Find Academic Supervisors

Ye XiaView profile

Associate Professor

• Computer Networking
• Wireless Networks
• Resource Allocation
• +7 more

Ye Xia is an Associate Professor at the Department of Computer and Information Science and Engineering within the Herbert Wertheim College of Engineering at the University of Florida. Her research focuses on computer networking, particularly in wireless networks, resource allocation, energy efficiency, and content distribution algorithms. She holds a Ph.D. from UC Berkeley, an MS from Columbia University, and a BA from Harvard University. Education: Ph.D. in Electrical Engineering and Computer Science, University of California, Berkeley (2003) MS in Electrical and Computer Engineering, Columbia University (1995) BA in Electrical Engineering, Harvard University (1993) Research Interests: Dr. Xia’s work spans performance evaluation of network protocols, wireless link scheduling, energy-saving techniques in sensor networks, and stochastic processes. Her recent publications explore scheduling algorithms, network lifetime optimization, and distributed systems. Publications: Her recent work includes studies on maximum weight scheduling in wireless networks, distributed CSMA algorithms, and node density optimization in wireless sensor networks. These contributions emphasize practical applications of theoretical models to improve network efficiency and reliability. Awards & Grants: No specific awards or grants are listed in the provided text. Advising & Labs: No student advisees or lab affiliations are explicitly mentioned here.

Zhongyuan ZhaoView profile

Academic

• Wireless Communications and Networking
• Machine Learning
• Signal Processing
• +7 more

Zhongyuan Zhao is a Research Assistant Professor at Rice University's Department of Electrical and Computer Engineering, affiliated with the George R. Brown School of Engineering. He holds a Ph.D. in Computer Engineering from the University of Nebraska-Lincoln and completed his postdoctoral studies at Rice under Prof. Santiago Segarra. His research integrates graph-based neuro-symbolic approaches with domain-specific models to address challenges in wireless communications, edge computing, and networked systems. Dr. Zhao has over a decade of industry experience in 4G base-station development at Ericsson and ArrayComm, followed by academic research roles focusing on distributed algorithms and machine learning applications. His education includes a B.Sc. and M.S. in Electronic Engineering from the University of Electronic Science and Technology of China, where he also worked as a teaching assistant for national design contests. He earned a minor in Finance (15 credits) during his Ph.D. and is completing the CFA Level III program in 2024. Zhao has contributed to large-scale wireless testbeds like NEXTT and authored/co-authored 25+ peer-reviewed publications, 2 patents, and delivered academic service roles including conference session chair and journal reviewer. Research interests span autonomous networking, graph-based machine learning, stochastic optimization, and their applications in infrastructureless wireless systems. His work emphasizes distributed solutions for resource allocation, scheduling, and edge intelligence. Awards include the Future Faculty Fellowship (2023) and IEEE travel grants. He has mentored over 40 students through research internships, hackathons, and undergraduate projects, showcasing his commitment to education. Recent projects focus on scalable computation offloading, generalized backpressure routing in tactical networks, and neuro-symbolic AI frameworks for networked systems. His lab work involves open-source contributions to tools like the Actor-Twin Framework and SelR algorithm, promoting reproducibility and collaboration in the field.

