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Zoran GajicView profile

Professor

• Control Systems
• Energy Systems
• Wireless Communications
• +4 more

Zoran Gajic is a Professor of Electrical and Computer Engineering at Rutgers University, where he has taught since 1984. He holds academic leadership roles including Graduate Program Director for the Electrical and Computer Engineering Department and President of the Rutgers AAUP-AFT Faculty Union. His expertise spans controls systems, energy systems (including solar, wind, and smart grids), wireless communications, and networking. Education: B.S. and M.S. in Electrical Engineering from University of Belgrade, followed by M.S. in Applied Mathematics and Ph.D. in Systems Science Engineering from Michigan State University (1984). Research focuses on control theory applications for energy systems and communication networks. He has authored/coauthored nearly 100 journal papers and eight books, including best-selling titles like Linear Dynamic Systems and Signals (translated into Chinese) and Lyapunov Matrix Equation in Systems Stability and Control (republished by Dover). His work includes innovations in multirate control systems, singular perturbation methods, and reinforcement learning applications. Professional recognitions include editorial roles across nine journals, five guest-edited special issues, and plenary lectures at international conferences. Ten of his 17 Ph.D. advisees hold faculty positions globally. Beyond academia, he is a chess master with Life Master ranking from the U.S. Chess Federation and World Chess Federation certification. Key contributions include foundational work on optimal control for renewable energy systems, sliding mode control algorithms, and system decomposition techniques. His research has been supported by NSF and industry partners like AT&T Bell Labs. He leads the Rutgers Center for Systems and Controls (SYCON) as Associate Director and actively contributes to standards through IEEE and IEC initiatives, particularly in power system protection and control system reliability.

• Wireless Sensor Networks
• Cyber-Physical Systems
• Network Protocols
• +3 more

John Harrison Kurunathan is an Integrated PhD Researcher affiliated with the CISTER Research Centre at the University of Porto, Portugal. He holds a PhD in Electrical and Computer Engineering (2021), a Master's in Very Large-Scale Integration (2014), and a Bachelor's in Electronics and Communication (2012). Education: PhD (2021) - University of Porto, Portugal MSc (2014) - SSN College of Engineering, Anna University BSc (2012) - SRM University His research focuses on Wireless Sensor Networks (WSNs) , Cyber-Physical Systems (CPS) , and Automotive Networks , with an emphasis on Quality-of-Service (QoS) optimization, secure communication, and vehicular platooning. Notable projects include SafeCOP for safety-related CO-CPS and work on IEEE 802.15.4e DSME networks. Recent publications (2023-2025) span areas like Visible Light Communication , Vehicular Security , and Machine Learning in UAV Operations , reflecting his interdisciplinary work bridging embedded systems and transportation technologies. Scientific Awards: Best oral communication Award (in ex aequo) at DCE 2019 Reviewing Roles: Conference: ICCPS, EWSN, MSN, RTN Journal: IEEE ACCESS, IEEE Transactions on Vehicular Technology, ACM Sigbed Harrison is actively involved in workshops and conferences, including chairing roles at WIN-WIN-4S 2024 and technical demonstrations at WoWMoM 2023. His work appears in venues like IEEE Transactions on ITS, IEEE COMST, and PDP 2025.

Jin LuView profile

Assistant Professor

• Machine Learning
• Optimization
• Bio-informatics
• +6 more

Jin Lu is an Assistant Professor at the University of Georgia's School of Computing, part of the Franklin College of Arts & Sciences. He earned his Ph.D. (2019) and M.S. (2019) in Computer Science and Engineering from the University of Connecticut. Prior to his current role, he served as an Assistant Professor at the University of Michigan–Dearborn (2019–2023). Educational Background: Ph.D. in Computer Science and Engineering, University of Connecticut, 2019 M.S. in Computer Science and Engineering, University of Connecticut, 2019 Research Interests: Dr. Lu focuses on machine learning, optimization, bio-informatics, and smart mobility. His work spans federated learning, healthcare applications (e.g., depression and BMI monitoring), IoT systems, and computer vision. Recent projects explore AGI's potential in medical and educational contexts, leveraging models like CycleGAN and reinforcement learning. Grants & Funding: Develop digital brains to advance portable diagnosis of neurological conditions (Google, 2025) Lab/Teams: While specific lab affiliations are not explicitly stated, his research involves collaborations in interdisciplinary areas such as health informatics and smart mobility.

