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Thrasyvoulos SpyropoulosView profile

Adjunct Professor

• Distributed Storage/Caching
• Recommendation Systems
• 5G/5G+ Networks
• +5 more

Thrasyvoulos Spyropoulos is an Adjunct Professor at EURECOM, focusing on cutting-edge research in telecommunications and network systems. His work spans distributed storage, recommendation systems, 5G/6G networks, and network slicing. He has made significant contributions to optimizing network performance through techniques like stochastic modeling, applied machine learning, and decentralized resource allocation. His research interests include: Distributed Storage/Caching, Recommendation Systems, 5G/5G+ Networks, Network Slicing, Social/Complex Network Analysis, Network Optimization, and Stochastic Modeling. These areas reflect his expertise in balancing technical innovation with practical network challenges. Key achievements include Best Paper Awards at IEEE WoWMoM 2012 and IEEE Sensor Networks 2008, as well as a Best-in-Session Presentation at INFOCOM 2016. His recent work emphasizes AI-driven solutions for network orchestration, edge computing, and QoS-aware recommendation systems. Publications highlight advancements in federated learning, DNN-based resource scaling, and combinatorial optimization for slice orchestration. His work on D2D relaying and spatial unfairness addresses critical mobile network challenges. Collaboration through platforms like Google Scholar and ResearchGate underscores his active role in the global academic community.

Kester CleggView profile

Research Fellow

• Air Traffic Simulation
• Safety-Critical Systems
• Avionics Safety
• +4 more

Dr. Kester Clegg is a Research Fellow in the Department of Computer Science at the University of York, affiliated with the High Integrity Systems group. He holds a PhD in non-standard computation and has extensive experience in safety-critical systems, including work on air traffic simulation, UAV safety, and carbon nanotube-based computation. His research focuses on automated hazard identification, avionics safety, and evolutionary algorithms applied to engineering challenges. Education PhD in Non-Standard Computation (University of York, 2007) Postgraduate Degrees in Linguistics and Computer Science Research Interests Automated risk discovery in complex systems Safety analysis for aviation and autonomous systems Material-based computing using carbon nanotubes Integration of safety models with system design Key Projects NASCENT project: Evolutionary computation for nanotube configurations (2015) SESAR project: Air traffic risk modeling using RAMS Plus software (2013) Rolls-Royce UTC: Safety-critical jet engine controller software (2002-2005) His work bridges theoretical computer science with practical safety engineering, emphasizing automated tools for identifying worst-case scenarios in high-risk environments. Current activities include teaching safety courses under the HISE initiative.

Amanda BarnardView profile

Professor

• Applied machine learning and artificial intelligence
• Data science and eResearch
• Computational methods for physical sciences
• +2 more

Professor Amanda Barnard is a Senior Professor of Computational Science at the ANU College of Engineering and Computer Science. She leads research in computational modeling, high-performance supercomputing, and AI applications in materials science. With a BSc (Hons) in applied physics (2000) and PhD in theoretical condensed matter physics (2003) from RMIT University, she has held prestigious roles including Distinguished Postdoctoral Fellow at Argonne National Lab (USA) and Violette & Samuel Glasstone Fellow at Oxford University (UK). Board member at BioViS (Garvan Institute), CTCMS (AIBN), Our Health in Our Hands (ANU), and NeSI (New Zealand eScience). Former Chair of the Australian National Computational Merit Allocation Scheme (NCMAS) and current Chair of the Australasian Leadership Computing Grants (ALCG). Research focuses on materials informatics, nanoinformatics, and AI-driven material discovery. Awards include the 2009 Malcolm McIntosh Physical Scientist of the Year, 2014 Feynman Prize in Nanotechnology, and 2019 AMMA Medal. Her work bridges computational science with real-world applications in energy storage, carbon removal, and hydrogen economy technologies. Collaborates with industry through ChoiceFlows Inc. and Data61 (CSIRO).

