جست‌وجوی استاد و پژوهشگر

• Data Management
• Database Systems
• Graph Data Management
• +۴ مورد دیگر

Professor Chengfei Liu is a faculty member at Swinburne University of Technology, leading the Web and Data Engineering research group and serving as focus area leader for Knowledge and Data Intensive Systems in the SUCCESS Centre. His research focuses on web data management, advanced database systems, graph data, and workflow models. He has held academic positions at the University of South Australia, University of Technology Sydney, and the University of Queensland's DSTC. His work spans data management, distributed systems, and information systems, with notable contributions to cohesive subgraph discovery and efficient algorithms. Research interests include keyword queries, uncertain data, graph databases, and artifact-centric workflows. His awards include the Vice-Chancellor's Research Excellence Award (2007) and multiple best paper/demo awards at top conferences. Professor Liu supervises numerous PhD students and holds grants from the Australian Research Council (ARC), including projects on dynamic networks, heterogeneous graphs, and information resilience. He has authored over 297 publications, with recent work addressing topics like Bitcoin transaction prediction, federated subspace clustering, and multimodal eating disorder detection. His professional roles include program committee memberships at SIGMOD, ICDM, and CIKM, reflecting his influence in data science and database research.

Masoud Sanayeiمشاهده پروفایل

استاد

• Bridge structural health monitoring
• Building train-induced vibrations
• Nondestructive testing of full-scale structures
• +۴ مورد دیگر

Masoud Sanayei is a Professor of Civil and Environmental Engineering at Tufts University's School of Engineering, where he has been a faculty member since 1986. He currently serves as an active professor in the Civil and Environmental Engineering department, teaching courses in structural analysis, finite element analysis, and structural dynamics. Dr. Sanayei's research focuses on bridge structural health monitoring (SHM), building train-induced vibrations, nondestructive testing of full-scale structures, and fatigue life prediction of structures with nonproportional multi-axial loading. His work has significantly advanced the field of structural health monitoring through the development of bridge signatures, an innovative nonparametric probabilistic method using operational bridge responses due to daily traffic for structural condition assessment. His recent publications demonstrate a strong trend toward integrating advanced computational methods including deep learning and machine learning with traditional structural engineering approaches. His research spans from fundamental vibration analysis to practical applications in bridge and building monitoring, with particular emphasis on train-induced vibrations in over-track structures and fatigue assessment of complex connections. Albert Nelson Marquis Lifetime Achievement Award by Marquis Who's Who 2017 Professor of the Year Award, Tufts University Dr. Sanayei has secured numerous research grants from the National Science Foundation, Federal Highway Administration, and Massachusetts Port Authority, focusing on structural performance monitoring, fatigue assessment, and asset management. His professional activities include editorial board membership for the Journal of Bridge Engineering (ASCE), committee leadership in the AISC/ASCE Steel Bridge Competition, and active participation in technical committees related to structural identification. His laboratory work includes extensive full-scale testing on structures such as the Tobin Bridge in Boston, Powder Mill Bridge in Massachusetts, Memorial Bridge in New Hampshire, Boston Convention Center, Hynes Convention Center, TD Garden, MIT Brain and Cognitive Science Center, and over-train-track buildings atop Shenzhen Metro Depot in China.

Alister Burrمشاهده پروفایل

استاد

• Wireless Communication Systems
• MIMO Systems
• Modulation and Coding
• +۴ مورد دیگر

Alister Burr is a Professor of Communications at the University of York, affiliated with the School of Physics, Engineering and Technology. He holds a BSc from the University of Southampton and a PhD from the University of Bristol. His research focuses on wireless communication systems, including MIMO, modulation/coding, and physical layer network coding. He has authored over 150 publications and a textbook on wireless communications. Awards include the J. Langham Thompson Premium (2002) and a Royal Society Fellowship (1999). He chairs the European COST 2100 programme and has held visiting roles at Vienna University of Technology. Current research spans 5G/6G, distributed MIMO, and iterative techniques. His work addresses challenges in channel modeling, precoding, and energy efficiency. Education: BSc Electronic Engineering, University of Southampton (1979) PhD, University of Bristol (1984) Affiliations: Chair, COST Action IC1004 (Green Smart Environments) Associate Editor, IEEE Communications Letters (2008-2011) Awards: Best Paper Award (European Wireless 2012) Grants/Projects: EU-funded COST 2100 programme Research on 5G NR, cell-free MIMO, and federated learning Labs/Teams: Leading teams in distributed wireless systems and network coding

