جست‌وجوی استاد و پژوهشگر

Vijayanand Nagarajanمشاهده پروفایل

استاد

• Computer Architecture
• Programming Languages
• Computer Systems
• +۳ مورد دیگر

Roles & Affiliations: Vijayanand Nagarajan is a Professor at the Kahlert School of Computing, University of Utah. He leads the CAPS Research Group, focusing on computer architecture, programming languages, and systems. His work emphasizes constructive, specification-driven design for efficiency and correctness. Research Interests: His research spans consistency models for distributed systems, persistent memory, coherence protocols, and hardware/software co-design. Key areas include key-value-store consistency, persistent memory optimization, and automated protocol synthesis. His group explores topics like cache coherence, fault tolerance, and scalable distributed storage. Articles Trends: Recent work emphasizes automated protocol generation (e.g., HeteroGen, PipeGen), consistency in distributed systems (LAW theorem), and fault-tolerant designs (Apta, Dve). Publications span top conferences like ISCA, ASPLOS, and VLDB, addressing both theoretical foundations and practical implementations. Advising & Grants: Advises current PhD students (An Qi Zhang, Soham Bagchi, et al.) and has mentored over a dozen alumni now in academia and industry (e.g., NVIDIA, Google, Huawei). Active in organizing and reviewing for conferences like ISCA, MICRO, and HPCA. Labs & Teams: Heads the CAPS Research Group, collaborating on projects like C3D, ATCache, and Odyssey. Engages in interdisciplinary work with robotics and compiler communities.

Liucheng Guoمشاهده پروفایل

پژوهشگر ارشد

• AI software-hardware co-design
• embedded AI
• high-performance computing (HPC)
• +۷ مورد دیگر

Dr. Liucheng Guo is a Visiting Senior Research Fellow and RAEng Industrial Fellow at King’s College London, affiliated with the Department of Mathematics within the Faculty of Natural, Mathematical & Engineering Sciences. His expertise spans AI software-hardware co-design, embedded AI, and high-performance computing (HPC), with a focus on sustainable human-machine interfaces (HMI) for sectors like IoT, Automotive, Healthcare, and XR. He holds a Ph.D. from Imperial College London and an MA from Peking University. Dr. Guo has co-founded the award-winning AI startup TG0, securing seven patent groups and over £20 million in grants from InnovateUK, RAEng, and the European Innovation Council. His research emphasizes smart home, IoT, and healthcare applications, including VR mirror therapy for post-stroke rehabilitation and gesture-based control systems. He is a Senior Member of IEEE and Fellow of IET/BCS/RSA, reflecting his leadership in bridging research and industry. His grants include Smart Grants, KTP awards, and SME Leader initiatives. He actively promotes STEM through roles as RAEng Visiting Professor and public engagement ambassador.

Boris Koldehofeمشاهده پروفایل

استاد

• Distributed Systems
• Computer Networks
• Software-defined Networking
• +۴ مورد دیگر

Prof. Dr. Boris Koldehofe is a Full Professor of Distributed and Operating Systems at the Technical University of Ilmenau, Germany since 2023. Previously, he held Full Professor of Computer Networks at the University of Groningen (2020–2023) and served as a Senior Researcher/Lecturer at Technical University of Darmstadt (2014–2020). He is also an Adjunct Professor at TU Darmstadt since 2020. Research Focus: Distributed Systems, Software-defined Networking, Complex Event Processing (CEP), Fog Computing, Privacy Engineering Education: Habilitation in Communications and Distributed Systems (2019), PhD in Computer Science (2005) Research Trends : His 2024–2023 publications emphasize analog/memristor-based network computing , privacy-preserving CEP , and time-sensitive networking . Key themes include programmable data planes , AI-driven infrastructure , and energy-efficient systems . Recent work explores federated learning in vehicular networks and privacy engineering for IoT analytics . Projects & Leadership : He leads the DFG CRC 1053 MAKI (2014–2024) and the Parrot: Privacy Engineering for IoT project (2021–). He serves as principal investigator for dynamic Industry 4.0 communication networks and has managed collaborations with institutions like EPFL, University of Stuttgart, and University of Heidelberg.

