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Doitsidis EleftheriosView profile

Associate Professor

• Multirobot Systems
• Autonomous Vehicles
• Control Systems
• +5 more

Dr. Eleftherios Doitsidis is an Associate Professor at the School of Production Engineering & Management of the Technical University of Crete (TUC) and a member of the Intelligent Systems & Robotics Laboratory. Previously, he served as faculty at the Department of Electronic Engineering at Hellenic Mediterranean University. His expertise spans multirobot systems, autonomous vehicle control, and computational intelligence. He holds a robust record of EU and national research project involvement. Research Interests: Specializes in multirobot team coordination, autonomous navigation systems for UAVs/AUVs, control systems design, and computational intelligence applications. Recent work focuses on energy-efficient path-planning for swarms, educational robotics frameworks like HYDRA, and digital twin integration in autonomous systems. Publications Trends: His 150+ publications address cutting-edge topics including: Autonomous vehicle control architectures Modular robotics for STEM education Optimization algorithms for multirobot systems Energy efficiency in manufacturing systems Advising & Projects: Lead researcher on numerous funded projects involving UAV/AUV missions, swarm robotics, and educational technology. Active in collaborative research with institutions like the University of South Florida. Labs & Groups: Leads the Intelligent Systems & Robotics Lab at TUC, developing advanced robotic platforms and educational tools. Maintains an open-access research portal at doitsidis.tuc.gr .

• Robotic Vehicles
• Computational Intelligence
• Cooperative Control and Optimization
• +3 more

Lefteris Doitsidis is an Associate Professor at the School of Production Engineering and Management, Technical University of Crete. He holds a PhD in Production and Management Engineering from the same institution (2008), with prior academic positions at the Department of Electronics, Hellenic Mediterranean University. His professional journey includes visiting scholar roles at the University of South Florida, USA. Research focuses on robotic systems, including autonomous navigation of UAVs/AUVs, multirobot teams, computational intelligence, and educational robotics. He leads the Intelligent Systems and Robotics Laboratory, developing tools like HYDRA for STEM education and frameworks for industry 4.0 applications such as bin-picking and precision agriculture. His work integrates control systems optimization, energy efficiency in manufacturing, and digital twin technologies. Over 65 publications span journals, conferences, and books, emphasizing practical implementations like ROS-based autonomous vehicle testbeds and energy management systems for electric vehicles. Key contributions include UAV path planning algorithms, swarm robotics coordination, and sensor fusion techniques. Current research trends emphasize sustainability in manufacturing, educational robotics platforms, and autonomous systems validation through advanced algorithms like Deep Deterministic Policy Gradient.

Bin ZhangView profile

Associate Professor

• Prognostics and Health Management
• Fault Detection and Isolation
• Failure Prognosis
• +8 more

Dr. Bin Zhang is an Associate Professor in the Department of Electrical Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. With over 20 years of experience in prognostics and health management, intelligent systems and control, and robotics, he has established himself as a leading researcher in battery management systems, power electronics, and fault-tolerant control systems. Dr. Zhang's educational background includes: Ph.D. in Electrical Engineering from Nanyang Technological University, Singapore M.E. in Mechanical Engineering from Nanjing University of Science and Technology, China B.E. in Mechanical Engineering from Nanjing University of Science and Technology, China His research focuses on active approaches to achieve intelligent smart systems with self-situational-awareness and self-adapting capabilities. Primary interests include prognostics and health management (PHM), which covers fault detection and isolation, failure prognosis, and fault tolerance; robotics and unmanned systems; intelligent systems and control; and dynamic systems design, modeling, simulation and control. His work integrates physics-based models with data-driven techniques and computational intelligence, including pattern recognition and machine learning. Analysis of Dr. Zhang's recent publications reveals a strong emphasis on battery modeling (particularly lithium-ion batteries), power electronics control (including fractional order delay and virtual variable sampling techniques), and deep learning methods (including graph neural networks, deep residual convolutional neural networks, and deep belief networks). His research spans multiple application domains including power grids, batteries, aircraft, helicopters, and manned/unmanned vehicles. Dr. Zhang serves as Associate Editor for prestigious journals including IEEE Transactions on Industrial Electronics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Neurocomputing. He is a Senior Member of IEEE and a member of ASME. As director of the Resilient Systems Laboratory, Dr. Zhang advises numerous graduate students working on cutting-edge research in battery technology, power cable insulation, and control systems. His lab is equipped with advanced facilities including an 8-channel ARBIN BT-Smart battery testing system, power electronics control systems, cable/wire testing systems, rotating machinery testing systems, and unmanned vehicles including quadrotors and hexacopters.