Maxime Ferreira Da CostaView profile

Associate Professor

• Signal Processing
• Data Science
• Inverse Problems
• +6 more

Maxime Ferreira Da Costa is an Associate Professor at CentraleSupélec, Université Paris-Saclay, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on theoretical and algorithmic foundations of data science, particularly in inverse problems, structured signal processing, and physical layer security. Key research areas include super-resolution, system calibration, and privacy-enhancing communication schemes. He holds a Ph.D. from Imperial College London (2018), and previously worked at USC and Carnegie Mellon University. Notable achievements include an ANR Young Researcher Grant (2023) for projects on data science and physical layer security. He actively contributes to conferences and workshops, with recent presentations at Institut Henri Poincaré and Université Paris-Saclay. Education: Ph.D. in Electrical Engineering, Imperial College London (2018) M.Sc. Electrical Engineering, Imperial College London (2012) Engineer Diploma, CentraleSupélec (2012) Research interests span continuous inverse problems, off-the-grid methods, wireless security, and sensing systems. Current projects explore goal-oriented resource allocation, fake path injection for privacy, and preconditioned optimization techniques. His work bridges signal processing theory with practical applications in imaging, telecommunications, and sensing.

• Wireless Networks
• Machine Learning
• Federated Learning
• +3 more

Choong Seon Hong is a Professor at Kyung Hee University's Department of Computer Science and Engineering in Yongin, South Korea. He earned his PhD in Instrumentation Engineering from Keio University, Japan, in 1997. His research focuses on next-generation wireless networks, with emphasis on 6G systems, federated learning, edge computing, and network optimization. Recent collaborative work explores semantic communication, UAV deployment, and multimodal learning frameworks. His publications highlight technical innovations in: Wireless network optimization for terrestrial, aerial, and satellite systems Federated learning architectures with knowledge distillation and prototype transfer Energy-efficient resource allocation in IoT and vehicular networks Security frameworks for EV charging stations and Open RAN systems Current trends show strong collaboration with Zhu Han, Walid Saad, and younger researchers like Apurba Adhikary and Yan Kyaw Tun. The work spans technical solutions for 6G non-terrestrial networks, holographic MIMO systems, and semantic communication frameworks.

Fady AlajajiView profile

Professor

• Information Theory
• Communications
• Coding Theory
• +5 more

Fady Alajaji is a Professor of Mathematics and Engineering at Queen's University, with a cross-appointment in the Department of Electrical and Computer Engineering. He holds a B.E. from the American University of Beirut, and M.Sc. and Ph.D. from the University of Maryland, College Park. His research focuses on information theory, coding for communication networks, probability models (e.g., Polya urns, contagion processes), and applications of information theory to machine learning (e.g., generative adversarial networks, data privacy). He has served as Associate Editor for the IEEE Transactions on Information Theory and has received awards for research and teaching. His work spans theoretical foundations (e.g., Shannon limits) and practical coding techniques for wireless systems. Education: B.E. (1988), M.Sc. (1990), Ph.D. (1994) in Electrical Engineering from the University of Maryland. Roles: Professor of Mathematics and Engineering, Cross-appointment in Electrical and Computer Engineering. Research Interests: Information theory, coding for communication networks, stochastic processes (network epidemics, Polya urn models), machine learning applications (information bottleneck, GANs), and data privacy. Recent work includes optimal signaling schemes for sensor networks, privacy-aware estimation, and curing models for contagion networks. Publications: Over 100 journal/conference papers, including foundational work on joint source-channel coding, hybrid digital-analog coding, and theoretical bounds for communication systems. Recent trends focus on information-theoretic machine learning and network science. Awards: Premier's Research Excellence Award (2001), Golden Apple Teaching Award (2015). Grants/Advising: Supervised postdoctoral fellow Jian-Jia Weng. Active in conference organization and editorial roles. Labs/Teams: Member of the Mathematics and Engineering Communications and Information Theory Group at Queen's University.

Pietro ManzoniView profile

Professor

• Internet of Things (IoT)
• Mobile Wireless Networks
• LPWAN Technologies
• +7 more

Pietro Manzoni is a Professor of Computer Engineering at the Polytechnic University of Valencia (UPV), Spain. He holds a Master's from the University of Milan (1989) and a Ph.D. from Politecnico di Milano (1995). His research focuses on IoT, edge computing, and wireless networks, with emphasis on TinyML, LPWAN, and edge-cloud systems. He coordinates the Computer Networks Research Group (GRC) and is active in IEEE committees. Education includes a Master's in Computer Science (Università degli Studi di Milano, 1989) and a Ph.D. in Computer Science (Politecnico di Milano, 1995). He interned at Bellcore Labs (USA, 1992–1993) and ICSI (USA, 1994). Research interests span IoT applications, resource-constrained devices, and distributed systems. His work prioritizes empirical validation through prototypes. Teaching includes courses on Networks and Security, Intelligent IoT Systems, and IoT fundamentals in Spanish programs. Publications emphasize IoT protocols, UAV swarms, and TinyML. No scientific awards listed, but over 130 theses advised. Coordinates GRC projects and contributes to editorial boards and conferences.