Steven B. LeebView profile

Professor

• Power Electronics
• Energy Conversion Systems
• Motor Drives
• +5 more

Steven B. Leeb is a Professor of Electrical Engineering and Computer Science (EECS) and Mechanical Engineering at MIT. He holds the Carl Richard Soderberg Chair in Power Engineering and is a MacVicar Fellow, recognizing his excellence in teaching and research. His work focuses on power electronics, energy conversion systems, motor drives, and nonintrusive power monitoring. Leeb has contributed to advancements in smart grid technologies, building energy management, and fault detection systems. He leads interdisciplinary research integrating control systems with renewable energy applications. Education: B.Sc., M.Sc., M.Eng., and Ph.D. in Electrical Engineering from MIT (1987–1993). His career at MIT spans over three decades, progressing from Assistant Professor (1993) to Full Professor (2005). He has advised numerous projects in collaboration with industries and military applications, including naval propulsion systems and unmanned aerial vehicle (UAV) energy buffering. Research Interests: Power Electronics and Motor Drives Nonintrusive Load Monitoring (NILM) Building Energy Management Smart Grid Technologies Sensors and Sensor Networks Electromagnetic Systems Awards: Over 15 major awards, including the National Science Foundation CAREER Award (1996), IEEE Fellow (2007), and multiple R&D 100 Awards. His work has led to patents in nonintrusive monitoring systems, smart lighting, and energy-efficient technologies. Teaching: Courses include Mechanical Engineering Tools (2.670) , Power Electronics Laboratory (6.131) , and Microcomputer Project Laboratory (6.115) . He emphasizes hands-on learning through labs like his DC motor experiments, which are featured in freshman orientation programs. Labs/Teams: Active in MIT’s Energy Initiative, collaborating with industry partners on projects such as naval propulsion systems and UAV energy systems. His group develops prototypes for commercial applications in smart buildings and renewable energy integration.

Yeying ZhuView profile

Associate Professor

• Statistics
• Causal Inference
• Machine Learning
• +3 more

Yeying Zhu is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. Her research focuses on causal inference, machine learning applications in networks, and statistical methodologies for handling missing data and confounding variables. She holds a Ph.D. in Statistics from The Pennsylvania State University (2013), an M.S. in Statistics & Applied Probability from the National University of Singapore (2008), and a B.S. in Statistics with a minor in International Economics & Trade from East China Normal University (2006). Her work bridges theoretical statistics with practical challenges in causal analysis, including mediation analysis, propensity score modeling, and network treatment effects. Recent research explores UAV-enabled integrated networks using reinforcement learning and anomaly detection in satellite systems through ensemble learning techniques. She has contributed to advancements in high-dimensional causal mediation analysis and semi-parametric estimation techniques. Notable areas of contribution include: Causal inference frameworks for non-ignorable missing data Machine learning-driven solutions for UAV trajectory optimization and satellite anomaly detection Development of robust statistical methods in longitudinal data and instrumental variable models Her articles reflect interdisciplinary collaborations spanning statistics, computer science, and engineering, emphasizing methodological innovation and real-world applicability.

Liang ZhangView profile

Assistant Professor

• Artificial Intelligence
• Mobile Edge Computing
• UAV Communications
• +3 more

Dr. Liang Zhang is an Assistant Professor in the Department of Engineering and Aviation Sciences at the University of Maryland Eastern Shore (UMES). He holds a Ph.D. in Electrical Engineering from New Jersey Institute of Technology (NJIT) and an M.S. in Information and Communication Engineering from the University of Science and Technology of China (USTC). His research focuses on machine learning, mobile edge computing, UAV communications, wireless communications, and IoT, with an emphasis on resource optimization and algorithm design. Education: Ph.D., Electrical and Computer Engineering, NJIT (2014–2020) M.S., Information and Communication Engineering, USTC (2011–2014) B.S., Electronic Science and Technology, Huazhong University of Science and Technology (HUST) Research Highlights: Dr. Zhang has pioneered work on deep reinforcement learning algorithms for UAV-assisted edge computing, caching optimization, and latency reduction in IoT systems. His contributions include the BRIDGES testbed project at George Mason University (GMU), supported by a $2.5M NSF grant, and the development of QoE-optimized frameworks for wireless VR and airborne networks. Awards & Recognition: Outstanding Dissertation Award (NJIT, 2023) Hashimoto Prize (NJIT, 2020) IEEE GLOBECOM Travel Grant (2016) Best Paper Award (IEEE ICNC, 2014) Professional Contributions: He has published 32 peer-reviewed articles and serves as a reviewer for IEEE journals. His work integrates machine learning with networking challenges, addressing practical issues like spectrum sharing in 5G, energy-efficient VM management, and dynamic resource allocation in optical networks.

