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Dr. Dena Bazazian is a Lecturer in Robotics and Machine Vision at the University of Plymouth's School of Engineering, Computing and Mathematics. Previously, she held positions as a Senior Research Associate at the University of Bristol's Visual Information Laboratory, Research Scientist at CTTC (Barcelona), and postdoctoral researcher at Universitat Autònoma de Barcelona, where she earned her PhD in 2018. Her international research includes visiting positions at Barcelona Supercomputing Center (2024), NAVER LABS Europe (2019), and University of Florence (2017). Her research focuses on robotic vision, underwater technologies, and AI-driven applications. Key interests include: 3D reconstruction and neural rendering techniques Anomaly detection using graph neural networks Underwater perception systems for marine environments Intergenerational co-design of immersive technologies Point cloud processing and geometric deep learning Publication analysis reveals strong emphasis on computer vision applications across robotics, industrial inspection, heritage preservation, and marine technology. Recent works increasingly integrate human-centered approaches with XR technologies and underwater systems. She currently supervises 8 PhD students across robotics, healthcare, and environmental monitoring domains: Offshore wind turbine maintenance drones (EPSRC-UDLA funded) Autonomous sea vessel swarms for climate forecasting (ARIA funded) AI for marine biodiversity monitoring (Cefas funded) Elderly fall detection systems (URS General funded) Healthcare anxiety detection through visual analysis (EPSRC DTP HMT funded) Dr. Bazazian leads modules including Artificial Vision and Deep Learning and Computer Vision . She co-organizes workshops including the Deep Learning for Geometric Computing series (CVPR/ICCV) and Women in Computer Vision workshops (CVPR/ECCV).

• Robotics
• Machine Vision
• Artificial Intelligence
• +3 more

Minhong Wang is a Professor at the Faculty of Education, University of Hong Kong, with additional affiliation at the University of Edinburgh's Usher Institute. With an extensive publication record spanning over two decades from 2005 to 2025, Wang has established themselves as a leading researcher in educational technology and learning sciences. Their work bridges the gap between computer science and education, developing innovative technology-enhanced learning environments that support complex skill development and knowledge construction. Wang's research interests focus on educational technology, learning analytics, computer-supported collaborative learning, and cognitive mapping approaches. Their work examines how technology can enhance problem-solving processes, support self-regulated learning, and improve educational outcomes across various contexts. Recent research has expanded into multimodal learning analytics, teacher professional development through technology, and the application of advanced computational methods to educational challenges. Wang's approach integrates theoretical frameworks with practical implementations, creating systems that transform how educators and learners interact with digital environments. Analysis of Wang's recent publications (2023-2025) reveals a strategic expansion of research scope while maintaining core educational technology focus. The work demonstrates increasing sophistication in learning analytics methodologies, with growing integration of computer vision techniques and advanced machine learning approaches. This reflects a trend toward more comprehensive multimodal analysis of learning processes, moving beyond traditional text-based interactions to incorporate visual, spatial, and behavioral data in educational contexts. The research maintains strong practical applications while advancing theoretical understanding of how technology mediates learning. Wang has collaborated extensively with researchers across multiple institutions globally, particularly in Hong Kong, mainland China, and international partners. Their work demonstrates consistent funding support through numerous research projects, though specific grant details aren't visible in the publication record. The collaborative nature of the work suggests leadership in research teams focused on developing and evaluating innovative educational technologies.

• Educational Technology
• Learning Analytics
• Computer-Supported Collaborative Learning
• +5 more

Dr. Petra Isenberg serves as a Research Director (DR2) at INRIA, leading the AVIZ research group and affiliated with the University of Paris-Saclay. Her research specializes in information visualization, visual analytics, and human-computer interaction, with particular focus on mobile and embedded visualization techniques. She teaches courses on Information Visualization and Visual Analytics at Université Paris-Saclay. Her recent work has earned honorable mentions at IEEE VIS 2024 for papers on perceived readability evaluation and speech interaction in collaborative analytics. Additionally, she received the IEEE SciVis Test of Time Award (2012) for her contributions to 3D point cloud visualization techniques. Current research explores glanceable visualizations for older adults, situated visualization in motion, and modeling register variation in semantic change.