Montserrat RosView profile

Associate Professor

• Embedded Systems
• Sensor Networks Data Fusion
• Cyber-Physical Systems
• +6 more

Montserrat Ros is an Associate Professor and Associate Dean (Education) at the School of Electrical, Computer and Telecommunications Engineering within the Faculty of Engineering and Information Sciences at the University of Wollongong, Australia. She has been with the university since 2006, initially joining as a Lecturer in Computer Engineering and progressing to her current senior academic and leadership roles. Her educational background includes: B.E.(Hons1)/B.Sc. double degree majoring in Computer Systems Engineering and Mathematics from the University of Queensland (2000) Ph.D. degree in Computer Engineering from the University of Queensland (2007) Professor Ros's research focuses on the intersection of embedded computing systems and practical engineering applications. Her work spans several key areas including embedded systems design, sensor network data fusion, cyber-physical systems development, and innovative approaches to engineering education. She has particular expertise in sensor-based localization techniques, computer architecture optimization, and code compression methodologies for resource-constrained environments. More recently, her research has expanded into machine learning applications for constrained systems and Internet of Things implementations. Analysis of her recent publication record reveals a strong emphasis on Internet of Things networks, UAV-based systems, and applications of artificial intelligence in both engineering education and manufacturing processes. Her work demonstrates a consistent pattern of bridging theoretical computer engineering concepts with practical real-world applications across diverse domains including healthcare, environmental monitoring, and industrial automation. Her significant contributions to academia have been recognized through numerous prestigious awards: 2019: AAUT Citation for Outstanding Contribution to Student Learning 2018: IEEE TALE 2018 Meritorious Service Award 2018: Featured in UOW Leadership in Education Booklet 2017: UOW Vice Chancellor's Award for Outstanding Contribution to Teaching and Learning 2016: UOW Women of Impact for inspiring young women in STEM 2015: UOW Vice Chancellor's Interdisciplinary Research Excellence Award 2012 & 2007: UOW Vice Chancellor's Awards for Teaching Excellence 2011: UOW Vice Chancellor's Award for Community Engagement Senior Fellow of WATTLE (Wollongong Academy for Tertiary Teaching & Learning Excellence) Professor Ros has secured substantial research funding across multiple projects spanning from 2006 to the present. Her grant portfolio demonstrates a consistent focus on engineering education innovation, sensor network development, and practical applications of embedded systems. Notable projects include "The AI Tutor: Enabling 24x7 student support across engineering" (2024), "AI/IoT-powered Airborne System for Monitoring Water Level and Tidal Floods" (2023), and "Smart Eye: Airborne and AI-Driven Assessment Solution of Sugarcane" (2022). She actively supervises HDR students and has completed multiple successful candidatures. Her leadership extends beyond research and teaching, as evidenced by her role as Associate Dean (Education) for the Faculty of Engineering and Information Sciences. She is also actively involved in community engagement through volunteering with the State Emergency Service (Wollongong SES) and Athletics Wollongong Club.

• Robotics
• Human-Robot Interaction
• Autonomous Agent Systems
• +3 more

Mauro Dragone is an Associate Professor at Heriot-Watt University's School of Engineering & Physical Sciences, affiliated with the Institute of Sensors, Signals & Systems. He holds a BSc in Computer Science from Bologna University (1999) and a PhD from University College Dublin (2009). Before academia, he worked as a software developer and project manager. His research focuses on robotics, human-robot interaction, IoT, and Ambient Assisted Living (AAL), with leadership roles in EU projects like METRICS/HEART-MET and the OpenAAL initiative. He co-directs the MSc Robotics program and established the Robotic Assisted Living Tested lab within the UK National Robotarium. Key research interests include assistive robotics for frailty, healthcare robotics, and self-adaptive robotic systems. Notable contributions include the FP7 RUBICON project (2011-2014) and the Cognitive Assistive Robotic Environment (CARE) group. He has received awards such as the 2024 Principal's Research Impact Award and 2022 Research Team of the Year. Recent articles emphasize healthcare applications, modular robotics, and trustworthy AI. His work aligns with UN SDGs addressing healthy aging and sustainable cities.