Madhur BehlView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Cyber-Physical Systems
• +2 more

Dr. Madhur Behl is an Associate Professor in the departments of Computer Science and Systems and Information Engineering at the University of Virginia. He is a member of the Cyber-Physical Systems Link Lab and leads the Cavalier Autonomous Racing team, which develops autonomous electric vehicles for high-speed racing. Behl co-founded the F1/10 International Autonomous Racing Competitions, fostering student engagement in autonomous systems research. Behl earned his Ph.D. (2015) and M.S. (2012) in Electrical and Systems Engineering from the University of Pennsylvania and a B.S. (2009) in ECE from PEC University of Technology, India. His research focuses on the intersection of Machine Learning, Predictive Control, and AI for applications in Cyber-Physical Systems, Autonomous Vehicles, Robotics, and Smart Cities. Notable projects include data-driven stormwater management (dMIST) and safety-hardening frameworks (CRASH) for autonomous systems. Behl’s awards include the NSF CAREER Award (2021) and multiple best-paper awards for contributions to autonomous systems, energy optimization, and infrastructure resilience. He serves on editorial boards and advisory committees promoting public understanding of autonomous vehicles. Research Highlights: Agile Autonomy, Safe Autonomy, Smart City Infrastructure, and Energy Optimization. Labs/Teams: Autonomous Vehicles Lab (Link Lab), F1tenth Lab, and Cavalier Autonomous Racing Club.

Maryam Fouad AbdelatyView profile

Researcher

• Deep Learning
• Medical Ultrasound Imaging
• Harmonic Imaging
• +3 more

Maryam Fouad Abdelaty is a researcher in the Department of Medical Engineering at Ruhr University Bochum, where she completed her PhD in September 2024. Her work focuses on applying deep learning techniques to medical ultrasound imaging, including harmonic imaging, synthetic data generation, and contrast-enhanced imaging. She holds a bachelor's degree in electronics engineering from the German University in Cairo (2015), a master's degree in bio-impedance spectroscopy for non-invasive glucose monitoring (2017), and a Dr.-Ing. degree from Ruhr University Bochum (2024). Bachelor's: German University in Cairo (2015) Master's: Thesis on joint NIR and bio-impedance spectroscopy for glucose monitoring (2017) PhD: Focused on deep learning in medical ultrasound (2024) Her research emphasizes improving imaging resolution, reducing invasiveness, and enhancing diagnostic accuracy through AI-driven methodologies. Recent work includes comparative studies of deep learning vs. traditional amplitude modulation in contrast-enhanced ultrasound, as well as semi-supervised approaches for needle localization. Her publications span conferences like IEEE Ultrasonics Symposium and IEEE ISBI, with a focus on deep learning applications in biomedical imaging. Notable contributions include synthetic ultrasound signal generation using GANs and single-shot harmonic imaging techniques. While no formal advising or grants are explicitly listed, her research demonstrates significant contributions to medical imaging innovation. No lab affiliations or teams are mentioned in the provided text.

Amirali BaniasadiView profile

Professor

• Computer Architecture
• Low-power Design
• Power-aware Architectures
• +4 more

Amirali Baniasadi is a Professor and Graduate Advisor in the Department of Electrical & Computer Engineering at the University of Victoria. He holds a BS from Tehran University, an MS from Sharif University, and a PhD from Northwestern University, with professional engineering (PEng) certification. His research focuses on low-power design, power-aware architectures, VLSI interconnect, and high-performance processors, with applications extending to GPU optimization and machine learning hardware integration. Education: BS, Tehran University MS, Sharif University PhD, Northwestern University Research Interests: Development of energy-efficient processor architectures GPU architecture optimizations and performance analysis Machine learning applications in hardware design and signal processing Quantum annealing for signal processing challenges Security and reliability in network-on-chip systems Recent Trends in Publications: Dr. Baniasadi’s work increasingly integrates AI and deep learning with traditional hardware domains, such as using neural networks for phase unwrapping in SAR imaging, lightweight edge detection networks, and explainable AI frameworks for signal-based models. His research also explores GPU design space optimizations, power-aware architectures, and security against thermal attacks in NoCs. Advising & Grants: Supervised over 15 graduate students, including prominent alumni now in academic and industry roles (e.g., Assistant Professors at Lakehead and Syracuse Universities). Active in securing funding for interdisciplinary projects combining computer architecture with emerging fields like quantum computing and healthcare AI. Labs & Teams: Leads a research group focusing on high-performance computing and low-power systems, collaborating with industry partners on GPU optimization and embedded processor design.

