Find Academic Supervisors

• Massive MIMO
• Cognitive Radio Networks
• Smart Grids
• +6 more

Professor Sangarapillai Lambotharan is a Professor of Digital Communications and Head of the Signal Processing and Networks Research Group at Loughborough University's Wolfson School of Mechanical, Electrical and Manufacturing Engineering. He holds qualifications including BEng, PhD, DIC, PGCAP, and is a Senior Member of IEEE, Fellow of IET, and Fellow of the Higher Education Academy. His research focuses on advanced wireless communication technologies such as Massive MIMO, Cognitive Radio Networks, and Smart Grids, alongside Machine Learning, Data Mining, and Cyber Security. He has authored over 200 publications in top-tier journals and conferences. His work integrates machine learning with signal processing to address challenges in adversarial attacks, federated learning, and network optimization. Recent research emphasizes intelligent reflecting surfaces (RIS), OTFS satellite communications, and federated learning defenses against model poisoning. Prof. Lambotharan's contributions span theory development, algorithm design, and practical implementations in 5G/6G systems, UAV networks, and edge computing environments. He leads collaborative projects on secure communications, distributed intelligence, and blockchain-based network security mechanisms. Prof. Lambotharan's academic leadership includes editorial roles at IET Signal Processing and teaching Digital Signal Processing and Information Theory. His lab explores cutting-edge topics like RIS-aided systems, anomaly detection in IoT, and context-aware caching strategies for heterogeneous networks. Key achievements include pioneering work on Kalman filtering for channel estimation and game-theoretic solutions for resource allocation in cognitive radio networks.

Miroslav MinovicView profile

Professor

• HCI
• Multimedia
• Computer Networks
• +3 more

Miroslav Minović is a Full Professor and Head of the Laboratory for Multimedia Communications at the University of Belgrade's School of Business Administration. He holds a PhD in Information Technology and has extensive experience in academia and research. His roles include Director of the Laboratory for Multimedia Communications and guest editor for the International Journal of Engineering Education . Education: PhD in Information Technology (2010), University of Belgrade M.Sc. in Information Technology (2002), University of Belgrade Visiting Researcher at University of the Balearic Islands (2015) and EBS Business School (2013) Research Interests: Human-Computer Interaction (HCI), Multimedia Systems, Biometric Technologies, Blockchain Applications, Computer Networks, and Gamification in Education. Notable Projects: TRAILER EU Project (2012–2013): Focused on informal learning recognition AI4WorkplaceSafety (2020–2022): AI-driven workplace safety solutions Blockchain Technology Research: Explored applications beyond cryptocurrencies Publications: Over 50 peer-reviewed articles in journals like IEEE Multimedia , Computers in Human Behavior , and Sensors . Key themes include gamification ecosystems, biometric systems, and e-learning analytics. Awards & Memberships: Member of IEEE and ACM National Contact Point for Serbia, European Association for Biometrics Grants & Labs: Directed multiple EU-funded and Serbian government projects Lab focuses on multimedia communications, blockchain, and biometric technologies

• Networking
• Wireless Communication
• Internet of Things (IoT)
• +5 more

Frank Yong Li is a Professor in the Department of Information and Communication Technology at the University of Agder, Norway. He holds a Ph.D. in Telematics from the Norwegian University of Science and Technology (NTNU). His research focuses on networking aspects of mobile and wireless systems, including 5G/6G, IoT, cognitive radio, and energy-efficient protocols. He has supervised numerous M.Sc. and Ph.D. theses, with notable awards such as the Best Master Thesis in ICT 2022 for Jon Anders Skadberg Øgaard's work. Teaching responsibilities include advanced courses on mobile communications, IoT, and 5G fundamentals. He has led projects prototyping open-source 5G systems and contributed to maritime IoT and vehicular networks. His work emphasizes protocol design, performance evaluation, and real-world implementation. Research interests span MAC mechanisms, routing protocols, cooperative communications, and network slicing. Key contributions include frameworks for energy-efficient IoT, grant-free access in 5G NR, and reliability analysis in ultra-reliable low-latency communications (URLLC). He advises on over 50 student projects, focusing on IoT, 5G, and network security. Notable collaborations include partnerships with Telenor Maritime and CERN, integrating academia-industry research. His labs explore cutting-edge topics in beyond 5G networks and private mobile infrastructure.

