جست‌وجوی استاد و پژوهشگر

Abhishek Phadkeمشاهده پروفایل

استادیار

• Cyber Physical Systems
• Unoccupied Aerial Vehicles (UAV)
• Swarm Robotics
• +۸ مورد دیگر

Dr. Abhishek Phadke is an Assistant Professor at Christopher Newport University (CNU) within the School of Engineering and Computing. His research focuses on resilient cyber-physical systems, UAV swarms, robotics, blockchain, and smart grids. He leads the ADAPT Lab, exploring heterogeneous robotics, sensor security, and UAV swarm optimization. PhD in Geospatial and Computer Science, Texas A&M University-Corpus Christi MS in Electrical Engineering, Texas A&M University-Kingsville BE in Electronics Engineering, University of Mumbai His research spans Cyber-Physical Systems , where he investigates robustness and adaptability in dynamic environments; UAV Swarms , emphasizing navigation, communication, and resiliency; and Blockchain , particularly for security and data integrity in decentralized systems. Additional interests include Smart Grids , renewable energy, and vehicular robotics. The ADAPT Lab, under Dr. Phadke, has produced numerous publications on UAV swarm resilience, blockchain integration, and simulation frameworks. His work has secured $116,527 in external funding. Current graduate students in the ADAPT Lab include George Hill and Mason Beckmeyer, focusing on applied physics and blockchain-driven cybersecurity. Undergraduate Cole Thomsen explores drone hardware and 3D printing. Dr. Phadke's prior advisor was Dr. F. Antonio Medrano. Notable collaborations and lab activities emphasize cross-disciplinary innovation, bridging robotics, cybersecurity, and sustainable technologies.

Kenechukwu Mbanisiمشاهده پروفایل

استادیار

• Human-Robot Interaction
• Autonomous Vehicles
• AI Systems
• +۳ مورد دیگر

Kenechukwu Mbanisi is an Assistant Professor of Robotics Engineering at Olin College. He holds a Ph.D. and M.S. in Robotics Engineering from Worcester Polytechnic Institute (2022, 2018), and a B.Eng. in Electrical and Electronic Engineering from Covenant University, Nigeria (2013). His research focuses on human-centered AI systems, shared autonomy in intelligent vehicles, and advancing engineering education in Africa. Research interests include: - Human-Robot Interaction design - Social navigation assistance for telepresence robots - Equity in STEM education through outreach programs like the Pan-African Robotics Competition (PARC) Engineers League. - Experiential learning models for underrepresented groups in robotics. Publications emphasize practical applications of AI and robotics in education and industry, with a focus on human-centric design principles. His work bridges technical innovation with societal impact through community initiatives. Awards: 2021 Dr. Glenn Yee Graduate Student Tuition Award Outreach: Coordinator of PARC Engineers League (30+ African countries), Cobots for Kids program Teaching: Principles of Integrated Engineering (PIE) Labs/Teams: Actively involved in collaborative robotics education projects and human-robot system design initiatives at Olin College.

• Autonomous Vehicles
• 3D Object Detection
• Motion Planning
• +۵ مورد دیگر

Sudip Dhakal is an Assistant Professor in the Department of Computing & Software Engineering at Florida Gulf Coast University's U.A. Whitaker College of Engineering. His research focuses on autonomous systems, computer vision, and AI applications in robotics. He specializes in 3D object detection, real-time motion planning, and cybersecurity for autonomous vehicles. Key research areas include facial emotion recognition using CNNs, sensor fusion for object detection (e.g., Camera-LiDAR integration), and privacy-preserving technologies for vehicular systems. His work addresses challenges like sparsity in 3D data, dynamic environment adaptation, and threat modeling for autonomous driving safety. Publications emphasize cutting-edge solutions in autonomous vehicle motion planning, privacy protection mechanisms, and open-source approaches to self-driving systems. Recent work (2023-2025) highlights advancements in real-time algorithms, differential privacy applications, and multi-modal sensor fusion frameworks. No scientific awards are explicitly listed. Advising and grant details are not provided in available texts. His affiliation with the Whitaker College positions him within a robust engineering research ecosystem focused on innovation in computing and software systems.