• Information Visualization
• Visual Analytics
• Human-Computer Interaction
• +2 more

Nikolaos Vassilas serves as Professor in the Department of Informatics and Computer Engineering at the University of West Attica, Greece, with leadership roles including Director of the Intelligent Knowledge, Image & Information Systems research Laboratory (ISLab) since 2019 and former Director of the Joint Greek-French Master Degree Program (2016-2020). His academic journey includes research positions at NCSR "Demokritos" and EPFL. His educational credentials feature a Diploma in Electrical Engineering from Aristotle University of Thessaloniki (1983), and M.S.E.E. and Ph.D. degrees from the University of Minnesota (1987, 1990). Professional affiliations include IEEE Computational Intelligence Society, Hellenic Society of Artificial Intelligence, and Greek Computer Society. Professor Vassilas' research centers on Computer Vision and Neural Networks with applications in urban analysis and remote sensing. His work integrates deep learning for building extraction from aerial/LiDAR data, semantic modeling in 3D urban environments, and neural network-based environmental prediction systems. Key methodological focuses include mean-shift algorithms, Hough transforms, and multisource spatial data fusion. Analysis of his recent publications reveals consistent specialization in computer vision techniques for urban infrastructure analysis, particularly building contour detection and 3D reconstruction using convolutional neural networks. The integration of aerial imagery with elevation data and robust line detection algorithms represents a dominant research trajectory across his 70+ publications. No scientific awards were documented in the source materials. He has coordinated two European Union co-funded research programs and participated in multiple national projects, with expertise in environmental data processing and intelligent spatial analysis systems. His leadership extends to directing research laboratories and international academic programs, demonstrating significant grant management experience. Professor Vassilas leads the Intelligent Knowledge, Image & Information Systems research Laboratory (ISLab), which focuses on advancing computer vision techniques for urban planning and environmental monitoring applications through interdisciplinary team collaboration.

• Computer Vision
• Neural Networks
• Pattern Recognition
• +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

• HCI
• Multimedia
• Computer Networks
• +3 more

Kai Eivind Wu is a Researcher at the University of Sheffield’s School of Electrical and Electronic Engineering, specializing in many-objective optimization with applications in pharmaceutical engineering and polymer chemistry. His work focuses on advancing computational methods for process optimization, automation, and machine learning integration in complex systems. Research interests include developing novel decomposition strategies for evolutionary algorithms, improving diversity metrics in multi-objective optimization, and applying these techniques to real-world problems such as continuous pharmaceutical production and nanoparticle synthesis. Collaborations involve cross-disciplinary projects with cloud computing and automated laboratory systems. Publications highlight contributions to self-optimizing laboratory platforms, constraint-handled manufacturing processes, and visualization methods for Pareto front analysis. His work bridges theoretical optimization advancements with practical industrial applications. No scientific awards are explicitly listed. Advising and grant details are not provided in the text. Wu is affiliated with the Amy Johnson Building in Sheffield and contributes to research teams focused on automation and high-dimensional optimization challenges.

• Many-Objective Optimization
• Process Optimization
• Machine Learning Applications
• +3 more