Radhika Groverمشاهده پروفایل

مدرس

• Human-Computer Interaction
• Accessible Design
• Machine Learning
• +۱۲ مورد دیگر

Radhika Grover is a Lecturer in the Electrical and Computer Engineering Department at Santa Clara University's School of Engineering. With over 15 years of teaching experience since 2004, she specializes in hardware and software domains. Education B.S. in Electrical Engineering, Indian Institute of Technology-Roorkee (1991) M.S. in Electrical Engineering, Birla Institute of Technology (1992) Ph.D. in Computer Engineering, Santa Clara University (2003) Her research focuses on Human-Computer Interaction and Accessible Design , exemplified by her work on a digital book for learning Python programming for students with blindness. She has also contributed to Embedded Systems , Machine Learning , and Computer Architecture . Her publications highlight expertise in FPGA Design , Quality of Service in Multimedia Systems , and Educational Technology . She has taught courses on Verilog HDL , Secure Coding , and Java Programming at institutions like Santa Clara University and UC Santa Cruz SV Extension. She authored a Java programming textbook and holds a U.S. Patent 10222870 for a wearable reminder device.

Tokunbo Ogunfunmiمشاهده پروفایل

استاد

• Deep Learning
• Artificial Neural Networks
• Adaptive Signal Processing
• +۷ مورد دیگر

Tokunbo Ogunfunmi is a Professor of Electrical and Computer Engineering and Director of the Information Processing and Machine Learning (IPML) Research Lab at Santa Clara University's School of Engineering. He served as Associate Dean for Research and Faculty Development from 2010-2014 and as Associate Dean for Mission, Culture and Inclusion from 2021-2024. Dr. Ogunfunmi holds a Ph.D. and M.S. from Stanford University (1990, 1984) and a B.S. from the University of Ife, Nigeria (1980). His research interests span deep learning, artificial neural networks, adaptive/nonlinear signal processing, digital signal processing, and multimedia signal processing with VLSI/DSP/FPGA implementations. Dr. Ogunfunmi has published 4 books and over 200 refereed journal and conference papers in these areas. His recent publications demonstrate strong focus on deep learning applications, hardware acceleration for neural networks, quaternion-based adaptive algorithms, and efficient video/speech processing techniques. Dr. Ogunfunmi has received numerous awards including the SCU Presidential Special Recognition Award (2020), IEEE Meritorious Service Awards (2021, 2013), Carnegie Foundation Visiting Professorships (2019, 2015), and multiple Best Paper Awards. He has served in significant editorial roles including Senior Associate Editor for IEEE Signal Processing Letters and Associate Editor for several IEEE Transactions. As an active member of the professional community, he is a Senior Member of IEEE, Member of Sigma Xi, and Member of AAAS. His industrial experience includes consulting for major companies such as Broadcom, AMD, NEC, AT&T Bell Labs, and NIKON Precision Research & Development. Dr. Ogunfunmi is also a registered professional engineer in California.

Lei Jiaoمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Tsetlin Machines
• +۷ مورد دیگر

Lei Jiao is a Professor in the Department of Information and Communication Technology at the University of Agder's Faculty of Engineering and Science. Previously serving as an Associate Professor from May 2014 to October 2022, Dr. Jiao has established himself as a leading researcher in artificial intelligence, with particular expertise in Tsetlin Machines and their applications across diverse domains. PhD in Information and Communication Technology, University of Agder (2008-2012) Master of Engineering in Communication and Information System, Shandong University (2005-2008) Bachelor of Engineering in Telecommunication Engineering, Hunan University (2001-2005) Dr. Jiao's research spans multiple cutting-edge areas including interpretable artificial intelligence, wireless communication protocols, network resource allocation, and signal processing. His work on Tsetlin Machines has pioneered new approaches to machine learning that emphasize interpretability while maintaining high performance. The research group he contributes to at the University of Agder focuses on Autonomous and Cyber-Physical Systems (ACPS), Battery recycling, and the Centre for Artificial Intelligence Research (CAIR). Analysis of Dr. Jiao's recent publications reveals a strong emphasis on interpretable AI systems, particularly through Tsetlin Machines. His work spans applications in GNSS jammer detection, crowd anomaly detection, DNA sequence classification, and hardware acceleration of machine learning models. The research consistently demonstrates how logical, rule-based approaches can provide transparent alternatives to traditional neural networks while maintaining competitive performance. Supervised numerous PhD students including Vojtech Halenka, Ahmed K. Kadhim, and Sindhusha Jeeru Mentored over 30 Master's thesis projects covering topics from Tsetlin Machines to signal processing and computer vision Collaborates extensively with Ole-Christoffer Granmo and other leading researchers in the AI field Dr. Jiao actively contributes to advancing the field through supervision of doctoral candidates, collaboration on major research projects, and development of novel machine learning approaches that balance performance with interpretability. His work bridges theoretical foundations with practical applications across telecommunications, computer vision, and natural language processing domains.