Michael Devetsikiotisمشاهده پروفایل

استاد

• Telecommunication Networks
• Smart Grid Communications
• Cyber-Physical Systems
• +۵ مورد دیگر

Michael Devetsikiotis is a Professor and Chair of the Department of Electrical and Computer Engineering at the University of New Mexico (UNM), part of the School of Engineering. He holds a Ph.D. from North Carolina State University (1993). His career includes roles as an Assistant/Associate Professor at Carleton University (1996–1998), Associate/Professor at North Carolina State (2000–2016), and leadership in UNM's ECE Department since 2016. He specializes in telecommunication networks, smart grids, IoT, and quantum information science. Education: Ph.D. in Electrical Engineering, North Carolina State University, 1993 M.S. in Electrical Engineering, North Carolina State University, 1990 Dipl. Ing. in Electrical Engineering, Aristotle University of Thessaloniki, Greece, 1988 Research Interests: Focuses on network design, smart grid communications, cyber-physical systems, and quantum technologies. He has published over 180 refereed papers and secured funding from NSF, NSERC, Cisco, and IBM. Notable projects include leading UNM’s Quantum Information Science program and managing the $20M NSF EPSCoR “SMART” Grid initiative. Articles Trends: Recent work emphasizes AI-driven network management (e.g., LSTM models for 5G/6G), blockchain for secure IoT/satellite systems, and quantum computing. Earlier contributions addressed EV charging infrastructure and smart grid resilience. Scientific Awards: IEEE Fellow (2012) NC State ECE Alumni Hall of Fame (2017) Advising & Grants: Directed UNM’s NSF Quantum Computing Faculty Fellowship (2020), enabling hires in quantum engineering. Previously managed a 800-student ECE graduate program at NC State. Active in IEEE leadership roles, including Distinguished Lecturer (2008–2011) and Chair of flagship conference committees. Labs & Teams: Spearheaded UNM’s IBM Q-Hub affiliation (2020), advancing quantum research collaboration. Leads interdisciplinary teams for smart city defense, blockchain energy markets, and 6G network automation.

Jeremy Singerمشاهده پروفایل

دانشیار

• Systems Software
• Garbage Collection
• Compilers
• +۴ مورد دیگر

Jeremy Singer is a Reader in Programming Language Implementation at the School of Computing Science, University of Glasgow. He specializes in systems software, compilers, garbage collection, and secure runtime environments. His research focuses on advancing memory management techniques, many-core parallelism, and edge computing. Singer holds a PhD from the University of Cambridge (2006) in Static Program Analysis based on Virtual Register Renaming. He is a Senior Member of the ACM and a Fellow of the BCS. His academic roles include supervising PhD students in areas such as quantum memory management, federated graph neural networks, and secure memory systems. He has led multiple EPSRC-funded projects, including M4Secure (2023-2026), Capable VMs (2020-2024), and FRuIT (2017-2019). He teaches courses like COMPSCI1016 (Computational Thinking) and COMPSCI4021 (Functional Programming in Haskell). Singer’s research spans compiler design, runtime systems, and security. Notable contributions include work on SSA-based compiler techniques, Raspberry Pi cluster systems, and secure microPython implementations. He has authored over 80 publications and co-developed MOOCs on functional programming and data science. His awards include Fellow of the BCS and Senior ACM Membership. Current research interests include secure memory management, compiler optimizations for heterogeneous architectures, and edge computing security.