• Computer Vision
• Medical Imaging
• Deep Learning
• +۴ مورد دیگر

Zhifeng Wang is a Professor at the National University of Defense Technology's School of Computer Science, specializing in computer vision, medical imaging, and artificial intelligence. His research spans multiple application domains with a recent focus on medical image synthesis, 3D reconstruction, and fluid dynamics simulation. Research interests include developing advanced AI techniques for medical imaging applications, particularly in vascular structure analysis and synthesis. His work bridges computer vision with healthcare applications, focusing on creating anatomically accurate 3D representations without invasive procedures. He has made significant contributions to diffusion models, neural radiance fields (NeRFs), and meta-learning frameworks for cross-domain applications. His publication record shows a clear progression from foundational work in graph algorithms and communications systems to cutting-edge medical AI applications. Recent work demonstrates expertise in combining state space models with diffusion techniques for medical image generation, showing particular strength in maintaining anatomical continuity in 3D vascular structures. While specific awards aren't documented in the provided information, his publications in top venues including CVPR 2025 indicate significant recognition in the computer vision community. His collaborative work spans multiple institutions and research domains, suggesting an active research group with diverse funding sources. His laboratory appears to focus on medical imaging AI, with particular emphasis on non-invasive diagnostic techniques. Current projects involve developing sophisticated diffusion models that can generate high-fidelity angiographic images from non-contrast inputs, potentially reducing patient exposure to harmful contrast agents.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Jia Liu is a Professor at Tsinghua University's School of Information Science and Technology, Department of Computer Science, with a distinguished research career spanning multiple disciplines in computer science and engineering. Her extensive publication record demonstrates leadership in artificial intelligence, machine learning, and their applications across diverse domains. Dr. Liu's research interests encompass the development and application of advanced computational techniques to solve complex real-world problems. She has pioneered innovative approaches in neural network architectures, medical imaging analysis, remote sensing applications, and multi-agent systems. Her work on Asym-UNet and ThreeF-Net has significantly advanced breast lesion segmentation techniques, while her contributions to digital twin technology have enhanced nuclear reactor operations and Martian exploration systems. Analysis of Dr. Liu's recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications in healthcare, environmental monitoring, defense technology, and education. Her work demonstrates consistent innovation in algorithm development, particularly in adapting machine learning techniques to domain-specific challenges. Dr. Liu maintains an active research program with numerous collaborations across academia and industry. Her work on federated learning for dual carbon initiatives reflects her commitment to addressing global sustainability challenges through technological innovation. As an educator, Dr. Liu has developed innovative teaching methodologies that integrate virtual reality and game-based learning, particularly in language education. Her research on interactive e-learning environments demonstrates her dedication to improving educational outcomes through technology.

• Artificial Intelligence
• Computer Vision
• Medical Imaging
• +۳ مورد دیگر

Rong Zhang is affiliated with the University of Southampton's School of Electronics and Computer Science in the UK. Their research spans interdisciplinary areas including artificial intelligence, biomedical engineering, and computational mathematics. Recent work focuses on autonomous systems, medical image analysis, and graph theory applications. Key research interests include machine learning for healthcare, computer vision, quantum walks, and environmental remote sensing. Publications from 2024-2025 highlight contributions to autonomous driving decision models, medical imaging segmentation techniques, and mathematical analysis of graph spectra.

Ravi Mukkamalaمشاهده پروفایل

استاد

• Data Security
• Cloud Computing
• Distributed Systems
• +۵ مورد دیگر

Ravi Mukkamala is a Professor & Chair in the Department of Computer Science at Old Dominion University . He holds advanced degrees including a Ph.D. in Computer Science (University of Iowa, 1987) and an MBA from Old Dominion University (1993). His research focuses on security, distributed systems, cloud computing, and real-time database systems, with notable contributions to privacy-preserving data management, blockchain applications, and UAV-based healthcare logistics. Education: Ph.D. in Computer Science, University of Iowa, 1987 M.B.A., Old Dominion University, 1993 Other in Computer Systems, Indian Institute of Technology Kanpur, 1978 B.E. in Electronics and Communications, Osmania University, 1976 Research Interests: Dr. Mukkamala’s work spans cybersecurity, blockchain technology, vehicular and UAV systems, distributed databases, and aviation software. He emphasizes practical applications like secure payment systems, privacy-preserving data mining, and automated healthcare delivery via drones. Articles Overview: His recent publications address cutting-edge topics such as blockchain-based healthcare monitoring, UAV logistics for medical supplies, and privacy-preserving machine learning techniques. Earlier work includes foundational studies on distributed databases and real-time systems security. Awards: Best Paper Awards (2008–2009) Phi Kappa Phi and Beta Gamma Sigma memberships Most Inspiring Faculty Member Award (Old Dominion University) Grants & Advising: He has led over 20 grants totaling millions, including projects on airspace systems, IT infrastructure management, and STEM education. His advisory work includes aviation software testing (e.g., TDMS) and coalition-based resource sharing. Labs & Teams: Collaborates with university and industry partners on projects involving cybersecurity, UAV systems, and distributed cloud architectures.

