalable cloud-based solutions and efficient query execution models. Publications highlight advancements in parallel query processing (e.g., PAXQuery), type-based optimizations for XML updates, and semantic graph analytics. He has collaborated with institutions like EDBT, WWW, and VLDB conferences.

Athula Dayanart Rajapakseمشاهده پروفایل

استاد

• Power Systems Protection
• Renewable Energy Integration
• HVDC Grid Protection
• +۳ مورد دیگر

Dr. Athula Rajapakse is a Professor in the Department of Electrical and Computer Engineering at the Price Faculty of Engineering, University of Manitoba, Canada. He leads the Intelligent Power Grid Laboratory and holds Fellow status with Engineers Canada, alongside senior membership in IEEE. His research addresses critical challenges in modern power systems, particularly renewable energy integration and grid resilience. His academic foundation includes: B.Sc. (Eng) First Class Honours in Electrical Engineering from the University of Moratuwa, Sri Lanka (1990) M.Eng. in Energy Technology from the Asian Institute of Technology, Bangkok, Thailand (1993) Ph.D. in Quantum Engineering and Systems Science from the University of Tokyo, Japan (1998) Dr. Rajapakse's research centers on power system protection in evolving grid environments, with specialized expertise in transient-based protection schemes , HVDC grid security , and machine learning applications for fault detection. His work bridges theoretical innovation and industry implementation through synchrophasor technology and wide-area monitoring systems, addressing vulnerabilities introduced by inverter-based renewable resources. Analysis of his 2023-2025 publications reveals three dominant thrusts: (1) Machine learning-driven voltage stability prediction using real-time monitoring, (2) Novel protection architectures for HVDC grids and microgrids requiring sub-cycle fault clearance, and (3) Standardized electro-thermal designs for scalable DC circuit breakers in medium-voltage networks. These reflect the industry's urgent need for adaptive protection in grids with high renewable penetration. His recognition includes: Fellow of Engineers, Canada Dr. Rajapakse serves as Associate Editor for the International Journal of Electric Power and Energy Systems (Elsevier) and Technology and Economics of Smart Grids and Sustainable Energy (Springer), while convening the Cigre/IEEE Joint Working Group C4/C2.62 on synchrophasor applications. Though not accepting new graduate students currently, his editorial leadership and working group contributions significantly shape power systems protection standards. His research has secured industry partnerships focused on real-world grid modernization. The Intelligent Power Grid Laboratory under his direction develops hardware-in-the-loop validation platforms for protection schemes, collaborating with Manitoba Hydro and international grid operators to test solutions for renewable-rich systems and remote microgrids.

• Machine Learning
• Neural Networks
• Neuromorphic Computing
• +۳ مورد دیگر

Marcel van Gerven serves as Professor of Artificial Intelligence at Radboud University, leading the Artificial Cognitive Systems laboratory within the Donders Institute for Brain, Cognition and Behaviour. He holds dual Principal Investigator roles at both the Donders Centre for Cognition and the Donders Institute, while directing the ELLIS Unit Nijmegen as an ELLIS Fellow. His research program bridges artificial and natural intelligence through machine learning and neuromorphic computing, with core expertise in neural networks, brain-computer interfaces, and neuroprosthetics for vision restoration. Current work develops brain-inspired AI systems that enhance computational efficiency while modeling biological neural processes, particularly focusing on cortical stimulation safety and real-time adaptive systems. Analysis of his 2025 publications reveals dominant themes in reinforcement learning for neuroprosthetics, anomaly detection frameworks, and spiking neural network applications. His work consistently integrates medical applications including epilepsy regulation, immunotherapy diagnostics, and prosthetic vision enhancement, demonstrating strong translational impact from fundamental AI research to clinical solutions. Scientific recognition includes: Vidi laureate from the Dutch Research Council ELLIS Fellowship for European AI leadership Professor van Gerven directs significant research funding through national and international grants, including the Vidi award. His laboratory develops specialized frameworks like Abmax and Kozax for agent-based modeling while mentoring students in cognitive AI systems, though specific advisees aren't documented in available sources. The Artificial Cognitive Systems lab operates within the Donders Institute ecosystem, collaborating closely with the ELLIS Unit Nijmegen to advance European neuromorphic computing research. Current initiatives focus on biologically plausible learning rules, efficient neural network architectures for embedded systems, and closed-loop neuroprosthetic control systems.

• Distributed Computing
• Grid Computing
• Cloud Computing
• +۳ مورد دیگر

Chronopoulos Antonis is a Professor at the University of Patras, specializing in distributed computing, grid and cloud computing, numerical algorithms, high performance computing, and scientific computing. His research spans both theoretical numerical methods and practical applications in distributed systems. His primary research interests focus on Distributed Computing , Grid and Cloud Computing , Numerical Algorithms , High Performance Computing , and Scientific Computing . His work bridges theoretical numerical methods with practical distributed systems implementations, particularly in the areas of load balancing, parallel iterative methods, and computational efficiency. Analysis of his publication record reveals a career trajectory that began with foundational work in numerical linear algebra and iterative methods in the late 1980s and 1990s, then evolved toward distributed and cloud computing applications in the 2000s and 2010s. His recent work shows increasing focus on healthcare applications of AI and machine learning, particularly in medical diagnostics and decision support systems. His research demonstrates consistent contributions to both theoretical numerical methods and practical distributed computing systems, with recent work expanding into healthcare applications of artificial intelligence.