• Smart Systems
• Optimum Resource Allocation
• Massive MIMO Systems
• +13 more

Dhammika Jayalath is an Associate Professor at Queensland University of Technology (QUT) in the School of Electrical Engineering & Robotics within the Faculty of Engineering. He has been with QUT since 2007, initially as a Senior Lecturer and later promoted to Associate Professor. Prior to joining QUT, he worked as a Senior Researcher at National ICT Australia Ltd and held a Fellowship at the Australian National University. His educational background includes a PhD in Wireless Communications from Monash University and a Graduate Certificate in Higher Education from QUT. He is a Senior Member of IEEE and active in multiple IEEE societies including Communications, Signal Processing, and Vehicular Technology. Research Interests: Dhammika's research focuses on Smart Systems with particular expertise in wireless communications and networking. His work spans Physical Layer Security, Massive MIMO Systems, Internet of Things, Optimum Resource Allocation, Cooperative communications, Cognitive radio, and Vehicular communications. He has made significant contributions to 5G New Radio, Chaotic Communications, Orthogonal Frequency Division Multiplexing (OFDM), and Space-Time Signal Processing. Publication Trends: His recent publications demonstrate a strong focus on 5G/6G networking technologies, physical layer security for IoT devices, and optimization of wireless communication systems. His work bridges theoretical communications theory with practical implementation challenges, particularly in vehicular networks, secure communications, and resource allocation for heterogeneous networks. The articles show increasing interdisciplinary work, combining machine learning techniques with traditional communications engineering approaches. Scientific Awards: 2007: Early Career Academic Recruitment and Development (ECARD) award from QUT 2009: Elevated to Senior Member Grade of IEEE 2000: IEEE travel grant Multiple scholarships during graduate studies at Monash University Supervision and Grants: Professor Jayalath has supervised numerous PhD students to completion with research topics including chaotic communication systems, resource allocation in heterogeneous networks, and vehicular communication systems. He has secured multiple research grants totaling over AU $300,000, including projects from ARC, QUT internal grants, and industry partnerships with Queensland Fire and Emergency Services. His current research includes physical layer security frameworks for IoT devices and optimization of massive MIMO systems for dense mobile networks. Laboratory and Team Work: He has been instrumental in establishing wireless communications research capabilities at QUT, including securing equipment grants for Software Defined Radio platforms. His work often involves interdisciplinary collaboration with researchers in signal processing, cybersecurity, and transportation systems.

Hyuck M. KwonView profile

Professor

• Wireless Mobile Communications
• Communication Systems
• Information Theory
• +6 more

Hyuck M. Kwon is a Full Professor of Electrical and Computer Engineering at Wichita State University (WSU), part of the College of Engineering. His research focuses on wireless communications, including satellite systems, smart antennas, MIMO, and CDMA. He has held academic appointments since 1985, including visiting roles at institutions like KAIST and George Mason University. Kwon has supervised numerous PhD and MS students and has been recognized with prestigious awards, including the Dwane and Velma Wallace Outstanding Educator Award. His work spans over 100 publications and grants from agencies like the Air Force and NASA. He has developed courses such as '5G Wireless Communications' and 'Information Theory'. Education: Ph.D. in Computer, Information and Control Engineering, University of Michigan (1984) MSEE, Seoul National University (1980) BSEE, Seoul National University (1978) Research Interests: Wireless communications and networks Smart antennas and MIMO systems 5G/6G technologies Signal processing for communications Anti-jamming and secure communications Grants and Awards: Over $2M in research funding from the Air Force, NASA, and industry partners Awarded multiple Air Force Summer Faculty Fellowships (2014–2016) Nominated for NAI Fellow (2017) Lab/Teams: Active in developing advanced communication systems, including satellite digital beamforming and nano-antenna research with metamaterials.

Lokesh DasView profile

Assistant Professor

• Autonomous Vehicles
• Traffic Control Systems
• Deep Learning
• +4 more

Dr. Lokesh Das is an Assistant Professor in the School of Computing at Wichita State University’s College of Engineering. His research focuses on developing advanced algorithms for autonomous vehicle traffic control systems using deep learning and V2X communication technologies. Key interests include reinforcement learning applications in real-world traffic scenarios, IoT security frameworks, and smart infrastructure integration. Research Interests: Autonomous Vehicle Coordination Reinforcement Learning for Traffic Systems IoT Security and Edge Computing Dynamic Wireless Charging Solutions Pedestrian Safety Prediction His recent work emphasizes multi-agent systems for traffic optimization and safety-aware adaptive control mechanisms. No specific awards are listed, but his publications reflect impactful contributions to intelligent transportation systems and machine learning applications. Advising and grants information is not explicitly provided in the text. Dr. Das’s research lab likely focuses on real-world implementation of AI-driven traffic solutions.