Kin K. Leungمشاهده پروفایل

استاد

• Wireless Communications
• Communication Networks
• Machine Learning
• +۴ مورد دیگر

Kin K. Leung is the Tanaka Chair Professor in the Electrical and Electronic Engineering and Computing Departments at Imperial College London. He serves as the Head of the Communications & Signal Processing Group (2009-2024) and Deputy Director of the University Defence Research Centre in Signal Processing. Previously, he held senior technical roles at AT&T Bell Labs and Lucent Technologies. Education: B.S. from Chinese University of Hong Kong (1980), M.S. and Ph.D. from University of California, Los Angeles (1982, 1985). Research Interests: Focuses on wireless technologies, communication networks, stochastic modeling, machine learning, optimization, and emerging quantum computing applications. His work integrates theoretical frameworks with practical implementations in multi-antenna systems, cross-layer network designs, and distributed quantum architectures. Publication Trends: Recent works (2021-2025) demonstrate a strong focus on quantum computing integration with networking systems, federated learning optimizations for edge devices, and reinforcement learning solutions for resource allocation in wireless environments. Awards and Honors: Fellow of Royal Academy of Engineering (2022) IEEE Fellow (2001) IET Fellow (2021) Royal Society Wolfson Research Merit Award (2004-2009) IEEE Communications Society Leonard G. Abraham Prize (2021) IEEE Best Survey Paper Award (2022) Grants and Leadership: Principal Investigator for EPSRC TITAN/HASC Com Hubs, DCIT Centre, and DQC & App (distributed quantum computing). Leads the Communications & Signal Processing Group and co-directs the School of Convergence Science in Space, Security and Telecoms.

• Machine Learning
• Medical Data Analysis
• Time-Series Data Analysis
• +۳ مورد دیگر

Dr. Weitong Chen is a Senior Lecturer at the School of Computer and Mathematical Sciences, University of Adelaide, and an ARC EC Industry Fellow at the Australian Institute for Machine Learning (AIML). He holds a PhD from the University of Queensland (2020), with prior roles as a Post-Doc Research Fellow and Associate Lecturer there. His research focuses on machine learning applications in medical data, particularly time-series analysis, semi-supervised learning, and IoT. He collaborates widely across academia, industry, and government, supported by multiple grants. His work emphasizes healthcare applications, adversarial robustness, federated learning, and unlearning mechanisms. Education: PhD in Machine Learning, University of Queensland (2020) Master's Degree, University of Queensland Bachelor's Degree, Griffith University Research Interests: Medical Data Analysis (e.g., EHRs, radiology) Time-Series Modeling (healthcare IoT, irregular data) Adversarial Machine Learning (backdoor attacks, robustness) Federated Learning (modality incompleteness, clustered frameworks) Data Privacy (unlearning, compliance) Grants & Collaborations: ARC EC Industry Fellowship Industry partnerships in healthcare and IoT Labs/Teams: Australian Institute for Machine Learning (AIML) Cross-disciplinary health tech collaborations

Cihan H. Dagliمشاهده پروفایل

استاد

• Systems Engineering
• Cyber-Physical Systems
• Machine Learning
• +۷ مورد دیگر

Cihan H. Dagli is a Professor of Systems Engineering and Engineering Management at Missouri University of Science and Technology (Missouri S&T). He is the founder of the Systems Engineering Graduate Program at Missouri S&T and directs the Smart Engineering Systems Lab (SESL). He serves as a Senior Investigator in the DoD Systems Engineering Research Center-URAC. His academic journey includes a Ph.D. in Applied Operations Research from the University of Birmingham (UK), with early work on water resource systems and evolving into systems engineering, cyber-physical systems, and machine learning. Research interests span systems architecting, computational intelligence, and deep learning applications in complex systems such as transportation, energy, and healthcare. He has secured over $25 million in grants, including a $10M DoD grant for the Systems Engineering Research Center-UARC. With 450+ publications and an h-index of 31, he is a Fellow of INCOSE (2008), IIE (2009), and IFPR (2019). He chairs the Complex Adaptive Systems conference series and advises over 35 PhD students across Engineering Management, Computer Science, and Systems Engineering. Recent work focuses on AI-driven kidney transplant optimization, meta-architectures for cyber-physical systems, and system-of-systems resilience. His educational contributions include developing the SEEE portal for systems engineering education and pioneering courses like Advanced Neural Networks and Complex System Modeling. Education: Ph.D., Applied Operations Research, University of Birmingham, UK (1979) Key Roles: Director of SESL, INCOSE Fellow, Omega Alpha Society Board Chairman Scientific Awards: Fellow of INCOSE (2008) Fellow of IIE (2009) Fellow of IFPR (2019) Member of Omega Alpha Association (2013) Advising and Grants: Advised 26 Engineering Management, 1 Computer Science, and 9 Systems Engineering PhD students Total grants: $25M+ from federal, industrial, and academic sources Labs/Teams: Smart Engineering Systems Lab (SESL) DoD Systems Engineering Research Center-URAC Complex Adaptive Systems conference organizer