Stephan Olariuمشاهده پروفایل

استاد

• Vehicular Ad Hoc Networks (VANETs)
• Cybersecurity
• Telemedicine
• +۴ مورد دیگر

Stephan Olariu is a Professor of Computer Science at Old Dominion University (ODU), affiliated with the College of Sciences. He holds a Ph.D., M.Sc., and B.S. in Computer Science from McGill University (1982–1986). His research focuses on vehicular ad hoc networks (VANETs), cybersecurity, telemedicine, and vehicular cloud computing. He has led projects like TRENDS, a cyber-physical system for traffic event detection, funded by ODU (2011). Dr. Olariu's work emphasizes vehicular networks' security and efficiency, including solutions for VANETs, UAV-based medical supply delivery, and blockchain-driven decentralized marketplaces. He explores smart city technologies, such as traffic optimization and environmental impact reduction. His recent publications address topics like drone coordination for surveillance, trust frameworks in blockchain systems, and UAV applications in counterterrorism. Grants: TRENDS (2011) and others focused on vehicular systems and telemedicine. Key Themes: Vehicular cloud computing, cybersecurity, UAV systems, and smart infrastructure. His contributions span academic leadership in computer science, with a focus on translating theoretical research into real-world applications like emergency response systems and resilient decentralized platforms.

Alessio Iovineمشاهده پروفایل

پژوهشگر

• Control Systems
• Power Systems
• Cyber-Physical Systems
• +۴ مورد دیگر

Alessio Iovine is a CNRS researcher at the Laboratory of Signals and Systems (L2S) affiliated with CentraleSupélec (Paris-Saclay University). His research focuses on control systems for cyber-physical applications, including energy systems (e.g., microgrids, renewable integration) and transportation (e.g., autonomous vehicles, traffic control). He holds a European Doctorate in Information Science and Engineering from the University of L’Aquila and CentraleSupélec, with postdoctoral experience at institutions like UC Berkeley and Efficacity. Research Areas: Cyber-Physical Systems DC/AC Microgrid Control Autonomous Vehicles & Adaptive Cruise Control Predictive Grid Management Multi-Agent Systems Publications Trends: Recent works emphasize mesoscopic control approaches for vehicular systems, distributed control for multi-agent networks, and predictive algorithms for power grid congestion management. Over 20 peer-reviewed articles since 2017 reflect his expertise in nonlinear control, hybrid systems, and renewable integration. Opportunities: Actively recruiting PhD/Master students for projects in cyber-physical power systems, machine learning for grid identification, and autonomous traffic control. Hosts seminars on energy systems and signal processing at Paris-Saclay. Labs/Teams: Member of L2S’s Modelling and Estimation team, collaborating with groups like ILOCOS (Information, Learning, Optimization & Communication Sciences).

Jocelyn FIORINAمشاهده پروفایل

استاد

• Wireless Communication
• Ultra-Wideband Systems
• Signal Processing
• +۳ مورد دیگر

Jocelyn FIORINA is a Full Professor and ViceDean of International Affairs at CentraleSupélec. She is affiliated with the Laboratoire des signaux et systèmes (L2S), where she leads research in telecommunications and signal processing. Her academic titles include HDR (Habilitation to Direct Research) and Engineer qualifications. Her research focuses on wireless communication systems, ultra-wideband (UWB) technologies, and advanced signal processing techniques. Key areas include indoor localization, federated learning for communication constraints, time reversal methods in UWB networks, and multi-armed bandits for network optimization. She has authored over 20 peer-reviewed articles and two books, including Cognitive Radio and Networking for Heterogeneous Wireless Networks (Springer, 2015). Recent work emphasizes federated learning strategies under communication limits (2024), adaptive K-nearest neighbor positioning (2022), and UWB beamforming (2013). Her contributions span theoretical analysis, algorithm design, and practical implementations in ad-hoc networks and sensor systems. As ViceDean, she oversees international collaborations and academic partnerships. Her lab, L2S, is part of Paris-Saclay University’s research ecosystem, focusing on energy, industry 4.0, and healthcare applications. She actively participates in workshops and conferences, including IEEE Vehicular Technology Conferences and the International Conference on Ultra-Wideband (ICUWB).

