جست‌وجوی استاد و پژوهشگر

Christos Strydisمشاهده پروفایل

دانشیار

• High-Performance Computing (HPC)
• Hardware Acceleration
• Reconfigurable Computing
• +۶ مورد دیگر

Dr. Ir. Christos Strydis is an Associate Professor jointly affiliated with the Erasmus Medical Center (Neuroscience department) and Delft University of Technology (Quantum & Computer Engineering department). He is also the founder and head of the Neurocomputing Laboratory and a senior member of the IEEE . His roles span academic research, engineering leadership at Neurasmus BV , and teaching at both institutions. BSc, MSc, and PhD in Computer Engineering from the Technical University of Crete and Delft University of Technology 2011–present: Post-Doctoral Researcher, Chief Engineer, and faculty member at Erasmus Medical Center 2016–present: Assistant and Associate Professor at Erasmus Medical Center Dr. Strydis’s research lies at the intersection of neuroscience and computer engineering , focusing on high-performance computing (HPC) , low-power embedded systems , and functional ultrasound (fUS) imaging . His work bridges hardware acceleration, reconfigurable computing, and brain simulations to advance neural implants and neuroimaging technologies. His recent publications highlight innovations in exascale FPGA architectures for brain simulations, AI chip performance analysis , real-time spike detection algorithms , and secure health data integration via cloud HPC. Themes span from hardware security to cerebellar signal processing, emphasizing scalability and energy efficiency. At Erasmus and Delft, he supervises PhD, MSc, and BSc students , teaches bachelor- and master-level courses , and leads grants funded by the ICT Delft Research Centre , Google Inc. , and national/EU projects. His lab, the Neurocomputing Laboratory , focuses on translating computational neuroscience into clinical applications.

K. G. Karthikeyanمشاهده پروفایل

دانشیار

• Computer Architecture
• Machine Learning
• Parallel Computing
• +۳ مورد دیگر

K. G. Karthikeyan is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison. His research focuses on cutting-edge advancements in computer architecture, machine learning, and hardware acceleration. Primary Affiliation: Civil and Environmental Engineering Additional Affiliation: Environmental Chemistry and Technology His work spans multiple domains including: Computer architecture optimization GPU acceleration techniques Dataflow processing models Transformer efficiency improvements Moore's Law alternatives Hardware-software co-design Research trends from his publications show a strong emphasis on: Breaking traditional computing limitations Developing energy-efficient architectures Creating flexible acceleration frameworks Advancing deep learning hardware Exploring von Neumann alternatives Pioneering open-source computing platforms

Maurice ter Beekمشاهده پروفایل

پژوهشگر

• Formal Methods
• Software Engineering
• Railway Systems
• +۵ مورد دیگر

Maurice ter Beek is a Senior Researcher at the Formal Methods and Tools (FMT) Group of the Institute of Information Science and Technologies (ISTI) , part of the Italian National Research Council (CNR) in Pisa, Italy. He leads the FMT Lab since 2019 and coordinates critical projects like ADVENTURE (railway systems evaluation) and T-LADIES (typeful language adaptation). He serves as Editor-in-Chief of Formal Aspects of Computing and holds editorial roles in journals like Science of Computer Programming . His career spans academic leadership in international conferences (chair of ETAPS 2026 , RSSRail 2025 , etc.) and extensive collaborations in formal methods, railway systems, and software product lines. Research Interests: His work focuses on formal methods applied to industrial critical systems, particularly railways, with expertise in variability modeling, attack-defense trees, contract automata, and team automata. Publications address quantitative analysis of reconfigurable systems model checking for railway safety empirical studies on formal methods security risk modeling collaborative software engineering Scientific Awards: ERCIM Fellowship (2003, 2002) SegraVis Fellowship (2005) Best Paper at SPLC 2019 Best Paper at ICIW 2007 Socrates Grant (EU, 2000-2001) Additional Roles: He is a faculty member of the joint PhD program in Smart Computing (Florence, Pisa, Siena), member of ISTI’s Institute Council, and coordinates CNR’s involvement in the PNRR National Sustainable Mobility Centre . His global engagement includes over 50 conference organizing roles and editorial board memberships.