Mulugeta Weldezgina AsresView profile

Researcher

• Data Science
• Machine Learning
• Deep Learning
• +4 more

Mulugeta Weldezgina Asres is a Researcher at the Department of Information and Communication Technology , University of Agder, Norway. His roles include developing large-scale anomaly detection systems for CERN detectors and advancing AI-driven industrial data analytics. He holds a Ph.D. in machine learning for non-intrusive load monitoring (NILM) and has prior academic experience at Mekelle University (Ethiopia) and Politecnico di Torino (Italy). Education: B.Sc. (Gold Medal) and M.Sc. (Gold Medal) in Computer Engineering from Mekelle University, followed by doctoral research at the Center for Artificial Intelligence Research (CAIR), Norway. His research focuses on time-series analysis, anomaly detection, IoT, and Industry 4.0 applications. Key research areas include multivariate time-series modeling, deep learning for anomaly prediction in high-energy physics detectors, and AI-powered industrial monitoring. He has contributed to projects at CERN, Telecom Italia, and automotive manufacturing, emphasizing data-driven solutions. Awards include Gold-Medal Awards for academic excellence in both B.Sc. and M.Sc. His work bridges theoretical machine learning with practical applications in energy efficiency, telecommunications, and particle physics instrumentation. Publications span IEEE journals and conferences, focusing on anomaly detection frameworks, NILM algorithms, and predictive modeling for industrial systems. Collaborations include institutions like CERN, Politecnico di Torino, and Midori srl, an energy efficiency startup.

• IoT Security
• Wireless Communication Protocols
• Vehicular Networks
• +3 more

Dr. Rafik Zitouni is a Research Fellow and Lead Software Engineer at the University of Surrey's School of Computer Science and Electronic Engineering, affiliated with the Institute for Communication Systems. His work focuses on wireless communication protocols, IoT security, vehicular networks, and adaptive networking solutions. He has contributed to advancements in 6TiSCH, LoRaWAN, and MQTT protocols, with an emphasis on security mechanisms, network optimization, and machine learning applications. Research interests include securing industrial IoT networks, vehicular emergency messaging systems, and low-power wide-area networks (LPWAN). He has pioneered techniques such as fuzzy logic-based detection of selfish nodes in 6TiSCH and Fuzzy C-Means clustering for LoRa parameter optimization. His work on FPGA-based sensor fusion for autonomous vehicles addresses real-time data processing challenges. Key contributions span 20+ peer-reviewed publications in journals like Wireless Networks and Computer Communications , and conferences including IWCMC and MENACOMM. He actively collaborates with industry and academia on open radio access networks (O-RAN) and underwater localization systems. Current research emphasizes machine learning-driven network automation and resilient IoT infrastructure.

