جست‌وجوی استاد و پژوهشگر

• Error-Correction Coding
• FPGA/ASIC Design
• Multicore Architectures
• +۳ مورد دیگر

Christophe Jégo is a researcher specializing in hardware/software co-design for communication systems, with affiliations suggesting connections to Université de Bordeaux's College of Engineering. His core expertise spans error-correction coding (LDPC, polar, turbo codes), FPGA/ASIC implementations, and high-performance computing architectures for telecommunications. Research interests focus on: Hardware acceleration of communication algorithms (LDPC/polar decoders, FFT processors) Multicore/GPU optimization for real-time signal processing RTL design automation tools and high-level synthesis frameworks 5G physical layer implementations including MIMO detection Resource-constrained embedded systems for IoT and space applications Publication analysis reveals strong emphasis on: FPGA implementations of communication blocks (67% of recent works) Parallel processing techniques for LDPC/polar decoding (2014-2025) Open-source tool development for hardware design (AFF3CT, Odatix) Cross-layer optimization from algorithms to silicon implementation

• Decentralized Systems
• Distributed Systems
• Privacy and Anonymous Communication
• +۷ مورد دیگر

Prof. Bryan Alexander Ford is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Decentralized and Distributed Systems (DEDIS) lab within the School of Computer and Communication Sciences, Department of Computer Science. He also holds teaching positions in SIN (Systems and Networking) and SSC (Security and Software Composition) at EPFL, and serves on the Open Science Strategic Committee. His academic journey includes faculty positions at Yale University following his Ph.D. at MIT. Dr. Ford's research spans multiple domains with a primary focus on building secure decentralized systems. His work encompasses privacy and anonymous communication systems like Dissent, systems security, blockchain technology, and novel approaches to operating systems for deterministic parallel computing such as Determinator. He has made seminal contributions to parsing theory through his development of Parsing Expression Grammars (PEGs) and packrat parsing algorithms, which provide linear-time parsing with backtracking capabilities. His research also extends to networking protocols including the Unmanaged Internet Architecture and Structured Stream Transport, as well as virtualization technologies like VX32. Ford's publication record demonstrates a remarkable evolution from foundational work in parsing and programming languages to cutting-edge research in decentralized systems and security. His early career focused on operating systems theory, parsing algorithms, and language design, culminating in influential papers on packrat parsing and PEGs. More recently, his work has shifted toward practical decentralized systems, blockchain technology, and security architectures, while maintaining connections to programming language theory through projects like Matchertext and MinML. This trajectory reflects both continuity in his interest in system architecture and a strategic pivot toward emerging challenges in decentralized computing. As an educator and mentor, Ford advises multiple PhD students at EPFL through the EDIC program and welcomes prospective students and researchers to join his lab. His teaching includes courses on decentralized systems engineering and technologies for democratic society, reflecting his commitment to both technical rigor and societal impact of computing technologies.

Lizy Kurian Johnمشاهده پروفایل

استاد

• Computer Architecture
• Multicore Processors
• Memory Systems
• +۵ مورد دیگر

Dr. Lizy Kurian John is a Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin, holding the Truchard Foundation Chair in Engineering. Her research focuses on computer architecture, multicore processors, memory systems, and machine learning workloads. Endowed Professorship: Cullen Trust for Higher Education Editor-in-Chief: IEEE MICRO (2019-2023) Professional Affiliations: IEEE Fellow, ACM Fellow, National Academy of Inventors Fellow Her research spans performance evaluation, workload characterization, and reconfigurable computing, with significant contributions to GPU architectures and deep learning infrastructure. Publications highlight advancements in heterogeneous systems and energy-efficient computing. Scientific Awards : NSF CAREER award (1996) IEEE Fellow (2009) ACM Fellow (2020) Best Paper ACM DAC (2016) Best Paper Runner-up IEEE ICPP (2015) She has secured grants from NSF, SRC, DARPA, and industry leaders like IBM, Intel, and Samsung, while mentoring students through refereed publications and editorial leadership in top journals.

Bernhard Scholzمشاهده پروفایل

استاد

• Programming Languages
• Compilers
• Static Analysis
• +۴ مورد دیگر

Bernhard Scholz is a Professor at the University of Sydney's School of Computer Science. His research focuses on programming languages, compilers, static analysis, and blockchain technologies, with significant contributions to Datalog optimization and smart contract security. He teaches COMP3109 Programming Languages and Paradigms and leads research in declarative programming frameworks. Scholz's primary research interests include: Development of Soufflé, a Datalog-based program analysis framework Ethereum smart contract security through tools like Ethainter and MadMax Parallel data structures for efficient Datalog evaluation Compiler optimizations for embedded systems and cloud environments His recent publications demonstrate a consistent focus on improving the efficiency and security of declarative programming systems, particularly through innovations in Datalog execution and smart contract analysis. This work bridges theoretical computer science with practical applications in blockchain and distributed systems. Scholz has secured research funding including an ARC Discovery Project on adaptive key-value stores and a Fantom Operations grant for smart contract toolchain development. He collaborates with international researchers on parallel computing and blockchain verification projects.