Jordan Vice is a Research Fellow at The University of Western Australia's School of Physics, Maths and Computing, specializing in Computer Science and Software Engineering. They hold a Ph.D. in Mechatronic Engineering from Curtin University (2023) and a 1st Class Honours degree in the same field (2020). Their research focuses on AI transparency, fairness, security, and reliability, with expertise in generative models, explainable AI, and robotics. Education: Ph.D. in Mechatronic Engineering (2020-2023), Curtin University, Thesis: Accountable, Explainable Artificial Intelligence Incorporation Framework for a Real-Time Affective State Assessment Module Bachelor of Mechatronic Engineering (2015-2019), Curtin University, Thesis: Bi-modal Affect-Based Authentication Machines Research Interests: AI ethics, generative models, cybersecurity in AI systems, facial expression analysis, and AI applications in healthcare. They advocate for transparent and accountable AI deployment while addressing security and privacy challenges. Recent work explores bias quantification in text-to-image models and backdoor attacks in generative systems. Key Trends in Publications: Focus on adversarial machine learning, generative model vulnerabilities, and ethical AI frameworks. Notable contributions include frameworks for real-time affect assessment and methodologies to quantify bias in text-to-image systems. Awards: Chancellor's Commendation (2023) Proxima Consulting Prize (2020) RTP Scholarship (2019) Advising & Grants: No explicitly listed advisees or grants, though their work suggests involvement in collaborative research projects. Labs/Teams: Affiliated with the School of Computer Science and Software Engineering at UWA, contributing to interdisciplinary AI research groups.

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +7 more

Foo Ji Jinn is a Senior Lecturer in Mechanical Engineering at Monash University Malaysia, specializing in heat transfer and turbulence mechanics. He holds a PhD from Nanyang Technological University and has held roles including Associate Professor and Research Director at SEGi University. His research focuses on novel heat transfer methods using fractal geometry to enhance thermal dissipation, funded by MOHE and DAIAKIN R&D. Key projects include fractal-induced turbulence optimization and thermochemical ablation for cancer treatment. Education: BS/MS (Mechanical Engineering, National Chung Cheng University), PhD (Mechanical Engineering, Nanyang Technological University) Postdoc: Max Planck Institute (2004–2008) Research interests revolve around turbulence-induced heat transfer enhancement, biomedical applications of fluid dynamics, and sustainable thermal systems. Collaborations span institutions in Malaysia, Singapore, Germany, and Taiwan. Notable work includes optimizing fractal grids for heat exchangers and computational studies on sonothrombolysis and thermochemical ablation. Active in UN SDG 7 (Affordable Clean Energy) and 3 (Good Health & Well-being). Publications (2013–2024) emphasize computational fluid dynamics, biomedical engineering, and turbulence modeling. Projects include 'Fractal Generated Turbulence' and 'Thermochemical Ablation Mechanisms'. Teaching commitments include MEC3458 (Experimental Project) and MEC4417 (Refrigeration & AC Systems).

• Heat Transfer Augmentation
• Fractal Geometry Applications
• Computational Fluid Dynamics
• +3 more

Dr. Ying Chen is an Assistant Professor in Atmospheric Science at the University of Birmingham's School of Geography, Earth and Environmental Sciences. An expert in climate change and air quality, Dr. Chen focuses on aerosol-cloud-climate interactions, sustainable energy, and climate extremes. Holding a PhD from Leipzig University (2017) and MSc/BSc from Peking and Nanjing Universities, Dr. Chen teaches in Birmingham's MSc Air Pollution Management and Control program. Research interests include: Aerosol-cloud-climate interactions Air quality analysis Machine learning applications in climate science Climate tipping points and carbon budgets Recent publications (2023-2025) demonstrate expertise in pollution transport modeling, monsoon system interactions, and renewable energy climate impacts. Dr. Chen holds editorial roles at Frontiers in Environmental Science and Atmosphere, and has contributed to patents in wind/solar power forecasting methods. Scientific involvement includes: Member of AGU, EGU, and Royal Society of Chemistry Reviewer for prestigious journals (Nature, Geophysical Research Letters) Conference chair at International Aerosol Conference 2022