Aritra DuttaView profile

Assistant Professor

• Machine Learning
• Federated Learning
• Optimization
• +7 more

Aritra Dutta is an Assistant Professor at the AI Initiative of the University of Central Florida (UCF), primarily affiliated with the Department of Mathematics and secondarily with the Department of Computer Science. He also holds an affiliation with the Pioneer Centre for AI (P1), Denmark. His research focuses on optimization (stochastic/nonconvex), distributed computing (including federated learning), numerical linear algebra, machine learning, low-rank approximation, and computer vision applications such as image/video analysis and object detection/tracking. Dr. Dutta’s work emphasizes interdisciplinary approaches, blending mathematical rigor with computational efficiency. He actively seeks Ph.D. students and postdocs with strong foundations in mathematics (optimization, linear algebra) or computer science (ML, distributed systems), prioritizing candidates with publication records in top-tier venues. His teaching includes courses in applied mathematics and computational methods. His research outputs span communication-efficient distributed learning frameworks, convergence analysis of optimization algorithms, and vision transformer architectures. Notable projects include GAEA (geolocation-aware conversational models) and MAVREC (multi-view aerial visual recognition). Postdoc opportunities: Open for exceptional candidates. Labs/Initiatives: UCF AI Initiative (UCF Aii), UCF Center for Research in Computer Vision (CRCV). Grants: Competitive research assistantships with tuition support.

• Antenna Engineering
• 6G/5G Communications
• Metamaterials
• +6 more

Ming Shen is an Associate Professor at the Department of Electronic Systems, part of The Technical Faculty of IT and Design at Aalborg University. His research focuses on antennas, millimeter-wave systems, and AI-driven RF sensors with applications in 5G/6G communications, biomedical engineering, and smart systems. His research interests span antenna design (including phased arrays, metamaterials, and compact structures), AI integration in electromagnetic systems, and medical sensor technologies. Recent projects include drone-based electromagnetic signature analysis, vibration energy harvesting for pacemakers, and smart healthcare systems for posture recognition and surgical site infection monitoring. Key projects include DRONES: Drone-Obtained Electromagnetic Signatures (2024–2028), Sensor Intelligence for Healthcare and Sports (2022–2027), and DeepBone (2021–2022), which explored deep learning for surgical infection detection. His work also bridges machine learning and electromagnetic design, with breakthroughs in surrogate modeling and automated antenna optimization. Ming Shen has supervised 6 PhD students and published over 160 peer-reviewed articles. Notable contributions include AI-assisted NLOS sensing, ultra-wideband antenna innovations, and medical applications such as electrical impedance-based bone healing assessment.

• Artificial Intelligence and Data Science
• Data Management & Mining
• Machine Learning & Intelligence
• +3 more

ZHENG Baihua serves as Professor of Computer Science at Singapore Management University's School of Computing and Information Systems (SCIS), concurrently holding leadership roles as Associate Dean for SCIS Post-Graduate Research Programmes and Director of the Master of Science in Computing programme. Currently on leave but maintaining full-time faculty status, his academic career spans over two decades with foundational training from Hong Kong University of Science and Technology. Professor Zheng's research program integrates artificial intelligence, data science, and urban computing to solve critical challenges in mobility and sustainability. His expertise centers on trajectory data management, social network analysis, and spatio-temporal modeling, with significant contributions to trajectory compression algorithms, influence minimization in social networks, and real-time traffic prediction systems. His work bridges theoretical database innovations with practical applications in smart city infrastructure and public health interventions. Analysis of recent publications (2024-2025) reveals a dominant focus on physics-informed trajectory processing, GPU-accelerated indexing for high-dimensional data, and transformer-based models for urban mobility prediction. Key trends include the fusion of graph neural networks with spatio-temporal dynamics, novel approaches to contact tracing through timeline graphs, and differentiable search techniques for structured data discovery. These works consistently target real-world deployment in transportation systems and epidemic control. No scientific awards are documented in available institutional records. Information regarding student supervision, research grants, laboratory facilities, or collaborative teams remains unspecified in current public profiles.