Weigang Wangمشاهده پروفایل

استاد

• Condensed Matter Physics
• Spintronics
• Nanomagnetism
• +۳ مورد دیگر

Weigang Wang is a Professor of Physics and Electrical and Computer Engineering at the University of Arizona, serving as Director of Graduate Studies. His research focuses on nanoscale spin and charge transport phenomena with direct applications in next-generation magnetic memory and logic technologies. His educational background includes a Ph.D. in Physics from the University of Delaware (2008). His research group specializes in voltage-controlled magnetism, spin-orbit torque effects, and magnetic tunnel junction engineering for ultra-low-power spintronic devices. Wang's experimental work bridges fundamental condensed matter physics and practical device engineering, emphasizing energy-efficient switching mechanisms through novel materials and nanostructure fabrication techniques including electron-beam lithography and self-assembly processes. His publications demonstrate consistent innovation in reducing switching energy barriers for nanomagnetic systems. His research trajectory shows increasing focus on antiferromagnetic spintronics, topological materials interfaces, and cryogenic-compatible magnetic memory since 2020, with significant contributions to voltage-controlled magnetic anisotropy and spin-transfer torque efficiency. Notable honors include: CSM Fellow (2024) University of Arizona Distinguished Scholar Award (2022) Department of Physics Outstanding Undergraduate Teaching Award (2018) NSF CAREER Award (2016) As Director of Graduate Studies, Wang oversees advanced degree programs while leading an experimental lab focused on magnetic device characterization and nanofabrication. His NSF-funded research explores voltage-mediated control of magnetic states, with implications for non-volatile memory and neuromorphic computing architectures. The lab maintains collaborations with national facilities for advanced characterization of spintronic materials.

• Quality of Experience (QoE) of Internet applications
• Artificial intelligence and machine learning for communication networks
• Measurement and analytics of encrypted network traffic
• +۵ مورد دیگر

Prof. Dr. Michael Seufert is a Full Professor and Chairholder at the University of Augsburg since October 2023, leading the Chair of Networked Systems and Communication Networks within the Faculty of Applied Computer Science. Previously, he served as a Private Lecturer at the University of Würzburg and completed his Habilitation in Computer Science there in 2023. His academic journey includes a PhD in Computer Science from the University of Würzburg (2017) with a thesis on Quality of Experience and Access Network Traffic Management of HTTP Adaptive Video Streaming, which earned him two prestigious Best Dissertation Awards. His research focuses on Quality of Experience (QoE) of Internet applications, artificial intelligence and machine learning for communication networks, measurement and analytics of encrypted network traffic, and data-driven proactive user-centric network management solutions. His work bridges theoretical foundations with practical applications in network monitoring, security, and performance optimization. Recent research has increasingly incorporated machine learning techniques to address challenges in network management, traffic analysis, and user experience assessment. Prof. Seufert's publication record shows a clear evolution from foundational work on QoE modeling and HTTP adaptive streaming toward more sophisticated applications of machine learning in network operations. His recent publications (2023-2025) demonstrate a strong emphasis on practical ML applications for network monitoring, security, and optimization, with particular attention to real-world constraints and performance requirements. The research spans multiple subfields including encrypted traffic analysis, explainable AI for network management, edge computing, and quality assessment for emerging applications. CNOM Young Professional Award of the IEEE Communications Society (ComSoc) Technical Committee on Network Operations & Management (CNOM) (2024) Best Dissertation Award 2018 of KuVS (Communication and Distributed Systems) special interest group Best Dissertation Award of the IEEE Communications Society (ComSoc) Technical Committee on Network Operations & Management (CNOM) Prof. Seufert actively supervises research projects and advises students through thesis projects and practical modules. His teaching portfolio includes Communication Systems, Management of Communication Networks, Practical Introduction to Internet-Technologies, and seminars on Networked Systems. He leads a research group focused on networked systems and communication networks, with current projects exploring machine learning applications in networking, as evidenced by the organization of the 3rd International Workshop on Machine Learning in Networking (MaLeNe 2025).