Jonathon Hareمشاهده پروفایل

استاد

• Representation Learning
• Deep Learning
• Computer Vision
• +۶ مورد دیگر

Jonathon Hare is a Professor in the School of Electronics & Computer Science at the University of Southampton. He holds a BEng degree in Aerospace Engineering and PhD in Computer Science, both from the University of Southampton. His research is centered around representation learning with the goal of developing techniques that allow machines to understand and utilize data to fulfill human information needs. His research spans three main areas: Novel representation techniques including differentiable neural architectures for counting and working with unordered sets; Understanding representations through biological inspiration, exploring how neural architectures relate to biological systems; and Applications of representation in geospatial intelligence and document analysis. He has made a strong commitment to open science, with many projects having open-source implementations, including the OpenIMAJ software that won the prestigious 2011 ACM Multimedia Open Source Software Competition. His recent publications (2024-2025) show a strong focus on hardware-efficient neural networks, representation learning, and explainable AI. His work addresses critical challenges in neural network memory configuration, FPGA acceleration, and stable algorithm learning using mathematical constraints. His research shows growing emphasis on practical implementation challenges and the intersection of theoretical neural network concepts with real-world hardware constraints. Vice Chancellors Teaching Award (2015) Faculty award for innovative teaching (2013-14) 2011 ACM Multimedia Open Source Software Competition winner Shortlisted for Blackboard and VLE Awards (2016, 2017, 2020) Fellow of the Higher Education Academy (FHEA) Senior Member of IEEE (SMIEEE) As Doctoral Programme Director, Professor Hare supervises over 150 PhD students across multiple disciplines including Computer Science, Geography, Engineering, and the MINDS interdisciplinary program. He leads undergraduate Computer Vision and postgraduate Differentiable Programming/Deep Learning modules, and has been instrumental in developing new machine learning curriculum including modules on Deep Learning, Natural Language Processing, and Machine Learning Technologies. His teaching excellence has been widely recognized through multiple awards and nominations. He also serves on the editorial board of IET Image Processing journal since 2020. Professor Hare is actively involved in several research groups including Vision, Learning and Control; International Centre for Ecohydraulics Research; Centre for Machine Intelligence; and Southampton Marine and Maritime Institute. His current projects include SemanticNews, JASH – AYURDA (Fish for Life), International Centre for Spatial Computational Learning, and various marine and geospatial research initiatives.

Mohammad Alianمشاهده پروفایل

استادیار

• Computer Architecture
• Data Center Systems
• Memory Systems
• +۵ مورد دیگر

Mohammad Alian is an Assistant Professor at the School of Electrical and Computer Engineering, Cornell University. He earned his Ph.D. (2020) and MS (2015) from UIUC and UW-Madison, respectively. His research focuses on redefining data-delivery hierarchies in data centers through computer architecture and systems research. Current Projects: Near-Memory Acceleration, Accelerator Fusion, Micro-Service Co-Design, Compound AI Systems, Memory Specialization, Gem5 Simulation Tools. Recent Awards: MICRO Hall of Fame (2025), NSF CAREER (2022), Miller Faculty Scholar (2023), Open Innovation Contest placements. His research spans Computer Architecture , Memory Systems , and Networked Computation , emphasizing algorithm-hardware co-design for distributed and heterogeneous computing. Recent work includes accelerating large-context LLMs (LongSight), optimizing gem5 simulation (Userspace Networking), and designing cross-accelerator chains (Data Motion Acceleration). ARG (Alian Research Group) collaborates with industry leaders like NVIDIA, Samsung, and SRC/DARPA JUMP 2.0 ACE Center. He serves on PC/Organizing Committees for top conferences (MICRO, ISCA, HPCA) and teaches Data Center Architecture (ECE 6960) and Digital Logic (ECE 2300) . Scientific Awards Inducted into MICRO Hall of Fame (2025) NSF CAREER Award (2022) IEEE Micro Top Picks Honorable Mention (2017) Best Paper Nominee - HPCA 2017, MICRO 2018 Open Innovation Contest: 2nd Place (2022), Finalist (2021) As Principal Investigator, he leads NSF-funded projects (CCRI, AI-Assisted Scaffolding) and co-leads the $31.5M SRC/DARPA JUMP 2.0 ACE Center . His lab develops open-source tools like dist-gem5 and DPDK on gem5, with industry support from NVIDIA (equipment donation) and Samsung.