Anh Vu Doanمشاهده پروفایل

مدرس

• Embedded Systems Design
• Combinatorial Optimization
• Problem Modeling
• +۳ مورد دیگر

Dr. Ir. Anh Vu Doan is a Lecturer at the Technical University of Munich (TUM) under the Chair of Integrated Systems and a Senior Project Leader at Infineon in Neubiberg, Germany. With a Belgian-Vietnamese background and prior residence in Japan, he has held academic roles including postdoctoral fellowships at Keio University and TUM, as well as teaching assistant and stand-in lecturer positions at Université libre de Bruxelles (ULB) and IÉSEG School of Management. Research Interests: Embedded systems design Combinatorial optimization Problem modeling and decision aiding Approximate computing and 3D-stacking memory Machine learning reliability and safety Network-on-Chip (NoC) optimization Recent Publications focus on neuromorphic systems, power optimization for multicore processors, adversarial attacks in ML, and multi-objective design strategies using genetic algorithms. His work bridges hardware-software co-design and sustainable mobility decision frameworks. Scientific Awards: Erasmus-Mundus Grant (EASED program) JST/CREST Research Program Education: MSc in Electrical Engineering (ULB, 2009) PhD in Engineering Sciences (ULB, 2015)

Shih-Hao Hungمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Processing
• Performance Evaluation
• +۷ مورد دیگر

Shih-Hao Hung is a Full Professor in the Department of Computer Science and Information Engineering at National Taiwan University, where he has served since 2005. He established the Performance, Applications and Security Lab (PAS Lab) and previously served as Section Director in the Computer and Information Network Center of NTU from 2012 to 2016. Before joining NTU, he worked for 5 years at Sun Microsystems in the San Francisco Bay Area. Professor Hung's research focuses on computer architecture, parallel processing, performance evaluation, and optimization. His work spans embedded systems, mobile applications, cloud computing, business applications, and information security. Current research interests include heterogeneous and multicore system architecture, active utilization of open source system software, and high performance deep learning systems. He has published numerous papers on GPU acceleration, heterogeneous computing, malware detection, and system performance optimization. His research has resulted in several notable publications and two Best Paper Awards (RACS 2014 and RACS 2017). Professor Hung maintains strong industry collaborations with leading technology companies including IBM, Intel, AMD, NVIDIA, ARM, MediaTek, and Foxconn, focusing on practical applications of advanced computing technologies. Best Paper Award at RACS 2017 for Program Analysis research Best Paper Award at RACS 2014 for Android malware detection framework Extensive publications in performance evaluation and optimization Significant industry collaborations across multiple technology sectors Professor Hung is known for his practical approach to computer science education and research, emphasizing the importance of bridging academic knowledge with real-world applications. His blog frequently discusses issues in computer science education, system architecture, and the development of skilled professionals in Taiwan's technology sector.

Haluk Topcuogluمشاهده پروفایل

استاد

• Multicore Architectures
• Parallel Algorithm Design
• Fault Tolerant Computing
• +۵ مورد دیگر

Haluk Topcuoglu is a Full Professor in the Department of Computer Engineering at Marmara University's Faculty of Engineering. He holds a PhD from Syracuse University (1999) and has been at Marmara University since 1999, progressing through roles as Assistant, Associate, and Full Professor. His research focuses on multicore architectures, parallel algorithms, fault tolerance, dynamic optimization, and hybrid evolutionary algorithms. He has led multiple funded projects, including TUBITAK initiatives on reliability optimization and sensor placement. Education: PhD in Computer Science (Syracuse University, 1999), MSc and BSc in Computer Engineering (Boğaziçi University, 1993 and 1991). Research Interests: Task scheduling for multicore systems, reliability-aware computing, dynamic optimization, and applications of evolutionary algorithms in cloud/fog computing. His work emphasizes balancing performance, energy efficiency, and fault tolerance in parallel systems. Awards include the 2008 IBM Faculty Award, 2010 IEEE ISDA Best Paper Award, and Marmara University's 2012 Publication Impact Award. He has supervised 4 PhD and 19 MSc theses, with ongoing research in machine learning-assisted metaheuristics and dynamic monitor selection. Editorial roles include Cluster Computing Journal (SCI-Expanded) and Journal of Aeronautics and Space Technologies. He teaches courses on evolutionary computing, multicore computing, and parallel processing. Current projects include cross-layer reliability frameworks and scheduling algorithms for fog computing, reflecting his focus on industry-relevant computational challenges.