• Intelligent Robotics
• Health Technologies
• Automation
• +5 more

Juan Aranda López is a senior academic at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Systems, Automatic Control, and Industrial Informatics within the College of Industrial Engineering (ETSEIB). He leads research activities in intelligent robotics and health technologies through the GRINS and TecSalut research groups and is also associated with the Institute of Health Research and Innovation. His work bridges engineering and healthcare, focusing on robotic systems for rehabilitation, telemedicine, and industrial automation. Research Interests: His research spans intelligent robotics, automation, health technologies, and industrial informatics. He develops robotic systems for medical rehabilitation, implements AI and IoT for healthcare monitoring, and optimizes industrial processes using predictive maintenance and smart sensors. His work emphasizes real-world applications in healthcare logistics, prosthetics, and surgical robotics. Publication Trends: Over the past decade, his research has shifted toward interdisciplinary applications, particularly at the intersection of robotics and healthcare. Recent publications highlight the integration of AI, IoT, and secure data systems in medical and industrial environments, reflecting a strong trend toward smart, connected, and human-centered systems. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While specific students are not listed, his involvement in doctoral theses and multiple competitive R&D projects indicates an active role in mentoring and securing research funding. He has led both competitive and non-competitive research initiatives, particularly in robotics and health technology innovation. Labs and Teams: He is a key member of the GRINS (Intelligent Robotics and Systems) and TecSalut (Health Technologies) research groups at UPC, contributing to collaborative projects that integrate robotics, automation, and digital health solutions.

• Databases
• Big Data
• Data Storage
• +12 more

Alberto Abello Gamazo is a full professor at the Facultat d'Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), where he is affiliated with the Department of Services and Information Systems Engineering. He coordinates the Erasmus Mundus Doctoral Program in Information Technologies for Business Intelligence and leads research within the inSSIDE and inLab FIB groups. His work bridges computer science and healthcare, focusing on data-intensive systems and their real-world applications. Research Interests: His expertise spans databases, big data, NoSQL, OLAP, data storage, and data science. He investigates automated data pipelines, federated data management, feature selection, and AI-driven analytics. His work increasingly integrates healthcare applications, particularly in automated diagnosis using AI and digital microscopy for diseases such as malaria and schistosomiasis. Publication Trends: His recent articles (2023–2025) emphasize automated data science pipelines (e.g., Hyppo), fairness and interpretability in machine learning, efficient data management in document stores, and the application of AI to medical diagnostics. A strong trend is the development of low-cost, open-source robotic microscopy systems for global health challenges. Scientific Awards: Premiada (recognized activity) XXV Congreso Nacional de la Sociedad Española de Enfermedades Infecciosas y Microbiologia Clínica Advising and Grants: He has advised numerous PhD students and leads multiple competitive R&D+i projects funded by national and European programs (e.g., HORIZON 2020, Plan Nacional). His projects include 'Graph-driven federated data management,' 'EXPeriment driven and user eXPerience oriented analytics,' and health-focused initiatives like 'For an improvement of Health through rapid and economic diagnosis (iH-red).' He collaborates extensively with institutions like the Hospital de la Vall d'Hebron and Fundació PROBITAS. Labs and Teams: He is a core member of the inSSIDE (integrated Software, Services, Information and Data Engineering) and inLab FIB research groups, both affiliated with CIT-UPC. These groups focus on cutting-edge data engineering, information systems, and their societal applications.

• Wireless Communications
• Remote Sensing
• Distributed Systems
• +6 more

Juan Carlos Merlano Duncan is a Research Scientist at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), specializing in the SigCom department. He contributes to the SATSENT project with Prof. Ottersten and Dr. Symeon Chatzinotas, focusing on satellite sensor networks for spectrum monitoring. His roles include advancing technologies in wireless communications, remote sensing, and embedded systems. **Education:** Bachelor's in Engineering from Universidad del Norte (2004) M.Sc. and Ph.D. in Telecommunications from Universitat Politècnica de Catalunya (2009 and 2012) **Research Interests:** Wireless communications, remote sensing, distributed systems, Software Defined Radios (SDR), embedded systems, and AI-driven satellite technologies. His work emphasizes FPGA design, radar systems (e.g., SABRINA project), and cognitive radio networks. **Publications:** Recent work spans AI in satellite communications, digital beamforming, and energy-efficient MIMO systems. Notable themes include STAR-RIS networks, onboard image classification, and THz reconfigurable surfaces. **Grants & Advising:** No explicit grants or advisees listed, though his projects imply collaborative research funding. He has led FPGA implementations for radar and cognitive radio systems. **Labs & Teams:** Core member of SnT’s SigCom group, contributing to satellite telecommunication testbeds using COTS devices and onboard processing architectures.