Khalid Elgazzarمشاهده پروفایل

دانشیار

• Internet of Things (IoT)
• Artificial Intelligence
• Big Data
• +۷ مورد دیگر

Dr. Khalid Elgazzar is an Associate Professor and Canada Research Chair in the Internet of Things (IoT) at Ontario Tech University's Faculty of Engineering and Applied Science. He holds a PhD in Computer Science from Queen's University and has expertise in IoT, AI, Big Data, distributed systems, and mobile computing. His research bridges physical infrastructure with technological innovations, with applications in healthcare, transportation, and smart cities. Education: PhD in Computer Science, Queen's University, 2013 MSc in Computer Engineering, Arab Academy for Science and Technology, 2007 BSc in Computer and Communication Engineering, Alexandria University, 1995 Research Interests: Dr. Elgazzar focuses on IoT architectures, real-time data analytics, and edge computing, with emphasis on cybersecurity, smart healthcare systems, and transportation safety. His work integrates AI techniques like deep learning and reinforcement learning to address challenges in distributed computing and sensor networks. Awards: Best Paper Award at IEEE/ACM International Conference on Utility and Cloud Computing (2013) Outstanding Achievement in Sponsored Research Award (2017) Queen's School of Computing Distinguished Research Award (2014) Grants & Labs: Recipient of a Canada Research Chair Tier II (2018-present). He leads the IoT Research Lab, advancing projects like real-time ECG monitoring systems and intelligent traffic prediction models. Collaborations include institutions like Carnegie Mellon University and IBM Canada. Teaching: Design and Analysis for IoT Software Systems (SOFE 4610U) Real-Time Data Analytics for IoT (ENGR 5785G) Introduction to Programming for Engineers (ENGR 1200U)

• Internet of Things (IoT)
• Cyber-Physical Systems (CPS)
• Smart Manufacturing
• +۴ مورد دیگر

Dirk H J Pesch is a Professor at the School of Computer Science and Information Technology , University College Cork (UCC), and currently serves as Head of School. His research focuses on future networked systems for the Internet of Things (IoT) and Cyber-Physical Systems (CPS), with applications in smart communities, health, and manufacturing. He leads the Science Foundation Ireland-funded ADVANCE CRT and is a co-PI of the CONNECT Centre . Prior to UCC, he was at Cork Institute of Technology (now Munster Tech U), where he founded the Nimbus Research Centre. He holds a PhD from the University of Strathclyde and a Dipl.Ing from RWTH Aachen University. Research Interests include IoT/CPS protocols, system architectures, interoperability challenges, and AI-driven analysis for well-being. He has authored/co-authored over 250 publications and co-edited a book on energy-positive urban systems. His work spans conference organization (e.g., IEEE SmartComp 2025 General Chair) and industry collaborations, with €80M+ in grant funding. Key contributions include developing low-power wireless protocols (e.g., MeshMAC), smart building monitoring systems, and frameworks for IoT in energy-positive neighborhoods. His recent work explores loneliness detection via passive sensing and 6G-V2X technologies for autonomous driving. Grants & Leadership: Directed €16M of his own research grants, managed large-scale EU/Irish projects, and advised on industrial IoT implementations. His Nimbus Centre became a leader in IoT applications under his leadership. Labs/Teams: Founded Nimbus Research Centre; active in Cork Smart Gateway initiative. Supervised numerous students and researchers in interdisciplinary projects.