• Parallel Computer architectures
• High Performance Computing
• Multi-/Manycore architectures
• +۵ مورد دیگر

PD Dr. Josef Weidendorfer is a qualified private lecturer at Technische Universität München (TUM) and leads the Future Computing Group at the Leibniz Computing Centre (LRZ). He holds a dual affiliation with TUM's Department of Informatics, Chair of Computer Architecture and Parallel Systems (Prof. Schulz), and the Leibniz Rechenzentrum der Bayerischen Akademie der Wissenschaften. His work focuses on developing smooth migration strategies for future HPC systems and evaluating novel technologies to improve system-level and workload analysis tools. Weidendorfer's research interests encompass Parallel Computer Architectures, High Performance Computing, Multi-/Manycore architectures, GPGPU, Performance analysis and optimization, Cache Simulation, Virtual Machines, and dynamic code generation. He is particularly interested in strategies for improving computational efficiency across various hardware structures, including specialized accelerator hardware for HPC codes. He regularly organizes the UCHPC workshop (since 2010 with Euro-Par) about unconventional hardware for HPC computing and is co-organizer of the PSTI workshop series. His recent publications reveal a strong focus on HPC system optimization, with particular emphasis on load balancing techniques, cache partitioning, application malleability, and performance monitoring. The research trajectory shows increasing attention to practical implementation challenges in modern heterogeneous computing environments, especially regarding GPU utilization, resource partitioning under power constraints, and phase-aware system monitoring. Weidendorfer maintains the open-source tools Callgrind/KCachegrind for cache simulation and has supervised numerous student projects across bachelor's, master's, and guided research programs. He teaches courses including Virtualization Techniques, Parallel Programming Systems, and Advanced Computer Architecture, demonstrating strong commitment to both research and education in computer architecture and parallel systems. As principal investigator for multiple large-scale projects including EU Project SEANERGYS (2025-2028) and BMBF Project ScalNext (2022-2025), he leads significant research initiatives focused on future computing technologies and HPC system development.

SEMA KAYHANمشاهده پروفایل

استاد

• Image Processing
• Compressive Sensing
• Signal Processing
• +۱۱ مورد دیگر

Professor Sema Kayhan is a faculty member at Gaziantep University , Faculty of Engineering , Department of Electrical and Electronic Engineering . She has held the academic rank of Professor since 2021, after progressing from Associate Professor (2016-2021) and Assistant Professor (2007-2015). Her career began at Gaziantep University as a Research Assistant (1996-2005) and includes administrative roles as Vice Head of Department (2013-2015). Doctorate: 1999-2005, Gaziantep University Institute of Science, Electrical and Electronic Engineering Degree: 1996-1999, Gaziantep University Faculty of Engineering, Electrical and Electronic Engineering & Computer Science Licence: 1991-1996, Gaziantep University Faculty of Engineering, Electrical and Electronics Engineering (English) Her research spans Image Processing , Compressive Sensing , Machine Learning , and Biomedical Signal Processing , with notable contributions to ECG signal reconstruction , chaotic encryption , and FPGA-based hardware acceleration . She has supervised 23 theses, including PhD works on hybrid deep learning for schizophrenia detection , sparse extreme learning machines , and smart home solar energy systems . Her 15 most recent publications (2015-2022) demonstrate expertise in image watermarking , encryption , antenna design , and ECG signal processing , often leveraging chaos theory , extreme learning machines , and FPGA implementations . She has also contributed to NLP for Arabic-alphabet languages and retinal blood vessel segmentation via transfer learning. Professor Kayhan's projects include renewing jacquard control systems (2007-2008) and ECG signal reconstruction via compressive sensing on FPGA (2016-2017). She co-designed a smart home model using solar energy (2017) and has been a Member of the Chamber of Electrical Engineers since 1997.