William T. Baumannمشاهده پروفایل

دانشیار

• Computational Biology
• Biomedical Applications
• Control Systems
• +۳ مورد دیگر

William T. Baumann is an Associate Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He holds a Ph.D. from Johns Hopkins University (1985), an M.S.E.E. from MIT (1980), and a B.S.E.E. from Lehigh University (1978). His research focuses on computational biology, biomedical applications, and control systems, particularly in modeling biological systems dynamics such as breast cancer cell resistance mechanisms and stochastic processes in yeast cell cycles. He has pioneered work on integrating control theory with biological systems to predict therapeutic outcomes and optimize treatment strategies. His teaching interests include control system design, microprocessor systems, and system identification. Research interests span dynamic modeling of cancer cell behavior, endocrine therapy resistance, and multiscale modeling of cellular processes. Recent projects involve predicting breast cancer proliferation dynamics and analyzing the unfolded protein response in cancer cells. He has also contributed to systems biology frameworks for understanding cell cycle regulation and autophagy-inflammation interactions in ER+ breast cancer. His articles emphasize computational modeling in oncology and control systems security, including frameworks for distributed simulation load balancing and secure industrial control architectures. While no specific awards are listed, his work demonstrates significant contributions to interdisciplinary research at the intersection of engineering and biology. Advising and grant activities are not explicitly detailed here, though his extensive publication record suggests active research supervision and collaborative grant efforts. His lab focuses on applying engineering principles to biomedical challenges, with potential collaborations in cancer research and computational systems biology.

• Computer Architecture
• Multi-core Systems
• Fault-Tolerant Computing
• +۴ مورد دیگر

Dr. Işıl Öz is an Assistant Professor in the Computer Engineering Department at Izmir Institute of Technology (IZTECH). She holds a B.Sc. and M.Sc. in Computer Engineering from Marmara University and a Ph.D. from Boğaziçi University. Before joining IZTECH in 2018, she worked as a postdoctoral researcher and ERCIM/Marie Curie Fellow at the Swedish Institute of Computer Science (SICS). Her research focuses on computer architecture, performance optimization, and reliability of multi-core systems, with a strong emphasis on fault-tolerant computing and GPGPU applications. Key projects include the TÜBİTAK-funded FTGPGPU initiative analyzing hardware fault tolerance for GPU applications. She teaches courses such as Operating Systems, Advanced Computer Architecture, and Parallel Programming Patterns. Her work spans machine learning-based vulnerability prediction, compiler design for reliable execution, and approximation techniques for efficient parallel computing. Publications appear in venues like ACM Computing Surveys, The Journal of Supercomputing, and International Journal of Parallel Programming. Awards: ERCIM/Marie Curie Fellowship Labs/Teams: Parsiyte Research Group (focused on parallel and distributed systems) Grants: TÜBİTAK ARDEB 3501 (FTGPGPU project)

• 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.

Dean M. Tullsenمشاهده پروفایل

استاد

• Computer Architecture
• Multithreading
• Multicore Processors
• +۷ مورد دیگر

Dean M. Tullsen is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego. He leads the High Performance Processor Architecture and Compilation Lab and focuses on computer architecture, particularly multithreading, multicore processors, secure execution, and energy-efficient design. His research bridges hardware and software for parallel computing, security, and resource optimization. Education: Ph.D. from the University of Washington (1996), B.S. and M.S. from UCLA. Research Interests: Computer architecture, multithreading, speculative execution, hardware security, energy efficiency, and heterogeneous computing. Scientific Awards: Best Paper Awards at USENIX Security Symposium (2024), IEEE S&P (2023), and Distinguished Paper Awards at POPL (2021), ASPLOS (2023), plus the 2020 ACM SIGARCH/IEEE-CS TCCA Influential ISCA Paper Award. Students: Advised 30+ graduate students, including Craig Disselkoen, Mohammadkazem Taram, and Ashish Venkat, who now hold positions at Google, Microsoft, and academia. Lab: Leads the High Performance Processor Architecture and Compilation Lab, advancing techniques in secure execution and parallel computing.

