جست‌وجوی استاد و پژوهشگر

Robert James Stewartمشاهده پروفایل

دانشیار

• Computer Architecture
• Functional Programming Languages
• Field-Programmable Gate Arrays (FPGAs)
• +۶ مورد دیگر

Robert James Stewart is an Associate Professor in the School of Mathematical & Computer Sciences at Heriot-Watt University, specializing in Computer Science. His research focuses on hardware processor architectures for functional programming languages and FPGA-based systems, including projects like the EPSRC HAFLANG initiative. He designs energy-efficient FPGA processors for Haskell and explores neural network compression, scalable parallelism, and high-level DSLs for FPGAs. He has authored over 40 publications, including work on Heron (graph reduction hardware), Cloaca (concurrent garbage collection), and educational tools integrating Canvas/GitLab. He actively participates in editorial activities for ACM and collaborates on security-aware coding initiatives. Stewart has supervised numerous datasets and is open to PhD students since 2013.

Christos Ttofiمشاهده پروفایل

مدرس

• Computer Vision and Image Processing
• Real-Time Software Algorithms
• FPGA Design
• +۹ مورد دیگر

Dr. Christos Ttofi is an Associate Lecturer at the Department of Electrical and Electronic Engineering, UCLan Cyprus. He holds a PhD in Computer Engineering (University of Cyprus, 2014), an MSc in Computer Engineering (2011), and a BSc in Computer Engineering (2009). His research focuses on embedded systems, FPGA design, real-time algorithms, and computer vision. He has contributed to over 10 peer-reviewed publications and has received multiple awards, including the Best Paper Award at the IEEE International Conference on Microelectronic Systems Education (2009). Education: PhD in Computer Engineering, University of Cyprus (2009–2014) MSc in Computer Engineering, University of Cyprus (2009–2011) BSc in Computer Engineering, University of Cyprus (2005–2009) Research Interests: Computer Vision and Image Processing Hardware/Software Co-design Embedded Systems & Microcontrollers FPGA Design and Real-Time Algorithms Networks-on-Chip (NoC) and MPSoC Architectures Publications: His work spans embedded vision systems, FPGA-based computing, and real-time algorithm optimization, with notable contributions to IEEE Transactions and ACM conferences. Awards: Best Paper Award (IEEE Microelectronic Systems Education, 2009) Distinction as Best Senior Student in Computer Engineering (2008–2009) Full Scholarship for Doctoral Studies (2011–2014) Teaching & Grants: He leads the 'Digital Systems' module and contributed to research grants such as the EUROSTARS-funded 'RUNNER' project (2011–2013) and the Research Promotion Foundation's MPSoC initiative (2009–2011).

• GPU Computing
• Field-Programmable Gate Arrays
• Energy Efficiency
• +۴ مورد دیگر

Craig Philip Steffen serves as a Senior Research Scientist at the National Center for Supercomputing Applications (NCSA), University of Illinois Urbana-Champaign, focusing on hardware acceleration and energy-efficient computing architectures within high-performance computing environments. His research expertise spans GPU computing, Field-Programmable Gate Array (FPGA) acceleration, and energy efficiency optimization. Steffen has pioneered methodologies for power measurement in large-scale systems and developed the EcoG architecture for power-efficient GPU clusters. His work addresses critical challenges in balancing computational performance with energy constraints, particularly for scientific workloads requiring heterogeneous computing solutions. Analysis of his 2007-2014 publications reveals an evolutionary research trajectory: beginning with reconfigurable computing foundations, progressing through FPGA acceleration techniques, and culminating in comprehensive GPU cluster optimization and power measurement frameworks. This progression mirrors the industry's growing emphasis on energy-aware design as supercomputing systems scale toward exascale capabilities. As part of NCSA's interdisciplinary research ecosystem, Steffen collaborates within teams developing advanced computational infrastructure that supports national scientific research initiatives across diverse domains.