Jim Harkinمشاهده پروفایل

استاد

• Embedded Systems
• Neural Networks
• Hardware Systems
• +۵ مورد دیگر

Professor Jim Harkin serves as Head of the School of Computing, Engineering and Intelligent Systems at Ulster University's Magee Campus in Derry~Londonderry. He is a prominent member of the Computational Neuroscience and Neuromorphic Engineering team within the Intelligent Systems Research Centre (ISRC), where he leads cutting-edge research bridging biological neural processes with hardware implementations. Harkin's research focuses on developing intelligent embedded systems capable of self-repair under error conditions, drawing inspiration from neural network models. His work explores how computer models of neural networks can be mapped to hardware to build highly efficient and reliable embedded computers. Key innovations include Networks-on-Chip strategies and hardware implementation of self-repairing Spiking Neural Networks. His research spans multiple domains including fault tolerance, neuromorphic computing, and AI hardware acceleration, with applications in healthcare, structural monitoring, and energy systems. Analysis of his recent publications reveals a strong trend toward practical applications of neuromorphic computing, particularly in healthcare monitoring systems, structural health assessment, and energy-efficient computing. His work shows increasing integration of spiking neural networks with real-world hardware implementations, demonstrating a clear trajectory from theoretical models to deployable systems with commercial applications. Harkin has received numerous scientific accolades including the Life and Health Startup Company of the Year 2019 from InventNI Ulster Distinguished Learning Support Fellowship Multiple awards for innovative routing strategies in neural network hardware implementations Professor Harkin has secured significant research funding exceeding £3.5 million from diverse sources including EPSRC, MRC, Innovate UK, HSC R&D, InvestNI, and DEL. His grant portfolio demonstrates strong industry and healthcare sector engagement, particularly through his co-founded startup Respiratory Analytics which focuses on medical analytics. He has supervised numerous research students and has been instrumental in Ulster University's Computer Science submissions to major research assessment exercises including RAE 2008, REF2014, and REF2021. As Head of School and leader within the Intelligent Systems Research Centre, Harkin oversees multiple research teams focusing on computational neuroscience, neuromorphic engineering, and intelligent embedded systems. His lab has developed specialized FPGA-based platforms for simulating and implementing self-repairing neural networks, including the AstroByte multi-FPGA architecture for accelerated simulations of fault-tolerant spiking astrocyte-neuron networks.

• Computer Architecture
• Computer Arithmetic
• RISC-V Systems
• +۳ مورد دیگر

David Mallasen Quintana is a Scientist and Lecturer at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with both the School of Engineering (STI) and the Integrated Systems Laboratory (ESL) within the Institute of Electrical and Microengineering. He also holds teaching responsibilities in the School of Computer and Communication Sciences (IC) at EPFL. Dr. Quintana completed his Ph.D. in Computer Engineering at Universidad Complutense de Madrid in 2024. His research focuses on: Computer architecture and arithmetic systems RISC-V ecosystem development and customization Energy-efficient hardware design and domain-specific accelerators Posit arithmetic implementations and optimizations Embedded systems and low-power computing solutions His recent publications demonstrate consistent focus on RISC-V extensions, posit arithmetic implementations, and hardware/software co-design approaches for efficient computing. The work spans from fundamental arithmetic units to application-specific accelerators, with evolving emphasis on scientific computing applications and energy-constrained environments. Dr. Quintana actively contributes to open-source hardware projects including PERCIVAL, x-HEEP, and various arithmetic unit implementations. His research group develops energy-efficient platforms and tools for hardware deployment within the RISC-V ecosystem.