Xiang SunView profile

Assistant Professor

• Mobile Edge Computing
• Internet of Things (IoT)
• Drone-assisted Mobile Access Networks
• +5 more

Dr. Xiang Sun is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of New Mexico (UNM). He holds a Ph.D. in Electrical Engineering from New Jersey Institute of Technology (2018), and M.E. and B.E. degrees from Hebei University of Engineering (2011 and 2008). His research focuses on Mobile Edge Computing, IoT, Drone-assisted Networks, Green Computing, and Data Center Optimization. Dr. Sun has been recognized with awards such as the 2018 InterDigital Innovation Award and NJIT Hashimoto Prize. Education: Ph.D., Electrical Engineering, NJIT (2018) M.E., Computer Applications Technology, Hebei University (2011) B.E., Electronic Information, Hebei University (2008) Research Interests: His work emphasizes IoT resource caching, semantic discovery, green communications, and drone-based mobile access networks. He also explores machine learning applications in edge computing and cybersecurity. Publications & Contributions: Dr. Sun has authored over 40 journal/conference papers, including impactful work on cloudlet networks and IoT semantic mashup. His research has been published in IEEE Transactions, Communications Letters, and top-tier conferences like ICC and GLOBECOM. Awards: 2018 InterDigital Innovation Award 2018 NJIT Hashimoto Prize 2017 IEEE Communications Letters Exemplary Reviewer 2016 IEEE ICC Best Paper Award Professional Activities: He serves as a TPC member for IEEE CCNC and MobiEdge, and reviewer for top journals like IEEE Transactions on Cloud Computing and IEEE Internet of Things Journal. He co-chairs the Social Computing and Semantic Data Mining symposium at IEEE ICNC 2019. Labs & Teams: Active in UNM's Electrical & Computer Engineering labs, focusing on mobile edge computing and IoT innovation. Collaborates with industry partners like InterDigital Communications.

Diala NaboulsiView profile

Professor

• Mobile networks
• Wireless networks
• Network virtualization
• +7 more

Diala Naboulsi is a Professor at the École de technologie supérieure (ÉTS) in the Department of Software Engineering and IT. Her research focuses on mobile networks, wireless systems, and cybersecurity, with a strong emphasis on machine learning applications in network optimization. She holds an M.Eng. from the Lebanese University and M.Sc. and Ph.D. degrees from INSA Lyon. Research Units: Summit Tech Research Chair, LASI Lab, Imagin Lab Expertise: Network virtualization, resource allocation, mobility management, UAV-based computing Her work spans resilience in wireless backhaul networks, energy-efficient frameworks in RAN slicing, and federated learning for privacy-aware traffic forecasting. She has advised numerous doctoral students, including Ahmed Abdelmoaty, Hnin Pann Phyu, and Philippe Lavoie. Key contributions include deep reinforcement learning approaches for network topology optimization and UAV-assisted MEC systems for Industry 5.0. Recent publications highlight advancements in 6G networks, network slicing, and edge computing. Her research aligns with strategic initiatives in sustainable and secure communication systems.

F. Richard YuView profile

Professor

• Machine learning and artificial intelligence
• Connected and autonomous vehicles
• Blockchain and distributed ledger technology
• +2 more

F. Richard Yu is a Professor at the School of Information Technology , Carleton University , Canada, since 2016. He served as Assistant/Associate Professor at the same institution from 2006 to 2015. His academic work spans machine learning, blockchain, autonomous vehicles, and network security. Ph.D., Electrical Engineering, University of British Columbia (2003) M.A.Sc., Computer Engineering, Beijing University of Posts & Telecomm. (1998) B.A.Sc., Electrical Engineering, Dalian University of Technology (1995) His research focuses on machine learning , blockchain , autonomous vehicles , network security , and mobile wireless networks . Trends in his publications include deep reinforcement learning for computation offloading , blockchain applications in edge computing , and network optimization for IoT and 5G systems . Highly Cited Researcher (2019-2023), Clarivate Electronics and Electrical Engineering Leader in Canada (2023), Research.com Fellow of Canadian Academy of Engineering (CAE) (2021), Engineering Institute of Canada (EIC) (2019), IEEE (2017), and IET (2016) Best Paper Awards: IEEE ICC (2022), IEEE GLOBECOM (2020), IEEE VTC (2017), IEEE TAOS (2012), IEEE TrustCom (2009) Research Achievement Award, Carleton University (2021, 2012) Early Researcher Award (2011), Ontario; Leadership Opportunity Fund Award (2009), Canada Foundation for Innovation His work has resulted in 800+ papers , 30 patents , and 48,000+ citations (H-index 107), with grants including the 2009 Canada Foundation for Innovation award.