Oscar Gustafssonمشاهده پروفایل

دانشیار

• Digital Circuit Design
• Field-Programmable Gate Array (FPGA)
• Application-Specific Integrated Circuit (ASIC)
• +۷ مورد دیگر

Oscar Gustafsson is an Associate Professor at Linköping University, affiliated with the Department of Electrical Engineering (ISY) and the Division of Electronics and Computer Engineering (ELDA). His research focuses on energy-efficient computations in digital circuits, particularly in ASIC and FPGA architectures, with applications in communication systems and hardware acceleration for AI. The 2024 publications highlight his work on FPGA implementations for dynamic programming in hybrid vehicles, hardware-aware neural network optimizations, FFT architectures for MIMO systems, Python-based finite word-length effect simulations, and CPU-FPGA co-design strategies. These works intersect with Computer Engineering Artificial Intelligence Signal Processing Computer Arithmetic Embedded Systems domains. He leads the Division of Computer Engineering at Linköping University, emphasizing collaborative research including Swedish-Chinese 5G technology partnerships.

• Unmanned Aerial Vehicles
• Field Programmable Gate Arrays
• Computational Fluid Dynamics
• +۳ مورد دیگر

Dr. Ramadan Janissary is an Assistant Professor at Istanbul Technical University's Department of Aeronautical Engineering, specializing in Unmanned Aerial Vehicles (UAVs) and Field Programmable Gate Array (FPGA) technologies. His research spans computational fluid dynamics, hardware acceleration, and multi-sensor tracking systems. Recent research trends focus on FPGA-based solutions for aerospace applications, including 6-DoF dynamical models, quaternion-Euler angle conversion, and formation flight systems for UAVs. His work addresses technical challenges in ad-hoc networks, time delay management, and nonlinear dynamics. Scientific awards include the BOEING Academic Study Incentive Award (2017) and Best Doctoral Theses Award (2015). Current projects involve UAV recording systems, FPGA-based accelerators, and aviation CNS technology development.

Kadir Durakمشاهده پروفایل

استادیار

• Quantum Cryptography
• Quantum Key Distribution
• Photonics
• +۵ مورد دیگر

Kadir Durak serves as Assistant Professor in Electrical and Electronics Engineering at Özyeğin University's Faculty of Engineering, specializing in quantum technologies. His research group operates within the Quantum Optics Laboratory focusing on cutting-edge quantum communication and sensing systems. Dr. Durak earned his B.Sc. in Physics from Middle East Technical University (2009) and completed his Ph.D. at National University of Singapore (2015). Following his doctorate, he led a research team at Singapore's Centre for Quantum Technologies developing space-ground quantum key distribution via CubeSat platforms. His research spans quantum cryptography, photonics, and quantum information with emphasis on: Optimization of entangled photon sources for secure communication Quantum key distribution networks (including satellite-based systems) Quantum radar and imaging technologies Single atom-photon interactions in cavity QED systems Quantum random number generation His recent publications demonstrate significant contributions to quantum security mechanisms, noise-tolerant quantum sensing, and practical quantum communication implementations. Notable scientific recognition includes: Bronze Medal for Quantum Cryptography Network invention at Istanbul International Inventions Fair (2019) Accepted publication in IOP Journal of Optics (2021) on vacuum fluctuation-based QRNG Dr. Durak actively recruits graduate students for quantum technology research through fully-funded positions offering tuition waivers, monthly stipends (5000-6000 TL), and research resources. His laboratory collaborates with defense institutions including TÜBİTAK and ASELSAN, with recent demonstrations of quantum radar and entangled photon imaging systems. Current projects focus on quantum communication networks, ultra-cold atom physics, and sub-diffraction limit imaging.