Narayan B. Mandayamمشاهده پروفایل

استاد

• Resilience in Smart Cities
• Prospect Theory in Cyberphysical Systems
• Privacy and Security for IoT
• +۶ مورد دیگر

Narayan B. Mandayam is a Professor and Director of the Wireless Information Network Laboratory (WINLAB) at Rutgers University's Department of Electrical and Computer Engineering. He previously served as Chair of the department from 2016 to 2022. His research focuses on smart city resilience, IoT security, energy-efficient systems, and game-theoretic models for cyber-physical systems. Located in the Computing Research & Education Building (CoRE), he leads interdisciplinary projects addressing societal challenges in communication networks and infrastructure security. Education : B.Tech. (Hons) Electrical Engineering, Indian Institute of Technology, Kharagpur (1989) M.S. and Ph.D. Electrical Engineering, Rice University, Houston TX (1991–1994) Research Interests include: Prospect Theory applications in cloud security and smart grids Noncontiguous spectrum access and software-defined networks Modeling social knowledge creation on the internet Visual MIMO networks and metamaterial-based security His work bridges economics, machine learning, and wireless systems, with recent emphasis on 6G and IoT resilience. Key Awards : 2015 IEEE Communications Society Advances in Communications Award 2014 IEEE Donald G. Fink Award 2009 IEEE Fred W. Ellersick Prize 2018 Indian Institute of Technology Distinguished Alumnus Award His research outputs span 5G/6G innovations, security protocols using metamaterials, and game-theoretic models for adversarial scenarios. Current trends include leveraging AI for network optimization and mitigating radio interference impacts on weather forecasting. Lab & Leadership : As WINLAB Director, he oversees cutting-edge projects in wireless systems, collaborating with industry and government entities. His teams develop solutions for smart grid resilience, autonomous vehicle security, and federated learning incentivization.

Christian Hochbergerمشاهده پروفایل

استاد

• Reconfigurable Computing
• FPGA Architecture
• Embedded Systems
• +۳ مورد دیگر

Prof. Dr.-Ing. Christian Hochberger is a Professor at Technische Universität Darmstadt, affiliated with the Department of Computer Science. His research focuses on reconfigurable computing, FPGA architecture, embedded systems, and hardware/software co-design. He teaches courses such as 'Rechnersysteme I/II' and 'High-Level Synthese.' University: Technische Universität Darmstadt Department: Department of Computer Science Research interests span FPGA-based acceleration, CAD tools for reconfigurable systems, and energy-efficient computing. His work frequently addresses challenges in hardware design, parallelization, and fault tolerance. Publications from 2022-2024 highlight contributions to memristive devices, genetic circuit design automation, and CGRA optimization. His recent work emphasizes experimental methodologies and novel techniques in resistive switching and memristor-based FPGAs. Awards: None explicitly listed in the provided text.

Jose Nunez-Yanezمشاهده پروفایل

استاد

• Hardware Architectures
• Heterogeneous Systems
• Machine Learning Hardware
• +۱۲ مورد دیگر

Jose Nunez-Yanez is a Professor in hardware architectures for Machine Learning at Linköping University, affiliated with the Department of Electrical Engineering and the Division of Electronics and Computer Engineering (ELDA). He has over 20 years of experience in high-performance embedded hardware design and is associated with the Wallenberg Autonomous Systems Program (WASP). His research focuses on energy-efficient, adaptive, and parallel hardware systems for machine learning and signal processing. Key areas include FPGA-based acceleration, run-time reconfiguration, and edge AI. He has led significant research projects with industry and academic partners such as ARM, ST Microelectronics, ESA, and Intel. The recent publications highlight a strong trend toward hardware-aware optimization of Graph Neural Networks for edge deployment, emphasizing quantization, dynamic range analysis, and heterogeneous computing. This reflects a broader shift toward efficient deep learning on resource-constrained platforms. Best Paper Award, Adaptive Hardware Systems (AHS) Conference, ESA/NASA, 2014 Best Paper Award, Adaptive Reconfigurable Computing (ARC) Conference, 2016 He has supervised 20 PhD students and served as the program director of the Advanced Microelectronics Systems Engineering (AMSE) program at the University of Bristol, significantly expanding its reach. He has secured substantial funding as Principal Investigator and Co-Investigator with partners including Sensata Systems, DSTL, Qioptiq, and Xilinx. His industry fellowships include Royal Society and Marie Curie awards. He is a member of the Royal Society Grants Board, the EPSRC College, and the Marie Curie Alumni Association. His work bridges academia and industry, with active consultancy in FPGA optimization and lossless data compression.

• Software Engineering
• Software Architecture
• Autonomous Systems
• +۴ مورد دیگر

Patrizio Pelliccione is a Full Professor in Computer Science and Software Engineering at Gran Sasso Science Institute (GSSI) and an Associate Professor at Chalmers | University of Gothenburg. He holds a PhD in Computer Science from the University of L'Aquila. His research focuses on Software Architecture and Autonomous Systems, with applications in automotive and robotics domains. He has extensive international experience, including roles as Associate Professor at University of L'Aquila and Visiting Professor at Charles University. His research interests include software architecture design, autonomous systems, robotics, and automotive systems engineering. He actively collaborates with industry partners globally and has contributed to over 100 peer-reviewed publications across journals and conferences. Pelliccione chairs the Computer Science area at GSSI and leads research initiatives in value-based software ecosystems, continuous compliance, and architecture-driven development. He serves on program committees for major conferences like ICSE and ICSA, and has authored/co-authored books on software architecture and fault-tolerant systems. His work emphasizes practical industry applications, with contributions to automotive systems engineering frameworks, robotic mission specification languages, and safety-critical software development. Current projects explore AI integration in safety-critical domains like space systems and medical devices.