• Atmospheric Science
• Climate Change
• Air Quality
• +3 more

Peter Jan Van Leeuwen is a Professor specializing in data assimilation methodologies with applications across geophysical sciences. His research develops advanced techniques for high-dimensional systems with particular emphasis on particle filtering approaches. His research innovations include: Development of implicit equal-weights particle filters for high-dimensional systems Nonlinear data assimilation frameworks using particle flow filters Ensemble methods for model error estimation Advanced Bayesian inference techniques for geophysical applications Novel approaches for causal discovery in complex systems Recent publications demonstrate wide applications from oceanography and atmospheric science to flood forecasting and astrophysics. His work consistently addresses fundamental challenges in high-dimensional uncertainty quantification and nonlinear system behavior. Research contributions include significant methodological advances in: Particle filter efficiency for ocean and atmospheric models Time-correlated model error estimation Riemannian data assimilation frameworks Causal inference in non-intervenable systems Preconditioning strategies for 4D-Var assimilation Dr. Van Leeuwen has collaborated extensively on projects including the SEASTAR satellite mission concept for ocean submesoscale dynamics and contributed to major data assimilation initiatives like MERCATOR and MERSEA.

• Data Assimilation
• Particle Filters
• Oceanography
• +3 more

Raúl Rojas is the Fred D. Gibson, Jr. Endowed Professor in Science at the University of Nevada, Reno (UNR), affiliated with the College of Science. His research focuses on Artificial Intelligence, Robotics, Autonomous Cars, and Number Theory. He teaches courses such as Linear Algebra, Calculus, and Statistical Machine Learning. Rojas holds a PhD H.c. in Computer Science from Inastituto Nacional de òptica y Electrónica (2016), a Habilitation in Computer Science from Freie Universität Berlin (1994), and a PhD in Economics (1988). His notable publications include books like Konrad Zuse’s Early Computers (2023) and The Language of Mathematics (2025), alongside influential papers on topics ranging from early computing history to LiDAR-based autonomous vehicle systems. His work bridges historical computing milestones with modern robotics and mathematical theory. Rojas’ research has been presented at major conferences like IEEE ICRA and futuRetro, highlighting contributions to autonomous systems and computational heritage. His interdisciplinary approach integrates theoretical mathematics with applied robotics, reflecting a career dedicated to advancing computational science and its societal impacts.

• Artificial Intelligence
• Robotics
• Autonomous Cars
• +3 more

Dr. Wei Zhang is a Researcher at the Complex Systems and Data Science group within The University of Sydney. Her work focuses on network science, evolutionary dynamics, and complex systems, with an emphasis on modeling social systems and analyzing meso-scale structures in social networks. She combines theoretical approaches, data-driven modeling, and web-based experimental methods to study emergent collective phenomena. Dr. Zhang holds a PhD in Network Science from ETH Zurich. Her research spans interdisciplinary topics including data science applications to complex systems and the detection of structural patterns in social networks. Recent projects include exploring domain adaptation techniques in machine learning and developing methods for 3D object detection and model compression. Her publications reflect expertise in computer vision, machine learning, and environmental science. Key themes include domain adaptation networks, 3D reconstruction, and LiDAR-based forestry analysis. While no specific awards are mentioned, her work demonstrates significant contributions to theoretical and applied data science. Dr. Zhang collaborates on projects involving interdisciplinary teams, though specific grants or lab affiliations are not detailed in the provided text. Her current research continues to bridge complex systems theory with modern data-driven methodologies.