Arun VenkataramaniView profile

Professor

• Networking
• Distributed Systems
• Mobile Systems
• +3 more

Arun Venkataramani is a Professor in the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst. He joined the faculty in 2005 as an Assistant Professor, advancing to Associate Professor in 2010. His roles include Chair of Master’s Admissions and Master’s Program Director. His research focuses on networking, distributed systems, mobile and sensor systems, with a particular emphasis on privacy-preserving consensus, interdomain routing, and secure communication in challenged networks. Education: PhD in Computer Sciences, University of Texas at Austin MS in Computer Sciences, University of Texas at Austin BTech in Computer Science and Engineering, Indian Institute of Technology, Bombay Research interests span Networking (privacy-preserving protocols, interdomain routing), Distributed Systems (consensus algorithms, replica coordination), and Mobile Systems (mobility-centric architectures, sensor networks). His work often addresses security, scalability, and resilience in next-generation internet architectures. Awards: Co-recipient of the J.C. Browne Fellowship (awarded to outstanding UT Austin CS graduate students) Ranked in the top 40 among 100,000+ candidates in India’s IIT entrance exam Advising & Projects: Leads the Advanced Networked Systems Research Group Contributions to MobilityFirst, GigaPaxos, and msocket systems Involved in NSF-funded projects like NeTS and Light-Speed Networking Labs/Teams: Primary affiliation with the Advanced Networked Systems Research Group, collaborating on global name services, secure communication, and mobility-centric architectures.

Mohamed-Yahia DabbaghView profile

Associate Professor

• Digital Signal Processing
• Image Processing
• Video Processing
• +6 more

Mohamed-Yahia Dabbagh is an Associate Professor, Teaching Stream in the Department of Electrical and Computer Engineering at the University of Waterloo. He holds a PhD from North Carolina State University (1989), an MSE from the University of Michigan (1981), and a BSE from the University of Aleppo (1976). His research focuses on digital signal processing, image/video processing, and VLSI multiprocessor implementation of algorithms for communication systems and computer vision. He has developed architectures for digital filters and explored video motion estimation, segmentation, and pattern recognition. Dr. Dabbagh has authored numerous articles in IEEE and other venues, including work on wireless sensor anomaly detection, smartphone application monitoring, and FPGA-based digital filter implementations. His teaching includes courses such as Signals and Systems (ECE 207), Linear Circuits (ECE 140), and Probability and Random Processes (ECE 316). His research has spanned VLSI environments, Bayesian classification for video processing, and real-time signal processing techniques. Notable contributions include multiprocessor filter implementations and adaptive motion estimation algorithms. Dr. Dabbagh is a Professional Engineer (PEng) registered in Ontario and has contributed to technical reports on imaging systems and geographic information systems during his tenure at Kuwait-based institutions.

Mei YangView profile

Professor

• Computer architectures
• Interconnection networks
• Machine learning
• +5 more

Mei Yang is a Professor and Chair in the Department of Electrical and Computer Engineering at the University of Nevada, Las Vegas (UNLV). Her research focuses on computer architectures, interconnection networks, machine learning, embedded systems, and wireless sensor networks. She leads the Networking and System Integration Laboratory (NSIL), advancing interdisciplinary projects at the intersection of hardware design and intelligent systems. Education Ph.D., Computer Science, University of Texas at Dallas, 2003 Research Interests Dr. Yang's work spans network-on-chip (NoC) architectures, thermal-aware task management, and deep learning applications in microscopy and environmental monitoring. She explores energy-efficient computing, cybersecurity in many-core systems, and educational strategies to engage girls in STEM through hands-on projects like IoT and embedded systems. Articles Trends Her recent publications emphasize hardware security (e.g., thermal covert channels), AI-driven biomedical image analysis, and optimizing multi-chiplet systems. She bridges theoretical contributions with practical implementations, such as FPGA-based NoC emulators and GAN-based protein localization tools. Labs & Teams Hosts the NSIL lab, fostering collaborations in photonic networks-on-chip, embedded systems, and cybersecurity. Active in IEEE and ACM conferences, organizing special sessions on high-performance computing and network coding.

Demosthenis TeneketzisView profile

Professor

• Stochastic Control
• Decentralized Stochastic Systems
• Communication and Queueing Networks
• +7 more