• Electromagnetism
• Electromagnetic Compatibility
• Antenna Design
• +۳ مورد دیگر

Prof. Valter Mariani Primiani is a Full Professor at the Polytechnic University of Marche within the Department of Information Engineering . His research focuses on electromagnetic fields, with expertise in reverberation chambers, wireless communication systems, and quantum computing applications. He holds office hours Monday to Friday from 9-13 at the Dipartimento di Ingegneria dell'Informazione (via Brecce Bianche, Ancona) and is reachable at v.mariani@univpm.it . His work emphasizes electromagnetic compatibility , 5G technology , and quantum-based simulations . Recent studies include optimizing reconfigurable intelligent surfaces for wireless networks and analyzing 5G signal propagation in controlled environments. He actively contributes to IEEE standards, such as the P2718 Working Group, and collaborates on open-source tools for electromagnetic measurement. His research integrates computational methods (e.g., FDTD simulations) with experimental setups in reverberation chambers, addressing challenges in dosimetry, channel emulation, and high-frequency performance. His work supports advancements in both theoretical and applied electromagnetics, with applications spanning from biomedical dosimetry to smart infrastructure.

Álvaro Michelena Grandíoمشاهده پروفایل

پژوهشگر

• Automated Systems Engineering
• Control Systems
• Power Electronics
• +۹ مورد دیگر

Álvaro Michelena Grandío is a researcher in the Department of Industrial Engineering at the University of A Coruña, specifically based at the Ferrol Engineering Polytechnic University College. His academic work focuses on automated systems engineering, with teaching responsibilities in control engineering, power electronics, data analysis, and smart monitoring systems across various degree and master's programs. Master in Industrial Engineering Automation and Industrial Electronics Engineering Master's Degree in Textile Technology and Sustainable Fashion Master's Degree in Energy Efficiency and Sustainability Master's Degree in Industrial Computing and Robotics His research interests lie at the intersection of intelligent systems, control engineering, and sustainable industrial technologies. He actively contributes to the development of IoT-based monitoring systems, embedded control solutions, and AI-driven energy applications. His work emphasizes practical, low-cost implementations for education and industry. The analysis of his recent publications reveals a strong focus on intelligent control, data analysis, and renewable energy systems. He frequently applies machine learning and neurocomputing techniques to industrial and environmental problems, including energy efficiency, sensor networks, and smart grids. His work bridges theoretical AI with real-world engineering applications. Álvaro Michelena is actively involved in research projects funded by the European Union, Telefónica, Navantia, and regional agencies. He supervises numerous final degree projects and master's theses, mentoring students in areas such as IoT, embedded systems, and intelligent control. He is a member of the 'Ciencia y Técnica Cibernética' (SUXI) research group and affiliated with the CITIC research center. He has contributed to multiple research projects in collaboration with institutions across Spain and Portugal. His work appears in high-impact journals like Neurocomputing , Applied Intelligence , and Sensors . He regularly presents at international conferences in Salamanca, Guimarães, and Bilbao, often in collaboration with multidisciplinary teams.

Jan H. Mikkelsenمشاهده پروفایل

دانشیار

• Antennas and Propagation
• RF Systems
• Microwave Engineering
• +۷ مورد دیگر

Jan H. Mikkelsen is an Associate Professor at Aalborg University's Department of Electronic Systems, part of the Technical Faculty of IT and Design. His research focuses on antennas, propagation, millimeter-wave systems, RF sensors, and biomedical engineering applications such as bone fracture monitoring. He has contributed to projects like the MARS2 satellite service system and the SOFT transceiver initiative, emphasizing software-defined radio and satellite communication. Key technical areas include power amplifiers, UWB technologies, RFID systems for cryogenic environments, and adaptive antenna designs using liquid metal. His work bridges theoretical research with practical applications, addressing challenges in mixed-signal circuit design, noise suppression, and 5G systems. Research interests span from fundamental material characterization (e.g., ferromagnetic resonance in hexaferrite crystals) to applied technologies like wearable Bluetooth earbud optimization and cryogenic RFID systems. His projects often involve collaboration with industry and government grants, such as the Danish Council for Independent Research. He has published extensively on topics ranging from spin-wave RF applications to biomedical impedance measurement techniques, reflecting his interdisciplinary expertise in RF engineering and biomedical systems. Notable contributions include reconfigurable Yagi-Uda antennas using liquid metal and advancements in nonlinear amplifier characterization. Despite no listed awards, his work has been cited in fields like wireless sensor networks and satellite communication. He advises on projects involving UWB localization and has a lab focus on antenna design and millimeter-wave systems.