Zhe Wangمشاهده پروفایل

پژوهشگر

• Image Processing
• Computer Vision
• Embedded Systems
• +۳ مورد دیگر

Zhe Wang is a Researcher at the Computational Imaging Systems department within the University of Stuttgart, Germany. Their work focuses on real-time image processing, data compression, and FPGA-based hardware architectures. Research Interests Real-time video/image compression Perceptual coding with JND models Parallel decoding architectures Embedded systems for high-throughput applications FPGA design for multimedia processing Publications Highlight Recent work spans FPGA-accelerated super-resolution, marker-free variable-length coding, and perceptual compression techniques. These contributions emphasize hardware efficiency, visual quality preservation, and high-speed data processing.

• Artificial Intelligence
• Computer Science
• Genomics
• +۱۱ مورد دیگر

Nikolaos Alachiotis is an Associate Professor at the Digital Society Institute, specializing in Artificial Intelligence, Field-Programmable Gate Arrays (FPGA), and Genomics. His work bridges Hardware Acceleration with Bioinformatics , focusing on scalable solutions for genomic analysis and evolutionary biology. Research Themes: AI Hardware, FPGA Optimization, Population Genetics Key Collaborations: Delft University of Technology, Zenodo, Frontiers in High-Performance Computing Research Focus Alachiotis develops deep learning and FPGA-based tools for selective sweep detection and phylogenetic inference , enabling faster genomic analyses. His work spans convolutional neural networks , spiking networks , and adaptive computing for bioinformatics and radio astronomy. Recent articles highlight trends in AMD Versal SoC applications, deep learning for natural selection , and scalable genomic pipelines . Scientific Awards Outstanding Student Paper Award (2023) Supervised Work His supervised projects include Accelerating Selective Sweep Detection and Effective Data Preprocessing Techniques , often in collaboration with institutions like Zenodo and Frontiers journals.

David Greggمشاهده پروفایل

استاد

• Software performance optimization
• Embedded systems
• Accelerating deep neural networks on embedded systems
• +۱۲ مورد دیگر

David Gregg is a Professor in the Department of Computer Science at Trinity College Dublin's School of Computer Science and Statistics, where he serves as Global Director for Computer Science (since 2020) and previously as Head of the Discipline of Software and Systems (2018-2022). His research focuses on software performance optimization and embedded systems, with particular expertise in accelerating deep neural networks on resource-constrained platforms. He teaches Systems Programming (CS2014/5) and Concurrent Systems I (CS3014). Gregg's research spans multiple areas of computer systems including: Compiler optimization and program analysis Processor microarchitecture and parallelism (multi-core, vector, instruction-level) Computer arithmetic and domain-specific languages Low-energy embedded systems and FPGA implementations Deep neural network acceleration He has served on numerous program committees including PACT, PLDI, CC, and other major computer systems conferences, and has been on the Board of Distinguished Reviewers for ACM TACO multiple times. Professor Gregg has advised numerous PhD and MSc students, many of whom have gone on to prominent positions at companies like Intel-Movidius, Google, Amazon, and Synopsys. He leads the triNNity project which includes optimized libraries and compilers for implementing convolutional neural networks on CPUs.

• FPGA Design
• Hardware Optimization
• Computer Architecture
• +۳ مورد دیگر

Kelvin Chung is a Researcher in the Department of Computer Science with a focus on FPGA design and hardware optimization. His work explores topics such as configuration logic, tile interfaces, and power consumption reduction in reconfigurable computing systems. He holds a Doctor of Philosophy and has contributed to advancements in FPGA architecture through peer-reviewed research. Research Interests: - FPGA Optimization - Configuration Bit Management - Storage and Memory Configuration - Physical Design of Electronics - Embedded Systems Design - Energy-Efficient Computing Collaborations include international contributions to field-programmable gate array (FPGA) research, as evidenced by his 2022 publication at the ACM/SIGDA International Symposium on FPGAs. This work addresses novel methods to reduce FPGA size while maintaining performance, with implications for embedded systems and reconfigurable computing.