Demosthenis Teneketzis is a Professor of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. He holds degrees from the University of Patras (Greece) and MIT (USA). His research focuses on stochastic control, decentralized systems, communication networks, and game-theoretic applications to cyber-security and electricity markets. He has held visiting positions at ETH Zurich and has industry experience with Systems Control, Inc. and Alphatech, Inc. Education: Diploma in Electrical Engineering, University of Patras, 1974 M.S., E.E., Ph.D. in Electrical Engineering, MIT, 1976-1979 Research Interests: Stochastic Control & Decentralized Systems Game Theory in Networks & Markets Cyber-Security & Resource Allocation Electricity Market Design Optimization & Mechanism Design Recent work trends include: Dynamic games with asymmetric information Information design in strategic systems Optimal mechanisms for electricity markets Decentralized control of cyber networks Game-theoretic security strategies No scientific awards explicitly listed in provided text. Advising/grants: No student list or grant details provided. Active in interdisciplinary projects like the SoilSCaPE wireless sensor network for environmental monitoring.

Monica D. KohlerView profile

Research Professor

• Earthquake Engineering
• Seismology
• Structural Health Monitoring
• +3 more

Monica D. Kohler is a Research Professor of Mechanical and Civil Engineering at the California Institute of Technology (Caltech). She holds a Ph.D. in Civil Engineering from Caltech (1995) and an A.B. in Physics from Harvard University (1988). Her career includes roles as Assistant Research Engineer at UCLA (1998-2011), Visiting Associate at Caltech (2007-11), and Research Assistant Professor at Caltech (2011-17). Research Interests: Dr. Kohler's work bridges earthquake engineering and observational seismology. She develops methods to detect structural damage using seismic arrays, applies tomographic inversion to study urban ground motion, and investigates wave propagation in buildings and geological systems. Her research leverages IoT sensor networks and real-time data for city-scale seismic monitoring. Article Trends: Her recent publications focus on structural health monitoring of high-rises via coda wave interferometry, community seismic networks for urban resilience, and subduction zone dynamics. Key subfields include nonlinear damage identification, tsunami wavefront mapping, and edge computing applications for seismic data. Students & Collaborations: She supervises graduate students at UCLA (Viviana Vela, Gloria Hyun, Esra Zengin) and collaborates with institutions like Caltech, UCLA, and IRIS. Her projects include the Community Seismic Network (CSN) and ShakeAlert earthquake early warning system. Contact: Office at 318 Gates-Thomas Laboratory, Caltech. Email: kohler@caltech.edu .

• Graph Algorithms
• Distributed Algorithms
• Parallel Algorithms
• +3 more

Dr. Peter Davies-Peck is an Assistant Professor in the Department of Computer Science at Durham University. His research focuses on distributed algorithms, graph theory, and parallel computing. He has held roles on programme committees for major conferences like PODC and ICDCS, and has been an invited speaker at workshops associated with DISC. His research interests include Graph Algorithms, Distributed Algorithms, Randomised Algorithms, and Communications Networks. Notable work involves applying the Lovász Local Lemma to distributed computing challenges and developing efficient message-passing protocols in noisy environments. Recent contributions include advancements in parallel derandomization for graph coloring, optimal message-passing in radio networks, and distributed mean estimation techniques. His work bridges theoretical algorithm design with practical distributed system challenges, emphasizing scalability and resilience. Esteem indicators include Programme Committee membership for PODC 2023, ALGOSENSORS 2022, and ICDCS 2021. His publications span top venues like STOC, SODA, and the Journal of the ACM, reflecting impactful contributions to theoretical computer science and distributed systems.

Yanting HuView profile

Senior Lecturer

• Power Systems Control
• Renewable Energy Integration
• Smart Grid Technologies
• +7 more