Ali Goharمشاهده پروفایل

پژوهشگر

• Communication Networks
• 5G and 6G
• Mathematical Modeling for Communication Networks
• +۳ مورد دیگر

Ali Gohar is a Researcher at the Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, University of Stavanger. He holds a PhD in Computer Science from the same university (2019-2024) and has supervised multiple master's theses. Education: PhD - Computer Science, University of Stavanger (2019-2024) MS - Computer Science, Kookmin University (2017-2019) BS - Telecommunication, COMSATS University Islamabad (2011-2014) Certifications: AI in Education, Constructive Alignment and Digital Course Design, Intercultural Competence, NyTi Teaching Certificate, CCNA Research Interests: Ali Gohar's work centers on Deep Reinforcement Learning, 5G/6G communication networks, mathematical modeling, and resource allocation optimization. His research develops autonomous algorithms for network systems in smart cities and space-air-ground integrated networks. Publications: He has contributed to 15 recent publications spanning peer-reviewed journals and conference proceedings, focusing on network slice brokers, virtual network embedding, scalable video coding, and remote driver selection. These works intersect with AI, telecommunications, and urban infrastructure. Supervision: He has mentored students on projects including security-aware resource allocation, federated learning, and vertical industry optimization in smart cities.

• Signal Processing
• Machine Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Arash Mohammadi is an Assistant Professor in the Department of Electrical and Computer Engineering at Concordia University, Montreal, Canada. He holds a PhD from the University of Toronto (2015) and was formerly affiliated with Amirkabir University of Technology, Iran. His research bridges signal processing, artificial intelligence, and biomedical applications. Research Interests: Signal and image processing for healthcare (e.g., lung cancer detection, ECG analysis) Machine learning for smart grids and cyber-physical systems AI in mobile edge computing and 6G networks Transformer and diffusion models for medical and motion data Federated and efficient deep learning for edge devices His recent publications (2021–2025) in top venues like IEEE TSP, ICASSP, and AAAI demonstrate a strong focus on applying cutting-edge AI—especially vision transformers, Mamba architectures, and diffusion models—to critical domains such as medical diagnostics, gesture recognition, and network security. Trends include multimodal fusion, uncertainty quantification, and efficient model design. Scientific Contributions: Developed novel frameworks like NYCTALE and MIXCAPS for lung nodule malignancy prediction Introduced CacheMamba and TEDGE-Caching for edge network optimization Advanced EMG-based gesture recognition using hybrid and transformer models Contributed to cybersecurity in smart grids via attack detection models He actively advises students and collaborates with researchers such as Konstantinos N. Plataniotis and Jamshid Abouei. He has contributed to special issues on neurorehabilitation and AI for COVID-19 diagnosis. His work often involves interdisciplinary teams and real-world applications in healthcare and smart infrastructure.

