جست‌وجوی استاد و پژوهشگر

• Embedded Systems
• FPGA Technologies
• Edge Computing
• +۴ مورد دیگر

Professor Roger Woods is a prominent academic and researcher affiliated with Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, part of the Faculty of Engineering and Physical Sciences. He holds the rank of Professor and is actively involved in advancing research and innovation in embedded systems, FPGA technologies, and AI-driven solutions for industry challenges. His work emphasizes practical applications through close collaboration with industry partners. His research interests span novel computing architectures (e.g., multi-precision and edge computing), FPGA-based systems for data analytics, and secure IoT communication protocols. Notably, he co-founded and serves as Chief Scientist of Analytics Engines Ltd, a data analytics company. He has led significant initiatives like the Kelvin-2 Tier-4 High Performance Computing centre and contributed to semiconductor reviews through EFutures. Professor Woods has been recognized with prestigious awards, including the IET Northern Ireland Engineering Excellence Award and IEEE Fellowship. He has supervised numerous PhD students focusing on topics like FPGA-based image processing, secure wireless communications, and embedded AI platforms. His publications reflect interdisciplinary strengths in hardware acceleration, physical layer security, and structural health monitoring. Key Collaborations : Projects with industry and institutions on semiconductor design, bridge monitoring, and AI hardware. Grants : Principal Investigator for multiple research grants, including Core Equipment Awards for advanced instrumentation. Labs/Teams : Active in Queen's Advanced MicroEngineering Centre and the EFutures network.

Heiner Giefersمشاهده پروفایل

استاد

• Cloud Computing
• Hardware Acceleration
• Field-Programmable Gate Arrays
• +۷ مورد دیگر

Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.

Dimitrios Nikolopoulosمشاهده پروفایل

استاد

• High-Performance Computing
• Systems Runtime Systems
• Memory Management
• +۶ مورد دیگر

Dimitrios Nikolopoulos is the John W. Hancock Professor of Engineering at Virginia Tech's College of Engineering, within the Department of Computer Science. His research focuses on high-performance computing, systems runtime systems, memory management, and edge computing. He holds a Chartered Engineer (CEng) certification. Education: M.Eng., University of Patras, Greece (1996) M.Sc., University of Patras, Greece (1997) Ph.D., University of Patras, Greece (2000) Research Interests: His work spans transprecision computing, parallel programming paradigms, efficient inference frameworks for large language models (LLMs), and energy-efficient server ecosystems. Recent efforts emphasize optimizing edge computing systems, GPU resource sharing, and adaptive memory management in cloud-edge environments. Recent Trends in Publications: Recent studies highlight advancements in edge-serving frameworks (e.g., SLED), multi-agent systems for HPC code optimization (MARCO), and novel approaches to GPU and memory resource utilization in constrained settings. Themes include reducing latency, improving scalability, and integrating AI-driven techniques into HPC workflows. Grants & Advising: Details on current grants and advisees are not explicitly listed in the provided materials. Labs/Teams: His research is conducted through collaborative groups within Virginia Tech's Department of Computer Science, focusing on systems, parallel computing, and AI/ML infrastructure.

Sabrina Herbstمشاهده پروفایل

پژوهشگر

• Quantum Computing
• Machine Learning
• High-Performance Computing
• +۳ مورد دیگر

Sabrina Herbst is a PreDoc Researcher at the Department of Computational Sustainability within the Faculty of Informatics at Technische Universität Wien . She works on Quantum Computing , focusing on Machine Learning integration and High-Performance Computing (HPC) augmentation for quantum algorithms. Education : Dipl.-Ing. (2023) in Informatics from TU Wien, with a thesis on quantum machine learning. Her research explores scalable Quantum Machine Learning (QML) algorithms, emphasizing theoretical foundations and robust numerical implementations. Key areas include Quantum Neural Networks (QNNs) , hyperparameter optimization, and the intersection of quantum computing with HPC and edge computing. Recent work investigates channel distinguishability in QNNs and hybrid quantum-classical systems for IoT data processing. Projects she contributes to include HPQC (High Performance integrated Quantum Computing), Themis FWF (Trustworthy and Sustainable Code Offloading), and TRITON FWF (Transprecise Edge Computing). Awards include the Andreas Dieberger–Peter Skalicky Scholarship (2025), Siemens Award for Excellence (2023), and a conference scholarship from TU Wien (2024). Scientific Awards Andreas Dieberger–Peter Skalicky Scholarship (2025) Siemens Awards for Excellence (2023) Conference Scholarship for female PhD students (2024)