Vera MiloslavskayaView profile

Lecturer

• Error-Correction Coding
• Information Theory
• Artificial Intelligence
• +2 more

Dr. Vera Miloslavskaya is a Lecturer in Information Technology at the University of New England's School of Science and Technology. She holds a PhD from Peter the Great St. Petersburg Polytechnic University and specializes in error-correction coding and AI applications for telecommunications. Research Focus: Miloslavskaya develops machine learning-enhanced coding schemes for next-generation wireless systems. Her work on neural network-based polar coding and graph neural network applications in MIMO detection aims to improve reliability and efficiency in 6G networks. She maintains collaborations with the University of Sydney where she previously worked as a Postdoctoral Research Associate. Recognition: Awarded the IEEE Transactions on Communications Exemplary Reviewer (2022) and multiple Russian academic awards including the Gold Medal of the Russian Academy of Science (2012).

Roshan AyyalasomayajulaView profile

Assistant Professor

• Wireless Systems
• Machine Learning
• Internet of Things (IoT)
• +5 more

Roshan Ayyalasomayajula is an Assistant Professor in the Department of Computer Science and Engineering at the State University of New York's School of Engineering and Applied Sciences. His research focuses on wireless systems, machine learning, and IoT applications at the intersection of wireless sensing and robotics. He holds a PhD in Electrical and Computer Engineering from the University of California, San Diego (2023), following an MS from the same institution in 2019. His work emphasizes designing scalable and reliable systems to bridge the digital and physical worlds, with notable contributions including Bluetooth localization protocols incorporated into the BLE5.1 standard and collaborations with Cisco DNA-Spaces for indoor localization systems. Dr. Ayyalasomayajula’s research spans WiFi-based SLAM optimization, UWB tag tracking, and privacy-preserving wireless systems. Key research areas include resource-efficient localization algorithms, adversarial network security, and deep learning applications in acoustics and robotics. His projects address challenges in indoor navigation, edge computing, and cross-platform sensor fusion. His publications highlight advancements in WiFi and UWB systems, privacy mechanisms for wireless devices, and novel multi-task learning approaches. He actively contributes to both theoretical and applied research in wireless communication protocols and IoT architectures.

Fahad AhmadView profile

Lecturer

• Machine Learning
• Cybersecurity
• Medical Informatics
• +6 more

Fahad Ahmad is a Lecturer in the School of Computing within the Faculty of Technology at the University of Portsmouth. He holds affiliations with the Portsmouth AI and Data Science Centre, Centre for Cybercrime and Economic Crime, and Portsmouth Centre for Advanced Materials and Manufacturing. His research focuses on machine learning applications in healthcare, cybersecurity, and quantum computing. He supervises PhD students in topics like quantum machine learning for securing IoT medical devices. Key research areas include: Medical imaging diagnostics using deep learning (e.g., echocardiograms, X-rays) Cybersecurity for financial systems and SDN-NFV networks Quantum key distribution for post-quantum security AI-driven health management systems Recent work emphasizes: Human activity recognition through machine learning Cancer subtype classification using RNA expression data Emotional empathy modeling in intelligent agents His articles span healthcare technology, cybersecurity frameworks, and hybrid AI architectures. He actively contributes to international conferences and journals, with over 60 peer-reviewed publications. Research collaborations include institutions in Pakistan and the UK.

Majid AhmadiView profile

Professor

• Digital Signal Processing
• Machine Vision
• Pattern Recognition
• +4 more

Dr. Majid Ahmadi is a Distinguished Professor in the Department of Electrical and Computer Engineering (ECE) at the University of Windsor's Faculty of Engineering. He holds a BSc from Sharif University of Technology (1971) and a PhD from Imperial College London (1977). His research focuses on digital signal processing, machine vision, VLSI systems, and memristive circuits. He is a Chartered Engineer (UK) and a Fellow of both IET and IEEE. His work spans cutting-edge areas including low-power VLSI architectures, illumination-invariant face recognition, and 3D IC testing. Recent publications address energy-efficient cognitive radio networks and microstrip filter optimization. His professional accolades include prestigious engineering fellowships. He advises graduate research in advanced signal processing and integrated circuit design. His contributions to IEEE Transactions and IET journals reflect his leadership in interdisciplinary electronics research.

