جستجوی استاد راهنمای دانشگاهی

Adrian Sampsonمشاهده پروفایل

دانشیار

• Computer Architecture
• Programming Languages
• Hardware-Software Co-Design
• +۴ مورد دیگر

Adrian Sampson is an Associate Professor in the Department of Computer Science at Cornell University, where he is part of the Computer Systems Laboratory and the Programming Languages Group. He leads the Capra research group, focusing on programming languages, computer architecture, and hardware-software co-design. Previously, he was an Assistant Professor at Cornell (2016–2022) and a researcher at Microsoft and Google. Education: Ph.D. in Computer Science and Engineering from the University of Washington (2015), advised by Luis Ceze and Dan Grossman. Dissertation: Hardware and Software for Approximate Computing . B.S. in Computer Science from Harvey Mudd College (2009). Research interests include approximate computing, compiler optimization, hardware accelerators, and formal methods in systems programming. His work on Geometry Types and Reticle has advanced abstractions for graphics programming and FPGA design. Sampson is known for contributions to compiler infrastructure for accelerators, such as the Timeline Types framework for predictable hardware design. Awards include the Cornell Bowers CIS Research Excellence Award (2024), IEEE TCCA Young Computer Architect Award (2021), NSF CAREER Award (2019), and the Google Ph.D. Fellowship in Computer Architecture (2013–2015). He has advised over 30 graduate and undergraduate students, contributing to projects like compiler-driven accelerator simulation and vectorization techniques. Teaching includes courses on compilers, programming languages, and computer architecture at Cornell. He has served on program committees for major conferences (ASPLOS, PLDI, ISCA) and chairs roles in ISCA and ASPLOS. Sampson’s research is supported by grants from NSF, Google, and industry partnerships.

• Complex Systems
• Embedded Systems
• Multiphysical Systems
• +۹ مورد دیگر

Omar Hammami is a Senior Lecturer at ENSTA Paris, affiliated with the Computer Science and Systems Engineering Unit (U2IS). He has been an Enseignant-Chercheur (Teacher-Researcher) at U2IS since 2000. Previously, he served as an Associate Professor in Japan from 1993 to 2000, where he led the performance evaluation laboratory. Between 1991 and 1993, he held temporary teaching-research roles at ENSEEIHT, INSA Toulouse, and ENSICA. Education: HDR (Habilitation à Diriger des Recherches) in Physics from the University of Orsay Doctoral Thesis in Computer Science from Paul Sabatier University in Toulouse Research Interests: His work focuses on complex systems, embedded systems, and multiphysical systems, with emphasis on systems engineering, theoretical analysis of complexity, and design space exploration (DSE). He also contributes to multidisciplinary optimization (MDAO) and develops applications for civil and military autonomous systems, including UAVs and robotics. His expertise spans electronics, microelectronics (ASIC circuits, FPGA), and manycore/multicore architectures. Scientific Awards: Prizes awarded to several articles at international conferences Labs and Teams: Omar has contributed to the performance evaluation laboratory in Japan and is currently part of the U2IS unit at ENSTA Paris. His research aligns with the Autonomous Systems and Robotics (ARS) axis, addressing industrial challenges in system design and reliability.

• Computer Architecture
• Machine Learning and Artificial Intelligence
• Embedded Systems
• +۴ مورد دیگر

Daniel Bates is a Senior Research Associate at the University of Cambridge's Department of Computer Science and Technology. His research focuses on Computer Architecture and Machine Learning, emphasizing co-optimization of hardware and software. Key projects include the Loki manycore processor (with 128 cores fabricated in 2018) and the Muntjac RISC-V multicore processor, designed for simplicity and extensibility. Bates explores energy-efficient machine learning optimizations like dynamic gating and network architecture search. He teaches courses including Part IA Algorithms, Part IB Computer Architecture, and Part II Advanced Computer Architecture. His supervision style prioritizes student feedback and clarity, offering tailored support and prompt communication. Bates has contributed to over a dozen peer-reviewed publications since 2013, addressing topics from neural network security to embedded system optimization. His work bridges theoretical research and practical hardware implementation, with a focus on open-source contributions like the Muntjac processor. He actively collaborates with industry through the University's research initiatives, emphasizing real-world applications of computer architecture advancements.