Hans Vandierendonckمشاهده پروفایل

استاد

• High-Performance Computing
• Runtime Systems
• Compilation
• +۷ مورد دیگر

Hans Vandierendonck is a Professor in High-Performance and Data-Intensive Computing at Queen’s University Belfast’s School of Electronics, Electrical Engineering and Computer Science. His research focuses on compilers, runtime systems, and architectures for parallel systems, with special attention to programmability, cache architecture, and performance evaluation. He has authored over 150 publications and supervised multiple PhD students, including Jiawen Sun, a finalist in the EPSRC Connected Nation Pioneers competition. His work spans compiler optimization, graph processing, transprecision computing, and blockchain technology. He is a Senior Member of IEEE and ACM, an IEEE Computer Society Distinguished Contributor (2023), and a Fellow of the Higher Education Academy. He has held visiting roles at FORTH, INRIA, and the Universitat Politècnica de Catalunya. His projects include the RELAX Doctoral Network and the Kelvin Living Lab for Net Zero HPC. He teaches courses on energy-efficient computing, concurrent programming, and research projects at both BSc and MSc levels. Key research interests include runtime systems, high-performance computing, energy efficiency, and scalable algorithms for data analytics. His awards include the IBM Belgium Prize (2000, 2004) and Jozef Plateau Prize (2000). He actively reviews for top journals and conferences, including ISCA, SC, and ICS. As part of the LINAS network, he explores interdisciplinary algorithmic solutions for societal impact. His current projects address challenges in graph processing, edge computing, and sustainable wearable systems. Collaborations span academia and industry, reflecting his commitment to bridging theoretical and applied computing.

• Optimization Techniques
• Machine Learning
• High-Performance Computing (HPC)
• +۳ مورد دیگر

Andrea Borghesi is an Assistant Professor (tenure-track) at the Department of Computer Science and Engineering (DISI), University of Bologna, Italy. His research focuses on optimization techniques, Machine Learning, and energy-efficient computing in High-Performance Computing (HPC) systems. He holds an M.S. (2013) and Ph.D. (2017) in Computer Engineering from the University of Bologna, and has been awarded the Italian National Scientific Qualification for Associate Professor in Computer Engineering (2018-2020). **Employment History**: 2020–Present: Executive Scientific Representative at ALMA-AI (Inter-departments Center for Artificial Intelligence, University of Bologna) 2019–Present: Assistant Professor (RTDA) at DISI, University of Bologna 2018–2019: Postdoctoral Fellow at DISI 2017–2018: Research Intern at University of Bologna’s Department of Electrical, Electronic and Information Engineering **Research Interests**: Borghesi’s work addresses complex systems, predictive maintenance, and energy efficiency. Key areas include HPC scheduling, AI-driven monitoring, and interdisciplinary AI applications. His projects span energy-aware scheduling, federated learning for anomaly detection, and AI fairness in healthcare and education. **Grants & Projects**: Lead or contributed to EU-funded initiatives such as AEQUITAS (AI fairness), StairwAI (AI accessibility), and OPRECOMP (transprecision computing), alongside national projects like vertOpt (AI application matching) and EXADATA (exascale monitoring). **Labs & Collaborations**: Active in the Inter-departments Center for Artificial Intelligence (ALMA-AI) and collaborated with institutions like CINECA and industry partners such as ENI and YANMAR. His work integrates AI, optimization, and simulation to solve real-world challenges in computing and healthcare.

Tobias Grosserمشاهده پروفایل

دانشیار

• Compilers
• Programming Language Design
• Performance Programming
• +۸ مورد دیگر