• Circuit Reliability
• System-on-Chip (SoC) Testing
• Embedded Systems
• +۳ مورد دیگر

Paolo Bernardi is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) , Politecnico di Torino. His academic career spans over 20 years with continuous contributions to circuit reliability and SoC testing. Research focuses on System-on-Chip Testing , Embedded Systems , and Automotive Electronics Key projects: ARBoard (Augmented Reality for PCB testing), TC4xx NVM Test Strategies, and multiple commercial collaborations with automotive industry leaders His work bridges Computer Architecture and Industrial Engineering , with significant contributions to Test Automation and Reliability Engineering . Recent publications analyze scan test cost models and logic diagnosis techniques for automotive SoCs. Scientific recognitions include: EDAA Outstanding Dissertation Award (2006) Best Paper Awards at DATE (2006) and DDECS (2016) IEEE Fellowship (2020-) As a PhD supervisor, he guides students in Automotive Reliability and Embedded Testing projects. His research involves collaborative efforts with institutions like CNRS and Université de Montpellier through the LIA LAFISI laboratory.

• Biomedical Signal Processing
• Machine Learning
• Neonatal EEG Analysis
• +۵ مورد دیگر

William Marnane is a Professor of Electrical and Electronic Engineering at University College Cork (UCC). He holds a B.E. from UCC (1984) and a D.Phil. from the University of Oxford (1989). His career includes roles as Lecturer (1989), Senior Lecturer (1999), Dean of Graduate Studies (2013–2016), and Head of the School of Engineering (2016–2019). He has led major research initiatives, including the SFI-funded INFANT Centre and the Claude Shannon Institute. His research focuses on biomedical signal processing, machine learning, and neonatal EEG analysis, with notable contributions to neonatal seizure detection algorithms and fetal health monitoring. He has been awarded the Giner de Los Ríos Visiting Research Fellowship (2007, 2020) and leads projects funded by Wellcome Trust, Science Foundation Ireland, and EU grants. His educational background includes significant contributions to curriculum development and graduate training in engineering and biomedical sciences. He has supervised numerous research projects, including advancements in cryptographic hardware, embedded systems, and low-power signal processing architectures. Research highlights include the ANSeR neonatal seizure detection algorithm (Wellcome Trust-funded), clinical trials on EEG-based seizure recognition, and development of secure TLS coprocessors. He has published over 200 peer-reviewed articles, with key contributions in neonatal EEG analysis, machine learning applications in healthcare, and cryptographic processor design. Current research explores AI for fetal heart rate monitoring and wearable health technologies. Grants and partnerships include leadership roles in EU-funded projects, SFI Strategic Research Clusters, and industry collaborations. He co-directs the INFANT Centre, bridging engineering, medicine, and data science for translational research in neonatal care.

Dylan Rankinمشاهده پروفایل

استادیار

• Particle Physics
• Machine Learning Applications in Physics
• High-Energy Physics
• +۳ مورد دیگر

Dylan Rankin is an Assistant Professor in the Department of Physics and Astronomy at the University of Pennsylvania’s School of Arts & Sciences. His research focuses on particle physics experiments at the Large Hadron Collider (LHC), leveraging machine learning (ML) for data analysis and optimizing high-speed trigger systems. He is a key contributor to the FastML collaboration, advancing FPGA-based ML inference for low-latency applications in physics and astronomy. Education: Ph.D. in Physics from Boston University (2018), Sc.B. in Physics from MIT (2012). Research Interests: Probing the Standard Model through LHC proton-proton collision data Machine learning applications in jet classification, mass regression, and event reconstruction Optimizing trigger systems for real-time data selection at the LHC Hardware acceleration (FPGA/GPU) for scientific computing challenges Recent work emphasizes ML deployment in latency-constrained environments, including gravitational wave astronomy and FPGA-as-a-service frameworks. His collaborative projects include hls4ml for low-latency inference and AIgean for heterogeneous cluster ML workflows. Advising/Grants: Active in training next-generation researchers in ML-driven particle physics methodologies. Involved in multi-institutional initiatives for computational infrastructure development. Labs/Teams: Core member of the FastML collaboration, leading FPGA-based ML solutions for physics experiments. Associated with the Penn High Energy Physics group.