Matthias Janetschekمشاهده پروفایل

دانشیار

• Computer Science
• Cybersecurity
• Distributed Systems
• +۵ مورد دیگر

Matthias Janetschek holds the position of Associate Professor at the Management Center Innsbruck (MCI), Austria, since September 2020. Previously, he served as a Lecturer at MCI (2018–2020) and was a Co-Founder/Senior Developer at Felix Solution UG (2013–2015). He earned a PhD in Computer Science from the University of Innsbruck (2011–2019), alongside earlier degrees in Computer Science (BSc 2002–2007, MSc 2007–2011). His research focuses on distributed systems, workflow management, cybersecurity, and IoT , with notable contributions to scientific workflow execution on cloud/manycore architectures and AR/VR applications in education. He has supervised over 18 bachelor/master theses addressing topics like IoT security, reinforcement learning, and edge computing. Teaching responsibilities include courses such as Distributed Systems , Virtual Reality , and Computer Architecture at MCI. He has also completed further training in online education tools (e.g., Sakai, Adobe Connect) and corporate communication. His publications span journals like Multimodal Technologies and Interaction and conference proceedings on workflow systems, cloud computing, and AR usability. Current work emphasizes large-scale IoT security assessments and edge computing optimizations.

Andrew G. Gerberمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• High-Performance Computing
• Multiphase Flows
• +۲ مورد دیگر

Dr. Andrew Gerber is Professor in Mechanical Engineering at the University of New Brunswick. His research specializes in computational methods for fluid dynamics, including manycore high-performance computing applications, non-equilibrium thermodynamics, and multiphase flow modeling. Recent publications focus on algorithmic optimization for hydrodynamic simulations and turbulence modeling. Research encompasses: Parallel computing architectures for fluid dynamics Advanced numerical methods (URANS, Eulerian models) Industrial applications (submarine maneuvering, mixing vessels) Turbulent droplet diffusion phenomena Publication analysis shows consistent focus on enhancing computational efficiency across 15+ studies. Recent work demonstrates innovations in parallel algorithms, turbulence modeling, and industrial fluid dynamics applications. Technical contributions include accelerated simulation techniques validated through experimental comparisons.

• High-Performance Computing
• Graph Algorithms
• Social Network Analysis
• +۳ مورد دیگر

Johannes Langguth is an Associate Professor at the Department of Informatics, University of Bergen. His work focuses on high-performance computing, graph algorithms, and social network analysis. He has contributed to GPU acceleration techniques for cardiac simulations and graph processing on manycore architectures. Langguth's research intersects computational science with societal challenges, including misinformation detection in social media and conspiracy theory analysis. He leads projects on temporal interaction networks and has developed datasets like COCO and GECO to study pandemic-related disinformation. His interdisciplinary approach combines computer science with psychology and public health, addressing both technical and human aspects of information dissemination. Notable contributions include optimizing breadth-first search on Graphcore IPUs and applying graph neural networks for combinatorial problems. Langguth collaborates widely, with affiliations at Simula Research Laboratory and BI Norwegian School of Business. His work is funded by institutions like the Research Council of Norway and the EU, reflecting its applied and impactful nature in both academia and industry.

• Computer Architecture
• High-Performance Computing
• Network-on-Chip (NoC)
• +۷ مورد دیگر

Vassilis D. Papaefstathiou is a researcher in computer architecture and high-performance computing, affiliated with the Department of Computer Science at the University of Crete and the Institute of Computer Science at FORTH-ICS. His work focuses on energy-efficient manycore systems, network-on-chip design, FPGA prototyping, and RDMA-based communication. His educational background includes a Ph.D. (2013), M.Sc. (2005), and B.Sc. (2002), all from the University of Crete. His research spans advanced topics in computer systems, including hybrid memory management, cache optimization, and scalable interconnects. His research interests include Computer Architecture , Network-on-Chip (NoC) , Reconfigurable Computing , Energy-Efficient Computing , RDMA , and High-Performance Computing . He has made significant contributions to FPGA-based prototyping of manycore systems and low-latency interconnects. His recent publications demonstrate a strong trend in optimizing on-chip networks, memory hierarchies, and communication mechanisms for performance and energy efficiency. Key areas include dual data-rate NoCs, hybrid memory systems with intelligent data migration, and RDMA-enhanced architectures. His work often integrates hardware-software co-design principles for scalable and efficient computing. HiPEAC Paper Award (2012) HiPEAC Paper Award (2016) HiPEAC Paper Award (2017) HiPEAC Paper Award (2020) Best Paper Award Finalist at IEEE/ACM NOCS 2018 He has advised several researchers and students, including Antonis Psistakis, Evangelos Vasilakis, and Ahsen Ejaz, who have co-authored significant publications with him. His collaborative projects include SARC, ExaNeSt, and ECOSCALE, often funded by EU initiatives or research councils. These projects focus on next-generation HPC systems, reconfigurable computing, and scalable architectures. He is a key contributor to the Formic FPGA prototyping platform and has worked extensively on RDMA-capable NICs, cache-integrated network interfaces, and virtualized communication systems. His work is deeply embedded in international research consortia and high-impact venues.