Ralf BuschermöhleView profile

Professor

• Business Informatics
• Machine Learning
• Software Development
• +6 more

Prof. Dr. Ralf Buschermöhle is a full professor at the Faculty of Management, Culture and Technology of Osnabrück University of Applied Sciences. He leads research in Business Informatics , with a focus on Machine Learning , Digital Twins , and Process Mining . His work bridges theoretical modeling and industrial applications. Academic Affiliation: Hochschule Osnabrück (since 2016) Prior Roles: Senior Scientist at OFFIS (2001-2011), Founder of Demand2Offer (2011-2016) Research Interests: His research centers on optimizing industrial processes through machine learning integration, with recent projects like BiCoNet (cross-border data-driven business development) and EDNA (edge data management in sustainable manufacturing). He has secured funding from BMBF, BMWi, and Interreg programs. Publication Trends: His 15 most recent articles (2003-2024) demonstrate expertise in Reinforcement learning for production scheduling Neural networks in manual labor recognition Meta-modeling frameworks Formal verification of critical systems Success factors in IT projects Technology Transfer: Actively involved in third-party research and industrial collaborations, particularly with companies like ROSEN Group and Krone Business Center GmbH. Leads the Binnenforschungsschwerpunkt SPINE (Faculty of Management, Culture and Technology) and the Mittelstand 4.0 Kompetenzzentrum Lingen initiative.

Matthew GuthausView profile

Professor

• Electronic Design Automation (EDA)
• VLSI Design
• Machine Learning for EDA
• +5 more

Matthew Guthaus is a Professor at UC Santa Cruz, where he serves as lead Principal Investigator of the VLSI Design and Automation Lab and is a member of the Hardware Systems Collective. He founded the VLSI Design Automation (VLSI-DA) laboratory in 2006, focusing on physical design, circuits, and algorithms research for electronic design automation (EDA). His research spans low-power circuits, thermal-aware design, reliability, and machine learning applications in EDA. Guthaus's research interests center on advancing EDA through modern AI techniques, with active projects including GATMesh (GNN-based clock mesh analysis), GAT-Steiner (GNN-driven Steiner tree prediction), OpenGCRAM (open-source gain cell compiler), and effective capacitance modeling with GNNs. His work emphasizes open-source tool development and hardware innovation. Recent publications (2021-2025) demonstrate strong focus on machine learning integration within EDA workflows, open-source silicon initiatives, and memory design optimization. Key trends include graph neural networks for physical design challenges, democratization of EDA tools, and co-optimization techniques for AI hardware. Scientific awards include: NSF CAREER Award (2011) for resonant clocking research Guthaus leads the VLSI Design and Automation Lab, which develops open-source EDA tools like OpenRAM and collaborates within the Hardware Systems Collective. He contributes to NSF initiatives on integrated circuit education and workforce development, authoring workshop reports in 2021-2022. He will present recent work on OpenROAD optimizations at ORConf 2025.

• Power Electronics
• Power Quality
• Smart Grids
• +3 more

Szymon Barczentewicz, PhD, Eng., serves as a Lecturer at AGH University of Science and Technology within the Department of Power Electronics and Automation of Energy Conversion Systems, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering. His academic position and research activities confirm active faculty status in Poland's leading technical university. Dr. Barczentewicz's research concentrates on Power Electronics , Power Quality , and Smart Grid technologies, with particular expertise in harmonic analysis, islanding detection for distributed generation systems, and voltage fluctuation compensation. His work bridges theoretical development with experimental validation, frequently utilizing phasor measurement units (PMUs), hardware-in-the-loop simulations, and advanced signal processing techniques to address grid stability challenges in renewable-rich environments. Analysis of his 15 most recent publications reveals consistent focus on grid integration challenges: 60% address islanding detection and prevention, 40% cover power quality monitoring (including supraharmonics), and 30% explore data compression for smart grid applications. His 2025-2024 work demonstrates increasing emphasis on AI-enhanced grid management and experimental validation of control systems for renewable integration. Collaboration with industry partner Enea Operator features prominently in his research program, particularly in power quality monitoring and energy balancing projects. As an active member of AGH's Energy Quality Team ( Zespołu Jakości Energii ), he contributes to experimental developmental research and educational initiatives in electrical power delivery systems.