Tobias Grosser is an Associate Professor in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on compiler technology, programming language design, and performance programming, with applications spanning hardware design, climate science, and quantum computing. He leads a research group developing innovative compiler frameworks and tools that bridge theoretical foundations with practical applications. Dr. Grosser completed his undergraduate studies in Computer Science at the University of Passau in Germany and pursued his PhD at École Normale Supérieure Paris as a Google PhD Fellow. Prior to joining Cambridge, he served as a Reader at the University of Edinburgh and held an Ambizione Fellowship at ETH Zurich. His research program centers on rethinking performance programming by re-connecting developers and compilers. He aims to make compilation more modular, predictable, automatic, and trustworthy while bringing open-source compiler innovation to increasingly diverse targets from GPUs to FPGAs and custom hardware. His work spans multiple domains including polyhedral compilation, constraint solving, quantum computing, and hardware design automation. Dr. Grosser is particularly interested in breaking down barriers between compilers and programmers by enabling their interaction through the programming language environment. His recent publications demonstrate a strong focus on compiler infrastructure development, particularly around the MLIR framework. He has pioneered work on Presburger arithmetic optimization with the FPL library, developed new intermediate representations for hardware description and quantum computing, and created tools for compiler education and prototyping like xDSL. His research shows a consistent theme of creating practical, high-performance compiler technologies that address real-world challenges across multiple domains. HiPEAC Technology Transfer Award 2021 for "Fast linear programming through transprecision computing on small and sparse data" OOPSLA 2021 Distinguished Paper Award for "FPL: Fast Presburger arithmetic through transprecision" Dr. Grosser actively mentors PhD students and postdoctoral researchers, currently supervising a team of over a dozen researchers working on various aspects of compiler technology. His group collaborates with industry partners including ARM and Xilinx, and maintains strong ties with the LLVM and MLIR open-source communities. He has secured funding for multiple research projects including work on verified compilation with Lean-MLIR, quantum compiler development, and hardware design automation. His research group operates at the intersection of multiple projects including Open-Source Electronic Design Automation, Seamless design of Smart Edge Processors, Lean-MLIR for verified compilation, FPL for fast Presburger arithmetic, and compilation frameworks for quantum computers. They maintain strong community engagement through regular Compiler Social events in Cambridge and active participation in LLVM developer meetings.

• High-Performance Computing
• Neuromorphic Engineering
• Bio-inspired Systems
• +۷ مورد دیگر

Pedro Filipe Zeferino Tomás is an Associate Professor at the University of Lisbon's College of Engineering, Department of Electrical and Computer Engineering. He is affiliated with INESC-ID research institute and teaches courses on Large Scale Computing, Computer Architecture, and High Performance Computing. His academic career includes a PhD in Neural Code modeling (2009), a Master's in DSP hardware structures (2006), and a Bachelor's in Bio-Inspired Artificial Retina design (2003). His research focuses on neuromorphic engineering, parallel computing architectures, and biosensing applications. Key areas include GPU optimization, transprecision arithmetic, neural prosthetics, and participatory urban biosensing. He has pioneered bio-inspired systems for visual neuroprostheses and developed energy-efficient hardware accelerators for machine learning. His recent work integrates biosensors into urban studies, exploring participatory methods for analyzing affective geographies and walkability. Over 60 peer-reviewed publications demonstrate his contributions to high-performance computing, neural modeling, and interdisciplinary urban technology. Grants: Multiple EU-funded projects in neuromorphic systems and urban biosensing Labs: Leads the Heterogeneous Computing Lab at INESC-ID and collaborates with neural engineering teams

Tobias Grosserمشاهده پروفایل

دانشیار

• Compilers
• Programming Languages
• Performance Programming
• +۷ مورد دیگر

Tobias Grosser is an Associate Professor in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on rethinking performance programming by bridging the gap between developers and compilers. He holds a PhD from École Normale Supérieure Paris and has held positions including Reader at the University of Edinburgh and Ambizione Fellow at ETH Zurich. His research interests span compilers, programming language design, static/dynamic analysis, and the integration of machine learning into compiler development. He emphasizes making compilation more modular, automatic, and trustworthy, with applications in quantum computing, climate science, and open-source hardware. Key projects include xDSL (a Python-native compiler framework), LoopOpt, and the Open Earth Compiler for climate simulations. Recent publications highlight advancements in multi-level intermediate representations (IR), formal verification in MLIR, and performance optimization for GPUs and FPGAs. His work often addresses barriers between programmers and compilers, aiming for intuitive collaboration between developers and automated systems. Tobias mentors a dynamic team of PhD students, postdocs, and researchers, including notable contributors like Siddharth Bhat, Arjun Pitchanathan, and Mathieu Fehr. His lab focuses on compiler toolchains for domain-specific hardware accelerators, quantum computing ecosystems, and verified compilation techniques.