Margaret Martonosiمشاهده پروفایل

استاد

• Computer Architecture
• Quantum Computing
• Power-Efficient Systems
• +۳ مورد دیگر

Margaret Martonosi is the William M. Addy ’82 University Professor of Computer Science at Princeton University, a position she has held since 1994. Her research focuses on computer architecture, with significant contributions to both classical and quantum systems. She has led major initiatives such as co-founding the ACM CARES movement and served as Director of the NSF Directorate for Computer and Information Science and Engineering (CISE) from 2020 to 2024. Her work emphasizes power-efficient designs, quantum hardware/software co-design, and scalable hybrid computing frameworks. Her academic leadership includes roles in broadening participation in computing through the Computing Research Association (CRA-W) and mentorship recognized by awards like the ACM Frances E. Allen Award. She has authored over 200 papers, with impactful contributions in security (e.g., SoCurity frameworks), quantum architectures (e.g., ARQUIN, HetArch), and heterogeneous computing (e.g., DCRA chiplet systems). Her recent research addresses challenges in quantum-classical integration and post-Moore’s Law computing paradigms. Publications highlight innovations like TensorQC for hybrid quantum-classical systems and CutQC for distributed quantum circuit optimization. Her NSF leadership shaped federal computing research priorities, emphasizing quantum science, AI, and cyberinfrastructure. She maintains active roles in technical societies and serves on non-profit boards focused on advancing computing equity and accessibility. Key grants include the $115M quantum science center (with Princeton’s electrical engineering collaboration) and EPiQC (Enabling Practical-Scale Quantum Computation). Her lab develops tools like MosaicSim for agile hardware-software co-design and RTLCheck for verifying hardware consistency models. Leadership Roles: NSF CISE Director (2020–2024), ACM CARES Co-founder, CRA-W Board Key Projects: Quantum-Supercomputing Integration, Power-Efficient Architectures, Formal Verification Frameworks Recent Focus: Quantum Benchmarking (SuperMARQ), Edge Computing Security, Chiplet-Based Systems

Murray Coleمشاهده پروفایل

استاد

• Parallel Programming
• Skeletons in Parallel Computing
• Heterogeneous Systems
• +۷ مورد دیگر

Professor Murray Cole is a faculty member at the School of Informatics , University of Edinburgh, holding the Personal Chair of Patterned Parallel Computing. He is affiliated with the Institute for Computing Systems Architecture and co-directs the Centre for Doctoral Training in Pervasive Parallelism . As a PGR Personal Tutor, he provides academic support for research students. His research focuses on parallel programming models , particularly skeletons—abstractions for structuring parallel programs. Key areas include performance portability across architectures (GPUs, manycore, clusters), dynamic optimization, and domain-specific languages. Projects like Transmuter and CoSPARSE highlight his work in reconfigurable hardware and graph analytics. He contributes to postgraduate teaching through the course Parallel Programming Languages and Systems . While no explicit scientific awards are listed, his leadership in major EPSRC-funded projects underscores his impact in compiler design and computer architecture.

• Parallel computer architecture
• Hardware/software codesign
• Edge learning of neural networks
• +۳ مورد دیگر

Yicheng Zhang is a Researcher at the Technical University of Munich within the School of Computation, Information and Technology , specifically affiliated with the Department of Computer Engineering . He works under the IH02 Chair of Computer Architecture and Operating Systems (CAOS) and the I10 Chair of Computer Architecture and Parallel Systems (CAPS). Research Focus: Parallel computer architecture, Hardware/software codesign, Edge learning of neural networks Teaching: Courses include Computer Architecture (INHN0003), Operating Systems, Hardware Design Languages, Ethics for Nerds, and History of Computer Science His publications focus on edge learning of recurrent neural networks (RNNs) and scalable hardware architectures for neural network training. Zhang actively seeks students for guided research projects related to manycore architectures for irregular parallelism.

Yonghong Yanمشاهده پروفایل

دانشیار

• Parallel Computing
• High Performance Computing
• Parallel Programming Models
• +۳ مورد دیگر

Yonghong Yan is an Associate Professor in the Department of Computer Science at the University of North Carolina at Charlotte. His research focuses on system-level innovations in computer science, particularly in parallel and high performance computing, compiler techniques, and computer architecture. Ph.D. Institution: University of Houston His research group, the High Performance Computing Architecture and System Lab , explores advanced programming models like OpenMP for heterogeneous and manycore systems, as well as addressing power and memory constraints in modern computing architectures. The lab’s work bridges system design, compiler optimization, and hardware-software co-design. For more information, visit his personal website at https://passlab.github.io/yanyh/ .