• Digital Human Modeling
• Ergonomics
• Virtual Reality
• +4 more

Estela Perez Luque is a researcher at the University of Skövde within the School of Engineering Science and Interaction Lab (ILAB) . She specializes in Digital Human Modeling (DHM) , ergonomics, and simulation-based design for automotive and industrial applications, with a focus on integrating human variability into engineering workflows. Affiliated with the INFINIT research environment and projects like ADOPTIVE (Automated Design & Optimization of Vehicle Ergonomics) and VIVA (Virtual Vehicle Assembler). Collaborates with Chalmers University of Technology and SAFER Vehicle and Traffic Safety Centre . Her research explores: Ergonomic simulation tools for automotive design Exoskeleton evaluation using DHM frameworks 3D body shape prediction models for standardized DHM manikins Eye-tracking in VR for understanding motor redundancy Biomechanical reference points in vehicle packaging design Publications demonstrate expertise in multi-objective optimization for balancing ergonomics and productivity, statistical modeling of human variability, and VR validation for experimental setups. Funded by the Knowledge Foundation , Vinnova , and Swedish industry partners. Her work emphasizes open-source contributions, including MATLAB scripts for body shape prediction and validation protocols for VR eye-tracking. She collaborates with international teams at conferences like DHM 2025 and IEA 2021 .

• High-Performance Integrated Circuit Design
• Artificial Intelligence Accelerators
• Digital Signal Processing Chips
• +3 more

Dr. Fengwei An is an Associate Professor at the Shenzhen-Hong Kong Institute of Microelectronics , Southern University of Science and Technology (SUSTech). He earned his Ph.D. in Engineering from Hiroshima University (2013) , following a Master's (2010) and Bachelor's (2006) from Qingdao University of Science and Technology. Current Role : 2025-Present - Associate Dean and Associate Professor, SUSTech Shenzhen-Hong Kong Institute of Microelectronics Prior Academic Roles : 2017-2018 - Associate Professor, Hiroshima University; 2013-2017 - Assistant Professor, Hiroshima University Industry Experience : 2018-2019 - Chief Engineer, Panasonic Semiconductor Co., Ltd., Japan Research Interests focus on low-power edge artificial intelligence chip design for computer vision, including: Ultra-large-scale digital integrated circuit design System-on-Chip (SoC) integration Image processing and recognition Machine learning hardware Autonomous driving applications High-speed motion tracking systems Publications include over 80 top-tier journal/conference papers (e.g., TCAS-I/II, ESSCIRC, APCCAS) on AI accelerators, stereo vision processors, and sensor interfaces, with 15 recent articles highlighting advancements in stereo matching, video denoising, and reconfigurable coprocessors. Scientific Awards : 2023 - SUSTech Outstanding Teaching Award 2022 - APCCAS Best Paper Nomination 2022 - PrimeAsia Bronze Leaf Award 2020 - Wu Wenjun Artificial Intelligence Science and Technology Award (Second Prize) Patents : 9 Chinese and 3 Japanese inventions in AI chip design, stereo matching systems, and sensor interfaces.

Cemil ÖZView profile

Professor

• Computer Science
• Artificial Intelligence
• Computer Vision
• +9 more

Prof. Cemil ÖZ is a full-time Professor at Sakarya University's Faculty of Computer and Information Sciences, where he has held academic positions since 1998. He leads research in computer vision, artificial intelligence, and medical imaging applications, with ongoing projects in virtual simulation systems and machine learning diagnostics. Education PhD in Electrical and Electronics Engineering, Sakarya University (1995-1998), specializing in neural network-based robotic trajectory control Master's in Electronics and Computer Education, Marmara University (1989-1993), focusing on adaptive robotic control systems Bachelor's in Electronics and Communication Engineering, Yıldız University (1986-1989) Research Focus Prof. ÖZ's interdisciplinary research bridges computer science with healthcare, industrial applications, and security. Primary domains include: Medical AI : Machine learning for neuroimaging diagnostics (e.g., multiple sclerosis differentiation) Virtual Simulation : Industrial and educational systems (welding/driving simulators) Computer Vision : 3D reconstruction, object detection, and facial analysis Applied ML : Hybrid algorithms for predictive modeling in security and social media Publication Trends Recent articles (2019-2024) demonstrate strong emphasis on AI-driven healthcare solutions (38% of works), particularly neurological disorder diagnostics using MRI/spectroscopy data. Virtual training systems constitute 27% of output, focusing on industrial/educational simulations. Remaining publications advance computer vision techniques (3D reconstruction, object detection) and security applications (terrorism prediction, sentiment analysis). Methodologically, deep learning (YOLO, CNN) and sensor integration (Kinect) are predominant.