Pere Millán MarcoView profile

Associate Professor

• Computer Communications
• Mobile and Sensor Networks
• Internet of Things
• +3 more

Pere Millán Marco is an Associate Professor in the Department of Computer Engineering and Mathematics (DEIM) at Universitat Rovira i Virgili (URV), Tarragona, Spain. He serves as coordinator for Non-permanent lecturers. His research focuses on computer communications, mobile/sensor networks, and IoT, with emphasis on quality prediction and performance optimization in wireless environments. Education: M.Sc. in Computer Engineering (Polytechnic University of Catalonia, 1992); PhD in Computer Engineering (URV, 2018). Research Interests : Specializes in mobile and sensor network architectures, underwater acoustic networks, real-time communication protocols, and low-cost hardware solutions for educational and environmental applications. His work integrates time series analysis, predictive modeling, and social behavior patterns to enhance network efficiency. Publications Trends : Recent work emphasizes IoT applications in environmental monitoring (e.g., deep aquifer pumping systems), optimization of underwater acoustic networks, and low-cost multicomputer systems for teaching. Consistently explores predictive techniques for network topology and quality-of-service improvements in ad hoc and community networks. Awards & Recognition : Holds a granted US patent related to location-based information systems. Advising & Infrastructure : Involved in DEIM's teaching laboratory development. Active in organizing international conferences like UCAmI and VISIGRAPP. Member of the CloudLab research group, advancing cloud and distributed computing solutions.

Amit Kumar MishraView profile

Professor

• Machine Learning
• Radar Technology
• Synthetic Aperture Radar (SAR)
• +7 more

Amit Kumar Mishra is a Professor of Communications Engineering and Director of the National Spectrum Centre at Aberystwyth University, UK. His primary affiliation is within the Faculty of Business and Physical Sciences. He leads interdisciplinary research in radar technology, machine learning, and cognitive architectures with applications in environmental monitoring, biomedical engineering, and cybersecurity. His research interests include Synthetic Aperture Radar (SAR) imaging, AI-driven environmental sensing (e.g., agricultural monitoring, biodiversity tracking), and advanced communication systems like IRS-enhanced NOMA networks. Recent work focuses on developing symbiotic AI-human cognitive systems for attention management and sensor data assimilation in industrial environments. Over 200+ publications span journals like Sensors , IEEE Transactions , and conference proceedings. Notable collaborations include projects on oil spill detection, underwater object recognition, and patient-specific medical modeling. He co-led the SMART Expertise project (2025) focused on innovation bridging. Media highlights include reports on medical imaging applications (Coarctation of Aorta studies) and the Aberystwyth University Technology Centre's innovation initiatives. His work aligns with UN SDGs related to sustainable cities and responsible consumption patterns.

Rand RaheemView profile

Lecturer

• Wireless Communication Systems
• Mobile and Vehicular Networks
• Femtocells Technology
• +3 more