• Field programmable gate arrays (FPGA)
• Wireless sensor networks
• Acoustic waves and sensors
• +۷ مورد دیگر

Abdellah Touhafi is a Professor at the Faculty of Engineering Technology, Department of Electronics and Informatics at Vrije Universiteit Brussel (VUB) in Brussels, Belgium. With an extensive research portfolio spanning nearly three decades, his work focuses on embedded systems, sensor networks, and FPGA technologies with applications in environmental monitoring and smart cities. His current research activities include leading multiple projects related to low-carbon technologies, health technologies, and sustainable sensing systems. Dr. Touhafi's research interests center around Field Programmable Gate Arrays (FPGA), wireless sensor networks, acoustic sensing, and machine learning applications for environmental monitoring. His work bridges hardware engineering with practical applications in smart city infrastructure, water quality monitoring, and sustainable sensing technologies. He has developed innovative approaches for hardware-assisted security mechanisms in environmental monitoring systems and has explored the integration of triboelectric sensors for self-powered sensing applications. His publication record shows consistent output with 154 research outputs, including recent articles in Sensors journal and conference papers at IEEE events. His h-index of 19 (with 1,433 citations) reflects significant impact in his fields of expertise. Current projects include DESTINY (Low-carbon solutions), GEAR (future health technologies), and ILSF 2024 (acoustic mapping). NSIS3: DESTINY: Low-carbon solutions and technology for a new future (2024-2029) OZR4208: Bilateral cooperation for joint PhD VUB-USMBA (2023-2027) IOF3016: GEAR: Future health technologies (2021-2025) BRGEOZ445: ILSF 2024 - This is the Sound of "ME" (2024) IOFACC12: Tech4Health (2024-2025) Dr. Touhafi actively supervises students and has served on PhD committees, including for projects related to sustainable public lighting and environmental monitoring systems. His research group has produced datasets like the AMIVU Acoustic Map Imaging VUB-ULB Dataset, demonstrating practical applications of his theoretical work. He regularly participates in conferences including IEEE events and has organized workshops on industrial electronics.

Arindam Mukherjeeمشاهده پروفایل

دانشیار

• Internet of Things (IoT)
• Fog Computing
• Real-Time Systems
• +۴ مورد دیگر

Dr. Arindam Mukherjee serves as an Associate Professor in the Department of Electrical and Computer Engineering at the University of North Carolina at Charlotte, College of Engineering. His office is located in EPIC 2336, and he can be reached at amukherj@charlotte.edu or by phone at 704-687-8417. His educational qualifications are as follows: Ph.D. from the University of California at Santa Barbara (2002) M.S. from the University of California at Santa Barbara (2000) B.Tech. from Jadavpur University, India (1996) Dr. Mukherjee's research spans Smart System Architectures, Internet of Things (IoT), and Fog Computing. He investigates cooperative and autonomous mobile systems, real-time system software, and database management for Big Data. His work includes scheduling algorithms for IoT systems and the integration of edge, fog, and cloud computing paradigms for real-time applications in the Big-Squared Data space. His publication record from 2003 to 2018 shows an evolution from VLSI design and bioinformatics to contemporary IoT and fog computing. Early work focused on logic synthesis, biochip testing, and FPGA-based bioinformatics implementations, while recent contributions address power management in heterogeneous processors, energy-efficient communications for smart buildings, and the synergistic integration of edge, fog, and cloud computing for real-time IoT data processing. No scientific awards are mentioned in the provided text. There is no information available regarding students advised or research grants. Similarly, no specific research labs or collaborative teams are described in the source material.