جست‌وجوی استاد و پژوهشگر

• Biophysics
• Cellular Mechanics
• Actin Cytoskeleton
• +۵ مورد دیگر

Dr. Ruby Peters is a Physics of Life Early Career Fellow at the University of Sheffield 's School of Mathematical and Physical Sciences. Her research focuses on the structural, mechanical, and material properties of actin cytoskeletal networks in cellular functions. University: University of Sheffield Research Group: Materials and Biological Physics Group Contact: F23, Hicks Building, Sheffield, S3 7RH Her work employs advanced fluorescence microscopy and computational approaches to quantify actin cytoskeleton organization, combined with atomic force microscopy for biophysical cell surface mapping. Key research themes include: Mechanical regulation of cellular behavior Actin network dynamics in disease Super-resolution microscopy techniques Quantitative analysis of fibrous structures Biomechanics of early embryonic development Machine learning applications in cell imaging Recent publications highlight her expertise in 3D spectral imaging, molecular orientation microscopy, and quantitative SMLM data analysis. She also contributes to STEM equality and diversity initiatives. Her lab collaborates with interdisciplinary teams utilizing tools like: Agent-based modeling of molecular aggregation Cluster analysis of live-cell datasets Biophysical imaging resources (Nano-org)

• Physical chemistry
• Atmospheric sciences
• Astronomical sciences
• +۲ مورد دیگر

Prof Ingo Waldmann is a Professor of Astrophysics at the Department of Physics & Astronomy, University College London (UCL). His research focuses on exoplanetary atmospheres, atmospheric retrieval methods, and the application of machine learning in astrophysics. He is a Turing Fellow at the Alan Turing Institute and has ongoing appointments at UCL since 2018, including roles as Lecturer, Associate Professor, and Professor. His research interests include: Exoplanet atmosphere modeling Disequilibrium chemistry Multidisciplinary applications of machine learning JWST data analysis Spectroscopy of planetary systems Climate modeling of exoplanets Recent publications highlight his work on neural networks for exoplanet chemistry, JWST transmission spectra retrieval, surrogate radiative transfer models, and instrument design for the EXCITE telescope. His work spans instrumental development, data analysis, and theoretical modeling. Scientific contributions include: 2025 : CHEXANET for exoplanet chemistry 2024 : EXCITE telescope design, JWST retrieval algorithms 2023 : Detection of SO2 and NH3 in planetary atmospheres Scientific awards: Turing Fellow, Alan Turing Institute (2021–present)

• Machine Learning
• Signal and Image Processing
• Acoustic Signal Processing
• +۷ مورد دیگر

Ahmed Nait Chabane is a Professor and teacher-researcher at CESI Engineering School in Saint-Nazaire, France, where he serves as the educational manager of the first year of the integrated preparatory cycle. His academic home is within the School of Engineering, specifically focusing on Electronics and Signal Processing. Education: Doctor of Signal and Image Processing from UBO Brest / ENSTA Bretagne (2013), Thesis: "Grazing-invariant segmentation of side-scan sonar images using a competitive neural approach" Specialized Master's degree in Image Processing and Geographic Information Systems from USTHB University, Algiers, and Télécom Bretagne (2009) Electronics Engineer from University of Bejaia (2007) Professor Nait Chabane's research spans multiple domains including acoustic signal and image processing, machine learning algorithms, decision systems, and Industry 4.0/5.0 applications. His early work (2010-2015) focused on acoustic signal processing and classification algorithms, while his current research (2017-present) emphasizes decision algorithms, heterogeneous data analysis, knowledge extraction, and human-robot collaboration systems. His work bridges theoretical research with industrial applications, particularly in aerospace and manufacturing sectors. His publication record shows a clear evolution from sonar image processing toward broader applications of machine learning in industrial settings, with recent work heavily focused on human-robot collaboration, Industry 4.0 applications, and optimization algorithms for manufacturing processes. The research demonstrates increasing interdisciplinary collaboration with industry partners through CIFRE theses. Professor Nait Chabane actively supervises multiple PhD students through CIFRE partnerships with industry, including Guillaume Morin-Duponchelle (completed), Pierre Hemono, and Houssein Olleik (ongoing). His teaching portfolio includes Electronics, Electricity, Signal and Image Processing, Artificial Learning, Statistics, and Operational Research. His Engineering and Digital Tools Research Team focuses on practical applications of advanced algorithms in industrial contexts, with particular emphasis on solving real-world constraints in confined environments, resource optimization, and human-robot interaction challenges.