Dr. Rand Raheem is a Lecturer in Computer Science at Middlesex University's Faculty of Science and Technology, London. She specializes in wireless communication systems, vehicular networks, and interference management in future mobile networks. Her research focuses on improving performance in safety-critical wireless sensor networks and 4G/5G/6G technologies. Education: Diploma in Electrical and Computer Engineering (Dhofar University, Oman, 2009) BSc Computer Communication Engineering (Dhofar University, 2010) MSc Telecommunication Engineering (Middlesex University, 2011) PhD on interference management in small cell networks (Middlesex University, 2016) Research Interests: Mobile and vehicular network optimization Bio-inspired algorithms for network scheduling Dependable safety-critical systems Cooperative femtocell technology Machine learning applications in wireless networks Teaching: CST4540 - Network Management CST3570 - Network Management and Disaster Recovery CST2560 - Project Management and Professional Practice CST2531 - Compliance and Project Management CST1500 - Computer Systems Architecture and Operating Systems Key Research Trends: Recent work emphasizes bio-inspired algorithms (bird flocking, bat algorithms) and neural networks for enhancing wireless sensor network dependability. Earlier research focused on femtocell interference management in LTE networks and vehicular environments. Advising & Grants: No specific grants or supervisees listed. Active collaborator with researchers like Dr. A. Lasebae on multiple projects. Labs/Teams: Participates in network research initiatives at Middlesex University's Computer Science Department, though specific lab affiliations are unspecified.

• Optical Communications
• Quantum Cryptography
• Fiber Optic Sensing
• +5 more

Nikas Thomas is an External Instructor at the Department of Informatics (DI) of the National and Kapodistrian University of Athens (NKUA). His work spans multiple interdisciplinary areas including quantum cryptography, fiber optic sensing technologies, and seismic monitoring. Key roles include advancing secure communication protocols through quantum key distribution (QKD) and developing novel Li-Fi transceivers using perovskite photodiodes. He also pioneers applications of Distributed Acoustic Sensing (DAS) for urban earthquake monitoring in Athens, leveraging existing fiber-optic infrastructure for environmental and geophysical studies. His research bridges theoretical frameworks (e.g., phase transmission analysis) with practical implementations in optical communication systems and seismic detection. Research interests focus on: Secure optical communication systems leveraging quantum principles Fiber optic-based seismic and acoustic sensing Emerging Li-Fi technologies for high-speed wireless networks Phase-sensitive fiber optic analysis for geophysical applications Publications from 2022-2024 highlight trends in: Quantum security protocols for optical and radio-over-fiber systems Urban DAS applications for earthquake monitoring Microwave frequency interferometry for low-cost seismic sensors No scientific awards are explicitly listed in the provided materials. His work often involves collaborative projects with industry and academic partners, though specific grants are not detailed here. Current projects include optimizing DAS for real-time urban seismic networks and exploring novel modulation formats for secure optical transmission.

Markos AnastasopoulosView profile

Associate Professor

• Optical Communications
• Wireless Networks
• Mobile Computing
• +3 more

Markos Anastasopoulos is Associate Professor at the National and Kapodistrian University of Athens, specializing in optical/wireless networks and mobile computing. His research focuses on 5G/6G network convergence, intent-based management, and AI-driven optimization for telecommunications. Recent publications address THz-optical integration, federated learning in transport networks, and semantic-aware radio systems. Applied work includes intelligent asset management for railways and techno-economic analyses of network deployments. Recognized with best dissertation and best paper awards, his research bridges theoretical networking with industrial applications in transportation and cloud infrastructure.

• Telecommunications
• Signal Processing
• Wireless Communications
• +4 more

George Alexandropoulos is an Associate Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His research focuses on telecommunications, signal processing, and reconfigurable intelligent surfaces (RIS) for next-generation wireless networks. He has contributed to advancements in integrated sensing and communications (ISAC), 6G technologies, and holographic beamforming. His work includes experimental validation of RIS prototypes, optimization of RIS-assisted systems, and analysis of secure communication strategies. Research interests include RIS hardware design, channel modeling, and applications in IoT, UAV communications, and disaster recovery networks. He explores topics like energy-efficient RIS operation, multi-RIS coordination, and RIS-enabled localization. His studies often address challenges at sub-THz frequencies, mutual coupling effects, and hardware impairments. Publications emphasize practical implementations of RIS in both indoor and outdoor settings, with a focus on real-world performance evaluation. Awards and grants are not explicitly mentioned, but his extensive publication record indicates significant academic contributions. His research often integrates machine learning for RIS configuration and reinforcement learning for resource optimization in dynamic networks.