• Compiler Optimization
• Static Program Analysis
• Datalog Systems
• +۵ مورد دیگر

Professor Bernhard Scholz is a faculty member in the School of Computer Science at the University of Sydney . He specializes in compiler optimization, static program analysis, Datalog systems, blockchain technology, and parallel processing. His research focuses on improving compiler efficiency, analyzing complex programs, and developing tools for smart contract security. He holds a professorial position and has been involved in significant projects like the Fantom blockchain partnership, which funds specialized blockchain research. His work includes developing the Soufflé Datalog compiler and tools like MadMax for smart contract analysis. Research interests span compiler design, formal verification, distributed systems, and energy-efficient computing. His recent work emphasizes blockchain security, Datalog scalability, and parallel execution optimization. Notable contributions include EVMTracer for Ethereum analysis and Julia Cloud Matrix Machine for cloud-based matrix computations. Key grants include ARC Discovery Projects on heterogeneous systems and smart contracts. Awards include industry partnerships and sustained research funding. He advises on large-scale program analysis and has collaborated globally with institutions like ETH Zurich and Fantom Foundation. Labs and collaborations include the University of Sydney's Blockchain Lab and Fantom-funded projects. His work bridges theoretical compiler research with practical applications in security and distributed systems.

Jason Marsمشاهده پروفایل

استاد

• Computer Architecture
• Warehouse-Scale Computing
• Machine Learning Systems
• +۳ مورد دیگر

Jason Mars is a Professor at the University of Michigan's Department of Electrical Engineering and Computer Science, leading the Clarity-Lab. His research focuses on cross-layer architectures and runtimes for future computing systems, particularly in warehouse-scale computers. He has pioneered projects like Sirius (an open voice/vision personal assistant), Protean Code (dynamic code transformations), and Adrenaline (tail latency management). His work addresses challenges in energy efficiency, resource allocation, and performance optimization in large-scale systems. Key contributions include architectural designs for AI services, thermal management strategies, and compiler techniques for approximate computing. Mars collaborates with industry leaders like IBM and has advised numerous students who have published at top conferences such as ASPLOS and HPCA. Recent research spans AI ethics, scalable programming models (e.g., Data Spatial Programming), and edge-cloud collaboration frameworks like Neurosurgeon. He has delivered invited talks at Baidu, IBM Watson, and conferences worldwide, emphasizing the societal impact of computing systems.

Gerd von Cöllnمشاهده پروفایل

استاد

• Energy-Efficient Systems
• Embedded Systems
• Cyber-Physical Systems
• +۵ مورد دیگر

Prof. Gerd von Cölln is a Professor of Computer Architecture at the School of Technology, Electrical Engineering and Computer Science, University of Applied Sciences Emden/Leer. His primary focus is on energy-efficient and autonomous systems, particularly in embedded and cyber-physical systems. He leads research in energy-autonomous sensors, low-power electronics, and IoT applications. Education details are not explicitly provided in the text, but his academic role suggests advanced qualifications in electrical engineering or computer science. His research projects include Sichere Offshore-Operationen (SOOP), Energieautarke Sensorsysteme (ID3AS), and LoRaWAN-based sensors (ELVIS), demonstrating expertise in industrial automation, sensor networks, and energy harvesting. Publications emphasize sensor design, wireless communication protocols, and optimization algorithms for complex environments. While no awards are listed, his extensive project portfolio highlights contributions to applied engineering research. Advising details are unavailable, but his role implies supervision of technical projects involving students and industry partners. Prof. von Cölln collaborates on labs and teams focused on maritime automation, industrial IoT, and embedded systems, with projects addressing challenges like harsh environmental conditions and energy sustainability.

Nektarios Kozyrisمشاهده پروفایل

استاد

• Parallel and Distributed Systems
• Computer Architecture
• High-Performance Computing
• +۵ مورد دیگر

Nektarios Kozyris is a Professor of Computer Science and former Dean of the School of Electrical and Computer Engineering at the National Technical University of Athens (NTUA). He holds a PhD and Diploma in Electrical and Computer Engineering from NTUA. His research focuses on parallel and distributed systems, computer architecture, high-performance computing, and cloud infrastructure. He has authored over 180 papers and contributed to major projects like the Okeanos public cloud and Synnefo open-source software. Kozyris has led EU initiatives such as ACTiCLOUD and EuroEXA, and advises startups like Arrikto. He is a senior member of the ACM, IEEE Computer Society, and co-founder of the Greek Free/Open Source Software Society (GFOSS). His academic roles include teaching courses on computer architecture, operating systems, and distributed systems. Awards include Best Paper Awards at IPDPS 2001 and CCGRID 2013, and Intel's recognition for research in transactional memory. He actively contributes to conferences as a PC member and co-chair, and has supervised numerous PhD students in areas like distributed systems and sparse matrix computations. Kozyris pioneered the ~okeanos cloud infrastructure and advanced storage systems like Pithos. His work on GridTorrent and XOROS demonstrates innovation in data management and peer-to-peer systems. Current research includes elastic resource provisioning, quantum machine learning, and FPGA-based acceleration. His labs at NTUA and collaborations with institutions like ICCS and IMIS drive advancements in computing systems and software engineering.