• Computer Architecture
• Memory Hierarchies
• Cache Coherence Protocols
• +۵ مورد دیگر

Ricardo Fernández Pascual is an Associate Professor at the Computer Engineering Department (DITEC) of the Universidad de Murcia , Spain. He teaches introductory and advanced computer architecture courses like ' Estructura y Tecnología de Computadores ' and ' Organización y Arquitectura de Computadores '. His academic work focuses on computer architecture , particularly in memory hierarchies for chip multiprocessors cache coherence protocols fault tolerance energy-efficient design His PhD thesis (2009) at the Universidad de Murcia, titled ' Fault-tolerant Cache Coherence Protocols for CMPs ', was supervised by José Manuel García Carrasco and Manuel Eugenio Acacio Sánchez. He has also collaborated with institutions like Intel Barcelona Research Center and FORTH. Recent research trends in his publications include optimizing coherence directories for manycore scalability hybrid photonic-electronic interconnects fault-tolerant mechanisms in CMP architectures transactional memory enhancements dynamic resource management private-shared cache organization He has advised PhD student Antonio García Guirado, who completed his thesis on energy-efficient cache-coherent multi-cores in 2013. His work has been published in leading journals like IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel Distributed Computing, and conferences including ICS, HPCA, and SBAC-PAD.

Michael Uelschenمشاهده پروفایل

استاد

• Software Engineering
• Embedded Systems
• Multicore/Manycore Computing
• +۵ مورد دیگر

Prof. Dr.-Ing. Michael Uelschen is a Professor of Software Engineering for Technical Systems at Osnabrück University of Applied Sciences, part of the Faculty of Engineering and Computer Science. He holds a PhD in Mechanical Engineering and has over a decade of industry experience in automotive software development. His research focuses on energy-efficient embedded systems, model-driven software engineering, multicore systems, and medical technology applications such as motion analysis for rehabilitation. He teaches courses in software engineering, embedded systems, and parallel algorithms at both bachelor's and master's levels. Research Interests : Software Engineering for Automotive Embedded Systems, Power Optimization, Energy-Aware Design Patterns, Medical Motion Analysis (gait stability), IoT Sustainability, and Process Improvement (CMMI). His work often bridges theoretical models with practical implementation in real-time systems. Key Projects : Developed a contactless monitoring system for elderly/patient care using commodity electronics, supported by Bayer Healthcare Grants4Apps. Also contributed to frameworks for energy-efficient embedded systems design patterns and algorithms for kinematic analysis in clinical settings. Teaching : Leads courses like 'Software Engineering for Parallel Systems', 'Embedded Systems', and 'Object-Oriented Design'. Supervises student projects and theses in automotive and medical software domains.

• Computer Architecture
• Heterogeneous Parallel Systems
• GPU Computing
• +۴ مورد دیگر

Juan Luis Aragon Alcaraz is a Full Professor in Computer Architecture at the University of Murcia (Spain), affiliated with the Faculty of Informatics and the Department of Computer Engineering and Technology. He obtained his PhD in Computer Engineering from the University of Murcia in 2003 and held a postdoctoral position as a Visiting Assistant Professor and Researcher at the University of California, Irvine. He has held visiting researcher positions at EPFL (2013) and Princeton University (2015–2022). His research focuses on computer architecture, emphasizing heterogeneous parallel systems, GPUs, memory hierarchies, and energy-efficient microarchitecture design. He has advised 7 PhD theses and co-authored over 60 publications in top-tier conferences and journals. His work includes contributions to GPU-accelerated medical imaging, energy-efficient graphics pipelines, and hardware-software co-design for parallel systems. Key research trends in his articles include optimizing GPU performance for real-time applications, energy efficiency in graphics rendering, and scalable latency tolerance in manycore systems. His patents include innovations in presbyopia correction devices leveraging GPU and FPGA technologies. Dr. Aragón has taught numerous courses in computer architecture and design, including Computer Architecture and Organization, Advanced Aspects in Multicore Architectures, and Embedded Systems Technologies. His labs and collaborations span academic-industrial partnerships, emphasizing practical applications of his theoretical contributions.

Piotr Arłukowiczمشاهده پروفایل

مدرس

• 3D Graphics
• Scientific Visualization
• Parallel Computing
• +۲ مورد دیگر

Piotr Arłukowicz is a Lecturer in Software Engineering at the University of Gdańsk, specializing in 3D graphics and computational visualization. He mentors students in graphics programming, animation technologies, and parallel computing frameworks. His applied research includes: Scientific visualization for STEM education High-performance computing architectures Special effects for film production Blender-based educational tools He develops interdisciplinary applications bridging computer science with physics and mathematics education through visual simulation techniques.