Mohamed Shehataمشاهده پروفایل

استاد

• Computer Vision
• Image and Video Processing
• Biomedical Applications
• +۴ مورد دیگر

Mohamed Shehata is a Professor in the Department of Computer Science, Mathematics, Physics, and Statistics at the University of British Columbia's Irving K. Barber Faculty of Science. He holds an adjunct professorship at Memorial University of Newfoundland's Computer Engineering Department. His research focuses on computer vision, biomedical applications, and intelligent camera systems. He earned his B.Sc. (Zagazig University), M.Sc. (Zagazig University), Ph.D. (University of Calgary), and P.Eng. licensure. Previously, he worked at Intelliview Technologies Inc. as Vice President of Engineering and Research. He has held roles as an assistant and associate professor at Memorial University before joining UBC in 2019. He serves as Editor-in-Chief of the IEEE Canadian Journal of Electrical and Computer Engineering and has contributed to over 70 peer-reviewed publications. His work spans video surveillance systems, domain generalization, and medical imaging applications. Education: B.Sc. (Honors), Zagazig University M.Sc., Computer Engineering, Zagazig University Ph.D., University of Calgary Research Interests: He explores cutting-edge topics like federated learning for domain adaptation, biomedical image analysis, and lightweight neural networks for embedded systems. His recent work emphasizes cross-domain generalization and few-shot learning for medical diagnostics and object tracking. Professional Contributions: Dr. Shehata has supervised graduate students and led projects in computer vision applications. His publications bridge theoretical advancements with practical systems like drone-based surveillance and IoT healthcare devices. He actively contributes to IEEE committees and academic journal editing.

Yuxuan Sunمشاهده پروفایل

پژوهشگر

• Mobile Edge Computing
• Edge Learning
• Federated Learning
• +۳ مورد دیگر

Yuxuan Sun is a Researcher at Tsinghua University's Department of Electronic Engineering and a visiting researcher at Imperial College London's Intelligent Systems and Networks (ISN) group. She earned a B.S. in Telecommunications Engineering from Tianjin University (2015) and a Ph.D. in Information and Communication Engineering from Tsinghua University (2020). Her research focuses on mobile edge computing, edge learning, and federated learning, with applications in vehicular networks and wireless systems. She has received the Best Student Award from Tsinghua-Intel (2018) and holds roles as an Assistant Editor-in-Chief for IEEE Transactions on Green Communications and Networking and a reviewer for top journals like IEEE TWC and TVT. Her work emphasizes optimizing computation offloading, scheduling policies, and energy efficiency in edge environments. Notable contributions include studies on distributed task replication for vehicular edge computing and mobility-aware federated learning frameworks. She has been recognized with travel grants (e.g., IEEE Comsoc at GLOBECOM 2019) and has collaborated on projects like the 'MEET' framework for green 6G networks. Education: B.S., Tianjin University, Telecommunications Engineering (2015) Ph.D., Tsinghua University, Information and Communication Engineering (2020) Awards: Best Student Award (2018) Grants: IEEE Comsoc Student Travel Grant (GLOBECOM 2019) Her research spans edge computing frameworks, federated learning in constrained environments, and semantic communication for 6G systems. She leads collaborative efforts in labs like Tsinghua's Information Processing and Communications Lab and contributes to industry-academia partnerships.

Masoud Daneshtalabمشاهده پروفایل

استاد

• Deep Learning Acceleration
• Hardware/Software Co-Design
• Interconnection Networks
• +۷ مورد دیگر

Masoud Daneshtalab is a Professor at Mälardalen University, leading the Heterogeneous System research group (HERO). He previously held roles as a European Marie Curie Fellow at KTH Royal Institute of Technology (2014) and as a university lecturer and group leader at the University of Turku, Finland (2012-2014). His research focuses on interconnection networks, hardware/software co-design, deep learning acceleration, and evolutionary optimization. He specializes in fault-tolerant DNN accelerators, time-sensitive networking (TSN), and embedded systems. His work bridges theoretical advancements with practical implementations, emphasizing reliability and efficiency in edge computing and AI applications. Research interests include: Network-on-Chip (NoC) architectures and congestion prediction Fault resilience in deep neural networks (DNNs) Optimization of federated learning and homomorphic encryption for edge AI Integration of TSN with 5G and automotive systems Hardware acceleration techniques for computational efficiency Recent publications emphasize advancements in robust AI architectures, fault tolerance mechanisms, and TSN-based communication protocols. His work often addresses practical challenges in deploying machine learning models on resource-constrained devices. He actively contributes to interdisciplinary projects in autonomous systems, healthcare monitoring via FMCW radar, and neural architecture search for embedded applications. Labs/Teams: Leads the HERO group at Mälardalen University, focusing on heterogeneous computing systems and real-time embedded systems.