Luca Caucciمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Optical Sciences
• Biomedical Imaging
• +۶ مورد دیگر

Luca Caucci is an Assistant Research Professor in Medical Imaging at the University of Arizona, affiliated with the Arizona Health Sciences Center (AHSC). His research integrates optical sciences and computational imaging to advance medical imaging technologies, particularly in nuclear medicine and 3D imaging . He has pioneered methods for photon-processing detectors and stochastic modeling in oncology and virology. Research Trends: His publications focus on positron emission tomography (PET) , machine learning for image reconstruction , and radiance sensor development . Key themes include GPU acceleration , list-mode data processing , and statistical decision theory in biomedical imaging. His work addresses challenges in 3D imaging , noise reduction , and task performance optimization . Collaborative Efforts: While the text does not explicitly mention lab teams or grants, his contributions to FastSPECT III calibration and alpha/beta emission tomography suggest active involvement in interdisciplinary projects bridging optical engineering and medical diagnostics .

Vijaykrishnan Narayananمشاهده پروفایل

استاد

• Computer Architecture
• VLSI Design
• Energy-Efficient Computing
• +۳ مورد دیگر

Vijaykrishnan Narayanan is a Distinguished Professor in the Department of Computer Science and Engineering at Pennsylvania State University . His research focuses on ferroelectric memory systems , energy-efficient computing , and neuromorphic engineering , with applications in deep learning , edge computing , and secure hardware design . He leads projects such as EFRI BRAID (Neuroscience-Inspired Visual Analytics) and FuSe-TG (Heterogeneous Ferroelectronics for Big Data Analytics). Research Interests Compute-in-Memory (CIM) architectures Ferroelectric Field-effect Transistors (FeFETs) Low-power VLSI design Hardware security mechanisms Scientific Contributions Over 707 research outputs and 26 grants Key contributor to UN Sustainable Development Goals via energy-efficient computing NSF grants for projects like Ferro-CoDE (combinatorial optimization) and INSECT NET (entomology-computer science collaborations)

Sotirios Xydisمشاهده پروفایل

استادیار

• Hardware/Software Co-design
• High Level Synthesis
• Hardware Accelerators
• +۷ مورد دیگر

Sotirios Xydis is an Assistant Professor in the Division of Computer Science at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), with prior faculty appointment at Harokopion University of Athens (2020-2023). He maintains ongoing collaboration with the Institute of Communication and Computer Systems (ICCS) since 2014 and previously served as an engineer at HEDNO (2015-2018) and postdoctoral researcher at Politecnico di Milano (2011-2013). His academic credentials include: BSc in Electrical and Computer Engineering, NTUA (2005) MSc in Techno-Economic Systems, NTUA (2011) PhD in Electrical and Computer Engineering, NTUA (2011) Dr. Xydis specializes in hardware/software co-design , energy-efficient hardware acceleration , and memory management for embedded and cloud-edge systems. His research bridges low-power circuit design , heterogeneous architecture optimization , and resource management frameworks , with particular emphasis on AI workloads and serverless infrastructures. Current projects target Edge AI accelerators (CONVOLVE), disaggregated memory systems, and LLM inference optimization through hardware-aware algorithms. Analysis of his 2023-2025 publications reveals three dominant trends: (1) Energy-efficient hardware accelerators for Edge AI using approximate computing techniques, (2) Memory/resource management innovations for disaggregated serverless environments, and (3) GPU/FPGA optimization for LLM inference through dynamic frequency scaling and predictive throttling. These works consistently address the power-performance tradeoffs in heterogeneous computing systems. His scientific recognition includes: Best Paper Award, IEEE/NASA/ESA AHS (2007) Best Paper Award, ACM PARMA (2013) Best Paper Award, ACM Computing Frontiers (2020) Hipeac Award at DAC (2019, 2020) Dr. Xydis has secured over 15 European/national research grants as Principal Investigator and Technical Coordinator, focusing on hardware acceleration frameworks and energy-efficient computing. His advising encompasses graduate research in hardware design and optimization, though specific student names aren't publicly listed. Current projects include CONVOLVE for Edge AI and CollectiveHLS for collaborative hardware synthesis. He is a core member of NTUA's Microelectronics Laboratory (Microlab) and collaborates with ICCS on hardware acceleration projects. His team develops frameworks like CollectiveHLS and throttLL'eM, with active participation in DATE, DAC, and ISCA conference communities.