Yanting Hu is a Senior Lecturer in the Department of Computer and Engineering Sciences at the University of Chester. She holds a BEng in Electrical and Electronic Engineering (Power Systems & Automation), an MSc in Computer Application in Power Systems Control and Operation, and a PhD in Advanced Control of Power Systems. Her professional memberships include Senior Member of IEEE (SMIEEE), Member of the Institution of Engineering and Technology (MIET), Chartered Engineer (CENG), and Fellow of the Higher Education Academy (FHEA). Her teaching spans undergraduate modules such as Electrical Principles, Engineering Mathematics, FPGA Design, Digital Signal Processing, and Power Systems, as well as postgraduate courses including Advanced Electric Machine Drives, Embedded System Design, and Electrical Power and Distributed Generation. She has supervised projects at BEng, MSc, MRes, MPhil, and PhD levels. Research interests focus on power systems operation and control, power electronic converter topology and control strategies, EV and smart grid technologies (e.g., microgrids), and applications of wireless sensor networks in mechanical and electrical systems. She emphasizes optimization algorithms for system reliability, power quality, and stability. Her work also explores distributed generation integration, hybrid energy systems, and wide-area protection schemes against cascading events in power networks. She has developed novel approaches for battery energy storage systems and flicker mitigation in wind turbines.

Alison GriffithsView profile

Associate Professor

• Smart Grids
• Renewable Energy Integration
• Wireless Sensor Networks
• +4 more

Dr. Alison Griffiths is an Associate Professor at Staffordshire University, specializing in Electrical Engineering and Computing. She holds a PhD in Wireless and Mobile Communications (2004), Chartered Engineer status (CEng, 2006), and a Senior Fellowship from the Higher Education Academy (SFHEA, 2019). Her research focuses on Smart Grids, Renewable Energy Integration, and Communication Protocols for HVDC systems. She has supervised 9 PhD students and actively participates in STEM outreach through Micro:Bit and Raspberry Pi workshops. Affiliations: Staffordshire University, Faculty of Digital, Tech, Innovation & Business Teaching: Course Leader for MSc programs in Electrical Engineering, Robotics, and Smart Technologies Research Interests: Smart Grid Protection, Wireless Sensor Networks, and Signal Processing. Her work emphasizes real-time monitoring and optimization, contributing to both academia and industry collaborations. Publications: Over 40 peer-reviewed articles, focusing on HVDC protection, fault detection, and networked control systems. Recent work includes thermal management of EV batteries and Wi-Fi sensing applications. Awards: CEng, SFHEA, and active roles in WISE and IET.

Mohammad HasanView profile

Senior Lecturer

• Computer Networks
• Cyber Security
• Cloud Computing
• +5 more

Dr. Mohammad Hasan is a Senior Lecturer in Digital, Tech, Innovation & Business at Staffordshire University with extensive expertise in cybersecurity, cloud computing, and network engineering. His educational background includes a PhD in Mobile Ad-hoc Networks, MSc in Computer and Network Engineering, and BSc in Computer Science. His primary research focuses on: Computer networks including wireless systems and IoT Cybersecurity frameworks and threat analysis Cloud computing infrastructure and virtualization Machine learning applications in data analytics Recent publications demonstrate strong trends in machine learning applications for pattern recognition (eSports analytics), network security (XSS prevention), and biomedical signal processing (brain source localization). Dr. Hasan maintains professional certifications including CCSK, CompTIA Security+, and Cisco CyberOps Associate, and holds Senior Fellow status with the Higher Education Academy.

Tashnim ChowdhuryView profile

Professor

• Machine Learning
• Deep Learning
• Computer Vision
• +7 more

Dr. Tashnim Chowdhury is a Professor in the Department of Computer Science at Capitol Technology University. He holds a Ph.D. in Information Systems (AI and Machine Learning) from the University of Maryland Baltimore County, an M.S. in Electrical Engineering from the University of Toledo, and a B.S. in Electrical and Electronics Engineering from Chittagong University of Engineering and Technology. His research focuses on Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing, with applications in disaster damage assessment and large language models. He has published extensively on topics like UAV-based semantic segmentation for natural disaster analysis and high-resolution aerial imagery datasets for post-flood scene understanding. Dr. Chowdhury has over 3 years of industry experience as a Machine Learning Engineer and Software Engineer at companies such as Comcast, Intel, and Fluence Automation. His work bridges academic research with practical applications in AI, generative models, and environmental monitoring. He has advised students in academic settings and contributes to dataset development for critical infrastructure analysis.