Enrique DunnView profile

Associate Professor

• 3D Computer Vision
• Computer Vision
• Geometric Relationships
• +6 more

Enrique Dunn is an Associate Professor in the Department of Computer Science at Stevens Institute of Technology. He holds an academic position within the Charles V. Schaefer, Jr. School of Engineering and Science. His research focuses on 3D Computer Vision, emphasizing geometric and semantic relationships in imaged environments. Dunn earned a B.S. in Computer Engineering from the Autonomous University of Baja California (1999), an M.S. in Computer Science from the Ensenada Center for Scientific Research (2001), and a Ph.D. in Electronics and Telecommunications (2006). He held postdoctoral roles at UNC Chapel Hill (2008–2012) before joining Stevens in 2016. His research interests include 3D reconstruction, visual odometry, and large-scale visual analytics. Dunn has authored over 40 papers in top conferences/journals such as CVPR and ICCV. He serves as an Associate Editor for Elsevier's Image and Vision Computing journal and has held roles in program committees for ECCV and 3DV. Key contributions include VOLDOR-SLAM (2021), NeuroCS (2023), and methods leveraging dense optical flow residuals for visual odometry. His team comprises Ph.D. students Juan Carlos Dibene and Siyuan Cao, with alumni Zhixiang Min (Apple) and Xiangyu Xu (InnoPeak). Dunn's work addresses challenges in crowd-sourced imagery and dynamic scene modeling.

Hany ElgalaView profile

Assistant Professor

• Telecommunications
• Digital Signal Processing
• Embedded Systems
• +6 more

Dr. Hany Elgala is an Assistant Professor in the Department of Electrical and Computer Engineering at the University at Albany, State University of New York, and Director of the Signals and Networks (SINE) Lab. Previously, he served as a Research Professor at Boston University, co-leading the Multimedia Communications Lab and acting as Communications Testbed leader at the NSF Smart Lighting ERC. His research focuses on telecommunications, digital signal processing, and embedded systems, with specialization in visible light communications (VLC), LiFi networks, IoT security, and AI-driven wireless solutions. He has coordinated industrial projects with Airbus and EADS on optical wireless networks in aircraft cabins. Elgala's work emphasizes heterogeneous wireless networks, backscatter communication, and security in IoT. He has authored/co-authored ~50 publications/patents, contributing to fields like spectral efficiency, energy optimization, and network coexistence. His leadership in the SINE Lab drives innovations in 6G networks, UAV-based systems, and hybrid LiFi-WiFi architectures. His research portfolio includes breakthroughs in VLC system design, machine learning applications for physical layer optimization, and jamming-resistant IoT networks. Recent efforts focus on indoor flying networks and low-complexity VLC frameworks leveraging multi-task learning and autoencoder-based modulation.

Zafeiria RoumeliotiView profile

Assistant Professor

• Seismology
• Geophysics
• Earthquake Source Mechanics
• +3 more

Zafeiria Roumelioti is an Assistant Professor at the Department of Geology, University of Patras. Her research focuses on seismology, geophysics, and earthquake engineering, with a strong emphasis on earthquake source mechanics, ground motion simulation, and seismic hazard assessment. She leads projects such as ARGONET and PROION, involving advanced geophysical instrumentation and multidisciplinary monitoring networks. Education: BSc in Geology MSc in Geophysics PhD in Seismology from Aristotle University of Thessaloniki Research Interests: Kinematic modeling of earthquake sources Site effect studies using vertical arrays Strong ground motion simulation (deterministic/stochastic) Real-time earthquake monitoring systems Nonlinear soil response during seismic events Regional seismicity and tectonic dynamics Her work integrates geophysical data from accelerometers, GNSS, and distributed sensors to improve earthquake early warning systems and infrastructure resilience. Key contributions include the ARGONET vertical array and PROION project’s multi-parametric monitoring framework.