• Network Science
• Evolutionary Dynamics
• Complex Systems
• +5 more

Michael Bevis is a Professor and Ohio Eminent Scholar in the Division of Geodetic Science at The Ohio State University, where he leads the Geodesy and Geodynamics (G2) group. A space geodesist since the late 1980s, he holds a Ph.D. from Cornell University (1982) and has established geodetic networks across the South Pacific, Andes, Greenland, and Antarctica to study Earth system dynamics. Ph.D. in Geophysics, Cornell University, 1982 Bevis specializes in crustal motion geodesy, GPS meteorology, and reference frame realization, with research spanning plate tectonics, earthquake deformation cycles, mountain building, postglacial rebound, and sea-level change. His work integrates GNSS, GRACE, and terrestrial gravity data to investigate ice mass dynamics, elastic loading phenomena, and water vapor climatology, emphasizing the coupling between geophysical processes and climate systems. His recent publications (2023-2025) reveal a dominant focus on Antarctic and Greenland ice mass loss through elastic deformation modeling, alongside innovations in GNSS network applications for climate hazard monitoring and gravitational field theory. Key trends include joint inversions of multi-sensor geodetic data, advanced algorithms for satellite image processing, and theoretical refinements to spherical harmonic expansions for global gravity modeling. Ohio Eminent Scholar Bevis has secured extensive research funding for global geodetic network deployments, including the Polar Earth Observing Network (POLENET) in Antarctica and the Greenland Network (GNET). His projects involve major international collaborations with NASA, NSF, and global geodetic initiatives, supporting infrastructure for real-time crustal motion monitoring and climate impact assessment. While specific student names aren't listed in source materials, his leadership of the G2 group implies significant graduate mentorship in geodetic science. The Geodesy and Geodynamics (G2) group operates under the Division of Geodetic Science, maintaining specialized laboratories for GPS data processing, gravity measurements, and satellite geodesy. Bevis directs field teams conducting terrestrial gravity surveys in mountain chains and coordinates Antarctic network operations through the ANET GNSS array, with recent expansions into equatorial satellite constellations for climate hazard tracking.

• Geodesy
• Geodynamics
• Geophysics
• +10 more

Nicholas Meyer is a Lecturer at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. His research focuses on advanced geodetic measuring technologies, including terrestrial laser scanning (TLS) and 3D displacement estimation for structural and environmental monitoring. Key applications include deformation analysis of hydroelectric dams, long-range TLS in alpine environments, and vibration monitoring of bridges. His work emphasizes high-fidelity numerical simulations and multi-scale analysis, targeting infrastructure resilience and natural hazard assessment. He leads projects integrating TLS with RGB data for enhanced geomonitoring and employs self-supervised machine learning for debris flow analysis. Contact details: nicholas.meyer@geod.baug.ethz.ch. No scientific awards are explicitly listed in the provided materials. Research interests span geomatic engineering, environmental monitoring, and civil infrastructure diagnostics. Advising and grants sections remain unspecified, though his research group actively publishes in structural health and geospatial technologies.

• Geomatic Engineering
• Civil Engineering
• Environmental Monitoring
• +3 more

Dr. Nan Yu is a Senior Lecturer/Associate Professor at the University of Edinburgh's School of Engineering, serving as Deputy Director of the MSc Digital Design and Manufacture program. He holds visiting appointments at University College Dublin (2021-2026) and Osaka University (2022-2024). Dr. Yu's expertise spans precision manufacturing, plasma technologies, and additive manufacturing, with over £1M in grant funding as PI, 50+ peer-reviewed publications, and multiple awards including the Marie Curie Fellowship (2018-2020) and Royal Academy of Engineering Industrial Fellowship (2023-2024). He earned a PhD in Precision Engineering (Cranfield University, 2017), MSc in Mechanical Manufacturing (Harbin, 2013), and BSc in Mechanical Engineering (Harbin, 2011). His professional qualifications include Fellowships from the Royal Society of Art (2022) and Higher Education Academy (2023), alongside memberships in the European Society for Precision Engineering and CIRP. Research focuses on plasma-based precision machining, additive manufacturing sustainability, and digital twin frameworks. Key contributions include innovations in plasma polishing for optics, UAV swarm strategies, and edge computing sensors. Teaching includes courses on digital manufacturing, metrology, and mechanical design projects. He chairs the 12th CIRP Global Web Conference (2024) and has delivered 10 invited/keynote talks globally. His work emphasizes interdisciplinary collaboration, bridging advanced manufacturing with emerging technologies.

• Precision Engineering
• Plasma Technologies
• Additive Manufacturing
• +3 more