Alexander Perzyloمشاهده پروفایل

پژوهشگر

• Robotics
• Semantic Systems
• Industry 4.0
• +۷ مورد دیگر

Alexander Perzylo is a Researcher at fortiss , an affiliated institute of the Technical University of Munich (TUM) . He has been with fortiss since 2013 and previously worked as a Research Associate/Doctoral Student at TUM's Chair of Computer Science VI (Real-Time Systems and Robotics) from 2010 to 2013. He holds an M.Sc. and B.Sc. in Computer Science from TUM (2009 and 2005). Education B.Sc. in Computer Science, TUM (2005) M.Sc. in Computer Science, TUM (2009) His research focuses on robotics , semantic systems , Industry 4.0 , CAD analysis , and sustainability reporting ontologies . He specializes in knowledge representation, geometric constraints, and intuitive robot programming frameworks. Recent publications highlight trends in ontology-based CAD analysis , LLMs for geometric reasoning , semantic manufacturing processes , and knowledge-augmented socio-technical systems . His work bridges knowledge graphs , robotic assembly , and digital twins . Scientific Awards Best Cognitive Robotics Paper Award (2015) Perzylo has contributed to numerous EU H2020 , BMBF , and StMWi Bayern projects, including SMErobotics, RoboEarth, and DiProLeA. He has taught courses on Cognitive Robotics and Human-Robot Interaction at TUM since 2012.

Murat Kendirمشاهده پروفایل

پژوهشگر

• CityGML and 3D city models
• Geospatial data transformation
• Semantic data modeling
• +۴ مورد دیگر

Murat Kendir, M.Sc., is a researcher at the Chair of Geoinformatics at the Technical University of Munich (TUM), with over 5 years of involvement in teaching and research activities since 2018. His work focuses on geospatial data modeling, semantic transformations, and 3D city visualization. Technical University of Munich, Chair of Geoinformatics (Arcisstraße 21, 80333 Munich) His research spans CityGML standards , point cloud processing , and web GIS technologies , with a particular emphasis on integrating semantic 3D models with traffic simulation data for urban analysis. Recent publications highlight his contributions to dynamic 4D visualization frameworks. He has co-authored peer-reviewed publications in geoinformatics and participated in projects like Smart Sustainable Districts and CityGML Utility Network ADE . Contact: murat.kendir@tum.de

• Machine Learning
• Medical Data Analysis
• Quantum Computing
• +۳ مورد دیگر

Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.

• Emergency cartography
• Geographic data
• Digital photogrammetry
• +۷ مورد دیگر

Dr. Fabio Giulio Tonolo is an Associate Professor at the Politecnico di Torino , Department of Architecture and Design (DAD), specializing in geomatics and remote sensing for urban and environmental applications. His work bridges emergency cartography , UAV photogrammetry , and climate change monitoring through advanced geospatial techniques. Academic Roles : Teaching Satellite Remote Sensing and GIS and Advanced Geospatial Data Management at various cycles of the PhD in Urban and Regional Development Research Leadership : Scientific Director for projects like MOHYCAM (hydrogeological hazards) and EMERITUS (environmental crimes), with collaborations on glacier monitoring and cultural heritage preservation Scientific Affiliations : Secretary of AIT - Italian Remote Sensing Association (2019-2026), former President of IWG-SEM, and active member of ISPRS working groups Key Research Themes : Multi-source geospatial data integration, 3D modeling for heritage conservation, satellite-based climate impact analysis, and emergency mapping protocols His 15 most recent publications focus on remote sensing , glacier monitoring , and cultural heritage digitization , with methodologies spanning from UAV LiDAR to AI-based image segmentation . Dr. Tonolo has received ASITA Federation Best Poster Awards and contributes to editorial boards like ISPRS International Journal . He supervises PhD students in Urban and Regional Development, leads commercial projects such as 3D metric surveys in Assisi, and participates in international conferences including GISTAM and Gi4DM. His work aligns with SDG 11 (Sustainable Cities) and ERC PE10_14 (Earth Observations).

Dena Bazazianمشاهده پروفایل

مدرس

• Robotics
• Machine Vision
• Artificial Intelligence
• +۳ مورد دیگر

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).

• Educational Technology
• Learning Analytics
• Computer-Supported Collaborative Learning
• +۵ مورد دیگر

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.

Petra Isenbergمشاهده پروفایل

استاد پژوهشی

• Information Visualization
• Visual Analytics
• Human-Computer Interaction
• +۲ مورد دیگر

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.

• Computer Vision
• Neural Networks
• Pattern Recognition
• +۳ مورد دیگر

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.

Miroslav Minovicمشاهده پروفایل

استاد

• HCI
• Multimedia
• Computer Networks
• +۳ مورد دیگر

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

• Many-Objective Optimization
• Process Optimization
• Machine Learning Applications
• +۳ مورد دیگر

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.

Jordan Viceمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +۷ مورد دیگر

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.

• Heat Transfer Augmentation
• Fractal Geometry Applications
• Computational Fluid Dynamics
• +۳ مورد دیگر

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).