• Cybersecurity
• Internet of Things (IoT)
• Blockchain
• +7 more

Amar Kumar Seeam is a Lecturer at the Mauritius Campus of Middlesex University. He holds a PhD in Building Simulation and Predictive Control from the University of Edinburgh (2015). His research spans cybersecurity, IoT, smart cities, eHealth systems, and public administration innovations. He has led projects on blockchain-based healthcare systems, augmented reality control platforms, and cyber deception frameworks for IoT-edge networks. Notably, he contributed to pandemic response initiatives like ventilator prototyping during the 2020-21 crisis and developed disinfection technologies using visible light. He co-founded the Centre for African Smart Public Value Governance (C4SP), focusing on policy innovation in African urban development. His work integrates interdisciplinary approaches, combining technical systems with societal impact analysis. Education: PhD in Building Simulation (University of Edinburgh, 2015) Key Projects: MRIC/IEEE-funded multi-purpose light disinfection chamber Smart tourism chatbot for Mauritius Cybersecurity frameworks for IoT and SCADA systems Research Interests: Cyber-physical security, AI in education, blockchain healthcare systems, and sustainable energy management. His work emphasizes practical applications in developing regions, such as Mauritius and Africa, focusing on scalable solutions for public infrastructure and digital governance. Recent articles highlight trends in AI-driven education tools, cultural governance in multicultural projects, and IoT security protocols. His contributions span conferences like IEEE, ACM, and Edward Elgar Publishing, addressing both technical and policy-oriented challenges in emerging technologies.

Zhibo ZhangView profile

Professor

• Climate Science
• Atmospheric Sciences
• Aerosol Physics
• +5 more

Dr. Zhibo Zhang is a Professor in the Department of Physics at UMBC and serves as Associate Director of Academics at the Joint Center for Earth Systems Technology (JCET). He holds a Ph.D. (2008) and M.S. (2004) in Atmospheric Sciences from Texas A&M University and a B.S. (2001) in Atmospheric Sciences from Nanjing University. His research focuses on aerosol-cloud-radiation interactions, climate modeling, and remote sensing applications. Key areas include quantifying aerosol effects on Earth's energy budget, analyzing low-level cloud dynamics in the Southeast Atlantic, and improving climate model representations of aerosol-cloud processes. Research is supported by NASA, DOE, NSF, and JCET. His group, the Aerosol, Cloud, Radiation-Observation and Simulation (ACROS) Group, employs satellite data (e.g., NASA's A-Train, DOE's ARM) and global/regional models to study climate change, air quality, and weather impacts. Recent work examines African biomass burning aerosols' interactions with marine boundary layer clouds and the role of cloud diurnal cycles in regional warming. Dr. Zhang has received prestigious awards including the UMBC Mid-Career Award (2022) and the IRC Young Scientist Award (2016). He has held leadership roles such as UMBC Physics Graduate Program Director (2014–2019) and has authored over 100 peer-reviewed publications. His work addresses critical gaps in climate model parameterization, aerosol radiative effects, and satellite data interpretation. Current research priorities include developing the CAMRO-Dust database, improving dust aerosol retrievals using thermal infrared methods, and leveraging machine learning for cloud detection and classification. Collaborations emphasize multi-sensor data fusion and bridging observational and modeling communities to advance Earth systems understanding.

Michael NeriView profile

Researcher

• Computer Vision
• Deep Learning
• Audio Processing
• +3 more

Michael Neri is a Researcher in Audio Signal Processing at Tampere University and a Ph.D. student in Applied Electronics at Roma Tre University. His primary affiliation is with the Department of Signal Processing at Tampere University. He holds a B.Sc. in Information Engineering from the University of Padua (2019) and an M.Sc. in ICT for Internet & Multimedia from the University of Padova (2021). He was a visiting PhD student at Tampere University of Technology in 2023. His research focuses on Audio Processing, Computer Vision, and Deep Learning, with applications in safety and security. Key projects include RAILGAP (EU Horizon 2020), which uses GNSS for rail positioning, INSECTT (ECSEL EU Project) for secure IoT-AI systems, and ISEEYOO (University of Padova Grant) for anomaly detection in Cyber-Physical Systems. He has also contributed to the ESA-funded VOLIERA project on multi-sensor railway positioning. Neri's work spans environmental sound classification, speaker localization, and AI-driven multimedia quality assessments. His research is supported by grants from the European Union and ESA programs. He actively collaborates on projects addressing occupational safety through VR integration and anomaly detection in audio signals. Contact him via michael.neri@tuni.fi or his professional profiles on ResearchGate and GitLab .