Zhenhui Yuanمشاهده پروفایل

استادیار

• Machine Learning
• Networking
• IoT
• +۸ مورد دیگر

Zhenhui Yuan is an Assistant Professor at the University of Warwick, School of Engineering, leading the Connected Robotics Lab. He holds a BEng in Software Engineering from Wuhan University (2008) and a PhD in Electronic Engineering from Dublin City University (2012). His research focuses on machine learning in networking, IoT, and robotics, with expertise in UAV communications, edge AI, and vehicular networks. Prior roles include Senior Lecturer at Northumbria University (2020–2023), Associate Professor at Hangzhou Dianzi University (2015–2019), and co-founder/CTO of RobSense Technology (2015–2019). He has received Best Paper Awards at IEEE ICCRE 2016 and IEEE BMSB 2014. His research interests span machine learning applications in networking, robotics, and IoT, with emphasis on UAV swarm systems, edge computing, and quality-of-service optimization. He serves as a Guest Editor for IEEE journals and chairs workshops on 6G-empowered robotic vehicles. He leads projects funded by the University of Warwick (£9k), British Academy (£9k), and Horizon Europe (£92k), among others. His work includes developing the Connected Robotics Lab’s testbeds for 5G emulation and drone swarm control. Key achievements include pioneering AirSlice (UAV network slicing), founding the SwarmLink radio, and advancing video quality metrics for 3D streaming. He actively supervises PhD candidates and collaborates on grants addressing holographic telesurgery, multimodal transport networks, and EV optimization. His labs focus on hardware-in-the-loop emulation and edge AI for robotic systems.

Brian Markمشاهده پروفایل

استاد

• Communication Networks
• Wireless Communications
• Network Security
• +۶ مورد دیگر

Brian Mark is a Professor and Interim Chair of the Department of Electrical and Computer Engineering at George Mason University since 2000. His research focuses on communication networks, wireless systems, and network security. Prior affiliations include NEC Laboratories America and Télécom Paris. Education: PhD in Electrical Engineering (Princeton University, 1995), B.ASc. in Computer Engineering (University of Waterloo, 1991) Research Interests: Communication networks, 5G/6G wireless, machine learning, stochastic models, network security Awards: NSF CAREER award (2002), Outstanding Research Faculty award (2011) Editorial Roles: Associate Editor, IEEE Transactions on Vehicular Technology (2006-2009) His recent work explores dynamic spectrum sharing in 5G/6G networks, statistical inference for network performance, and security in IoT environments. He applies machine learning to wireless coexistence and object tracking, while also addressing challenges in traffic network optimization and cryptographic offloading. Scientific recognition includes the NSF CAREER award, IEEE Senior Membership, and the Volgenau School's Outstanding Research Faculty award. He has contributed to network tomography, spectrum sensing, and secure device pairing, with 15 recent publications spanning wireless design, AI security, and transportation network optimization.

Olivier Haasمشاهده پروفایل

دانشیار

• Control Systems Engineering
• Autonomous Vehicles
• Visible Light Communication
• +۵ مورد دیگر

Olivier Haas is an Associate Professor and Reader in Applied Control Systems at Coventry University's Centre for Future Transport and Cities. He leads the Intelligent Transportation Systems and 5G Team, focusing on multidisciplinary research spanning vehicle control systems, visible light communication, and medical applications of control engineering. Key affiliations: Coventry University, Centre for Future Transport and Cities Specializations: Autonomous vehicle systems, digital twin technology, radiotherapy control systems Research Interests: His work combines systems modeling, optimization, and AI with applications in: Autonomous vehicle testing and safety validation Deep learning for automated environment recognition Digital twin simulations for transportation infrastructure Visible light positioning for vehicular systems Medical applications including tumor tracking in radiotherapy Article Trends: Recent publications demonstrate expertise in vehicular communication systems (particularly visible light technology), optimization techniques for transportation systems, and machine learning applications in both automotive and IoT domains. His work consistently bridges control theory with practical implementations across multiple sectors. Scientific Awards: 2022 Energy Innovation Awards - Best Collaborative Initiative (for dynamic EV charging research) Elekta KTP Project 1st Prize (medical systems control) Professional Contributions: Dr. Haas has supervised 27 PhD students and contributed to major projects including UK CITE, iVMS, and EU FP6 MAESTRO. His industry collaborations span Jaguar Land Rover, Microcab, FEV, and Horiba-MIRA, with applications in powertrain optimization, fuel cell management, and hazardous material response routing. Labs & Teams: He leads the Intelligent Transportation Systems and 5G Team at Coventry University's Centre for Future Transport and Cities, maintaining close ties with international institutions like DLR and VERO prototype systems.