Ganesh Neelakanta Iyerمشاهده پروفایل

مدرس

• Software Engineering
• Agile Software Development
• Cloud Computing
• +۱۳ مورد دیگر

Ganesh Neelakanta Iyer is a Lecturer in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a PhD (2012) and MSc (2008) from NUS and a B.Tech. in Computer Science and Engineering from Mahatma Gandhi University, India (2004), where he ranked first. He brings over a decade of industry experience from companies like Salesforce, NXP, and Progress Software, and prior academic roles at Amrita Vishwa Vidyapeetham and IIIT-Hyderabad. His research interests span Software Engineering , Cloud/Edge Computing , Machine Learning , AI for Cultural Heritage , and Software Engineering Education . He founded the STeAdS (Software Engineering and Technological Advancements for Society) research group in 2021, focusing on technology applications in healthcare, education, and traditional arts like Kathakali and Wayang Kulit. The recent publications highlight a strong trend in AI-driven educational technologies , including LLMs for curriculum customization, web-based DevOps learning platforms, and agile project management tools. Another major theme is AI in cultural preservation , with works on Kathakali gesture and facial expression recognition. A third stream involves distributed systems and networking , particularly in edge computing and federated learning. Faculty Teaching Excellence Award 2024, NUS AWS Educate Cloud Faculty Ambassador (2019, 2020) Certified Kubernetes Administrator (CKA) University Topper (First Rank, 2004) NUS Postgraduate Research Scholarship (2009–2012) Dr. Iyer mentors students through UROP, FYP, Master’s, and PhD projects, and has led numerous grants and initiatives in cloud computing education and AI for societal benefit. He has delivered workshops and keynote speeches across the USA, Europe, Australia, and Asia. He actively leads the STeAdS virtual research group, fostering global collaborations. As a maestro in Kathakali, he integrates traditional art with technology, composing and staging performances while applying AI to preserve and analyze this heritage.

• Network Security
• Cloud Security
• Software Defined Networking Security
• +۶ مورد دیگر

Udaya Kiran Tupakula is an Associate Professor in the Department of Computing within the School of Computer, Data and Mathematical Sciences at Western Sydney University. With over two decades of research experience, his work focuses on cybersecurity with particular emphasis on network security, cloud infrastructure protection, and emerging technologies. He has established himself as a leading researcher through extensive collaborations, particularly with Professor Vijay Varadharajan, and has contributed significantly to securing virtualized environments and next-generation networks. Dr. Tupakula's research spans multiple critical areas in cybersecurity. His early work focused on fundamental security challenges like DDoS mitigation and TCP SYN flood attacks, evolving into more complex domains including Software Defined Networking security, cloud security architectures, and IoT protection. Recent research directions include AI security, privacy-preserving machine learning, and securing 5G infrastructure. His approach combines theoretical frameworks with practical implementations, often developing novel security architectures that address specific vulnerabilities in emerging technologies. Analysis of his publication trends shows a clear progression from traditional network security toward more sophisticated systems. His work has increasingly incorporated machine learning techniques for intrusion detection, expanded into industrial control systems security, and recently embraced AI safety challenges. The breadth of his research demonstrates adaptability to evolving security landscapes while maintaining focus on practical, implementable solutions rather than purely theoretical approaches. Dr. Tupakula has mentored numerous researchers who have become active contributors in the cybersecurity field. His collaborative approach is evident in the extensive co-authorship network spanning multiple institutions and countries. His research has been supported by various grants focusing on critical infrastructure protection, cloud security, and next-generation network security. His laboratory work focuses on practical security testing environments for SDN, cloud infrastructure, and IoT systems. Current research directions include securing AI systems from malicious use, enhancing privacy in federated learning environments, and developing more robust security frameworks for industrial control systems. The applied nature of his research ensures close alignment with real-world security challenges faced by industry and government organizations.