• Wireless Communication
• Software-Defined Radio
• 5G
• +۹ مورد دیگر

Xianjun Jiao is a Researcher at the Department of Information Technology within the Faculty of Engineering and Architecture at Ghent University . His work focuses on wireless communication systems, particularly in the domains of Software-Defined Radio (SDR) , Wi-Fi , 5G , Radio Virtualization , and Network Densification . As a PhD supervisor, he has guided doctoral researchers including Felipe Augusto Pereira de Figueiredo Thijs Havinga Muhammad Aslam Baiheng Chen Robbe Gaeremynck on topics such as Next-Generation Wi-Fi Chip Design , Cell-Free Massive MIMO , and Hardware-Efficient Wireless Systems . His recent publications (2025) explore advanced Wi-Fi architectures, including OFDMA-based interference mitigation , joint transmission systems , and machine learning for handover optimization . Earlier works (2020-2024) address hardware efficiency in transceivers , time-sensitive networking , and NOMA schemes for concurrent transmission .

Georgi Gaydadjievمشاهده پروفایل

استاد

• Reconfigurable Computing
• Advanced Computer Architectures
• Parallel Memory Systems
• +۷ مورد دیگر

Georgi Gaydadjiev is a Professor in Innovative Computer Architecture at the University of Groningen's Faculty of Science and Engineering. Previously, he held roles including Chair Professor at TU Delft and Chalmers University of Technology, and served as VP of Dataflow Software Engineering at Maxeler Technologies. He holds a PhD from TU Delft and has over 35 years of industry and academic experience, focusing on reconfigurable computing, high-performance systems, and energy-efficient architectures. His research spans embedded systems, fault tolerance, and scalable architectures. Education: MSc Electrical Engineering (TU Delft), PhD (TU Delft), studies at Voenmeh (Baltic State Technical University). Research interests include reconfigurable computing, advanced architectures, parallel systems, and HPC. He leads projects funded by EU, Google, and Swedish Research Councils, addressing exascale computing and customized hardware. His work has been recognized with awards like the CES Design Showcase (1999) and best paper awards at ICS'10 and WiSTP'07. He advises PhD students and oversees labs like Maxeler IoT-Labs BV, focusing on deploying dataflow technology beyond data centers.

Joel S. Emerمشاهده پروفایل

استاد

• Accelerator Architectures for Sparse Computation
• Accelerator Architectures for Deep Learning
• Spatial Processing Architectures
• +۵ مورد دیگر

Joel S. Emer is a Professor of the Practice in MIT's Department of Electrical Engineering and Computer Science (EECS) and a Senior Distinguished Research Scientist at NVIDIA. His research focuses on computer architecture, processor micro-architecture, and performance modeling. He has contributed to advancements in simultaneous multithreading, cache optimization, and reliability analysis. Emer holds over 25 patents and has published over 60 papers, earning awards like the IEEE Rau Award and induction into the National Academy of Engineering. Education: Ph.D., Electrical Engineering, University of Illinois Urbana-Champaign, 1979 M.S., Electrical Engineering, Purdue University, 1975 B.S., Electrical Engineering, Purdue University, 1974 (highest honors) Research interests include accelerator architectures for sparse computation and deep learning, spatial processing, memory hierarchy design, and reliability analysis. His work on Eyeriss and other accelerators has shaped energy-efficient neural network hardware. Recent projects explore hierarchical structured sparsity (HSS) and compute-in-memory (CIM) techniques. Key awards include the ISCA Best Paper Session (2024), IEEE Micro Top Picks (2024), and the SIGMICRO Test of Time Award (2022). He co-advises students with Prof. Vivienne Sze, focusing on sparse tensor acceleration and energy-efficient designs. Awards: 2023 IEEE Rau Award 2022 IASED Lifetime Achievement Award 2020 National Academy of Engineering Membership 2009 Eckert-Mauchly Award Grants and collaborations span industry partnerships (e.g., NVIDIA) and academic initiatives. Emer leads the Emze Group, exploring hardware-software co-design for emerging architectures. Current work includes sparse tensor accelerators (e.g., HighLight, Tailors) and modeling tools like Sparseloop and Accelergy.