Ramanathan SubramanianView profile

Associate Professor

• Human-centered computing
• AI systems
• Non-verbal behavioral analysis
• +7 more

Ramanathan Subramanian is an Associate Professor in the Department of Information Technology & Systems, AI and Robotics at the University of Canberra. His past affiliations include IIT Ropar and UIUC, Singapore. He has received notable awards including the IEEE Transactions on Affective Computing Best Paper Award (2019) and Multimedia Rising Star nomination (2015). His research focuses on Human-centered computing, AI systems leveraging non-verbal behavioral cues, and applications in health analytics and virtual reality. He actively supervises PhD and Master’s students in areas like Human-Computer Interaction, Data Science, and VR for healthy aging. Research interests: Human perception modeling, intelligent interfaces, health-focused VR, and applied machine learning Key projects: Driver sentiment prediction, distraction awareness campaigns, and robotics for waste management His work contributes to UN SDGs related to health and well-being, particularly through technologies improving aging care and mental health monitoring. Recent projects include developing explainable AI for emotion inference and multimodal data fusion systems. Awards: IEEE Best Paper Award, Multimedia Rising Star Grants: Lead or Co-Investigator on three major grants totaling over AUD 4M Labs/Teams: Collaborates extensively with the University's robotics and health tech groups, contributing to interdisciplinary projects in AI ethics and medical signal processing.

Mahmood Azimi-SadjadiView profile

Professor

• Digital Signal Processing
• Machine Learning
• Distributed Sensor Networks
• +3 more

Dr. Mahmood Azimi-Sadjadi is a full Professor in the Department of Electrical and Computer Engineering at Colorado State University (CSU), affiliated with the Walter Scott, Jr. College of Engineering. He leads the Digital Signal/Image Processing Laboratory , focusing on advanced signal processing, machine learning, and underwater acoustics research. His work spans sonar systems, distributed sensor networks, and remote sensing applications. Educational Background: B.S. in Electrical Engineering, University of Tehran (1977) M.S. and Ph.D. in Electrical Engineering, Imperial College of Science and Technology, University of London (1978, 1982) Research Focus: His research emphasizes statistical signal/image processing, machine learning, and underwater target detection/classification using sonar. He has pioneered methods like manifold-based UXO classification and Bayesian occupancy grids. His lab develops both theoretical frameworks (e.g., canonical correlation analysis) and applied systems (e.g., acoustic tomography). Recent Publications Trends: Recent work includes adaptive machine learning for underwater UXO classification, wildfire prediction via satellite data, and autonomous systems navigation. His publications blend signal processing fundamentals with cutting-edge AI techniques. Awards & Affiliations: 1999 Abell Faculty Teaching Award (College of Engineering) Fellow of CIRA (Center for Information and Remote Sensing Research) Member of IEEE (Life Member) and IEEE Neural Network Society Advising & Labs: Directs the Digital Signal/Image Processing Lab, which collaborates on projects like acoustic source localization and sensor network optimization. His research has been supported by grants from agencies like SERDP (e.g., MR-2416 project).

Michalis MichaelidesView profile

Assistant Professor

• Communication Systems
• Wireless Sensor Networks
• Environmental Monitoring
• +6 more

Michalis Michaelides is an Assistant Professor at the Department of Electrical Engineering, Computer Engineering and Informatics at the Cyprus University of Technology , where he has held a tenure-track position since 2012. He holds a PhD in Electrical Engineering from the University of Cyprus (2009), and earlier degrees from Purdue University (MSEE, 1998; BSEE, 1997). His research focuses on communication systems, wireless sensor networks, environmental monitoring, and computational intelligence applications in maritime logistics and smart infrastructure. He received the Elsevier Building and Environment Journal Best Paper Award in 2014. His work spans smart port operations , IoT integration , and data-driven environmental modeling . Recent publications address berth allocation optimization, vessel arrival prediction, CO2-based virus transmission risk analysis, and AI-driven maritime terminal management. His interdisciplinary approach combines signal processing, optimization algorithms, and sensor networks to solve challenges in transportation, environmental sustainability, and public health. Education: PhD in Electrical Engineering, University of Cyprus (2009) MSEE, Purdue University (1998) BSEE, Purdue University (1997) Awards: Elsevier Building and Environment Journal Best Paper Award (2014) His research group collaborates on projects like intelligent irrigation systems and coastal eutrophication analysis , leveraging computational intelligence for real-world applications. Ongoing work emphasizes port efficiency, sensor network reliability, and decision support systems for maritime logistics.