جست‌وجوی استاد و پژوهشگر

• Photography
• Media Studies
• Art History
• +۳ مورد دیگر

Yanai Toister is an Associate Professor (tenure track) specializing in Communication Sciences, focusing on interdisciplinary research at the intersection of photography, media theory, and visual culture. His work explores topics such as post-visual imagery, drone aesthetics, sensory perception, and the philosophical underpinnings of photographic systems. He has been actively publishing since 2007, with notable contributions in journals like PHILOSOPHY OF PHOTOGRAPHY and Journal of Comparative Literature and Aesthetics . Research Interests: Toister's research spans photography history , digital art , information aesthetics , and sensory studies . He critically examines how technological advancements reshape visual perception, particularly through drone imagery, algorithmic programming, and electromagnetic spectrums. Publications: His recent articles analyze the speculative camera, post-visual imagery in drone culture, and the interplay between echolocation and participatory frameworks. Earlier works include studies on Flusserian theory, the Turin Shroud, and speed in photographic practice. Awards & Activities: While no specific awards are listed, Toister is an active academic, organizing exhibitions, reviewing for journals, and delivering lectures globally (e.g., University of Wyoming, 2025). He also serves in academic committees and evaluates funding applications.

• Astronomy
• Astrophysics
• Machine Learning
• +۵ مورد دیگر

Robert Brunner serves as Professor of Astronomy at the University of Illinois at Urbana-Champaign, where he bridges astrophysical research with computational innovation. His work focuses on extracting knowledge from massive astronomical datasets through advanced statistical and machine learning techniques, while also extending methodologies to finance and agricultural applications. Research interests center on developing machine learning algorithms (random forests, deep neural networks, Bayesian estimation) for astronomical data analysis, cosmological parameter constraints via n-point clustering measurements, and hardware acceleration using GPUs/cloud systems. His interdisciplinary approach spans source classification, transient phenomena detection in surveys like SDSS and DES, and applications in financial time-series analysis and agricultural remote sensing. Recent publications (2019-2025) reveal strong cross-domain expertise: astronomical catalogs for Rubin Observatory and Spitzer surveys coexist with financial market analysis using community detection methods and agricultural computer vision systems. Key methodological threads include spatio-temporal forecasting, multimodal learning for earnings calls, and anomaly detection via extended isolation forests, demonstrating consistent innovation in handling petascale datasets across scientific boundaries.

Kerstin Bunteمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• XAI (Explainable Artificial Intelligence)
• +۷ مورد دیگر

Kerstin Bunte is a Professor of Machine Learning for interdisciplinary data analysis at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Bernoulli Institute's Intelligent Systems Group. She holds an Honorary Fellowship at the University of Birmingham and leads the Intelligent Systems Group. Her research focuses on interpretable machine learning, interdisciplinary applications (e.g., astrophysics and biomedical data), and visualization techniques. Research Interests: - Machine Learning - Artificial Intelligence - Explainable AI (XAI) - Interpretable Models - Dimensionality Reduction - Data Visualization - Astrophysical Data Analysis - Medical Imaging Awards & Grants: - DSSC XS funding (2023) - NWO VIDI grant (2020) - Rosalind Franklin Fellowship (2016–present) Advising & Students: Supervised PhD students include Elisa Oostwal, Janis Norden, Matteo Marcantoni, and Petra Awad. Research spans topics like tumor segmentation in medical imaging, astrophysical structure detection, and autonomous navigation systems. Labs & Collaborations: Leads the Intelligent Systems Group, collaborating with institutions like the University of Birmingham and the University of Warwick. Work involves interdisciplinary projects combining machine learning with astronomy, biomedical sciences, and robotics.

Oliver Eberleمشاهده پروفایل

پژوهشگر

• Explainable AI
• Natural Language Processing
• Digital Humanities
• +۳ مورد دیگر

Oliver Eberle is a Researcher at the Machine Learning Group within the Technical University of Berlin (TU Berlin), affiliated with BIFOLD – Berlin Institute for the Foundations of Learning and Data. He holds a Joint M.Sc. in Computational Neuroscience (2017) and a Ph.D. in Machine Learning (2022) from TU Berlin and Humboldt-Universität zu Berlin. His research focuses on Explainable AI (XAI), Natural Language Processing (NLP), and their applications in Digital Humanities and Cognitive Science. Notable projects include XAI methods for transformer architectures, historical corpus analysis, and collaborations with the Max Planck Institute for the History of Science on the Sphaera Corpus. Key contributions include developing frameworks like BiLRP for similarity models, MambaLRP for sequence models, and xMIL for medical imaging. His work bridges technical advancements in AI with interdisciplinary applications in humanities and science.

• Computer Vision
• Medical Image Analysis
• Pattern Recognition
• +۵ مورد دیگر

Jaime S. Cardoso is a prominent researcher at the University of Porto and Institute for Systems and Computer Engineering, Technology and Science (INESC TEC) in Portugal. His extensive publication record spanning two decades demonstrates his leadership in computer vision, medical image analysis, and pattern recognition. His research primarily focuses on applying artificial intelligence to healthcare challenges, particularly in medical imaging and diagnostics. Cardoso's research interests center on explainable AI for medical applications, biometrics, and computer vision. His work bridges the gap between theoretical machine learning and practical medical solutions, with significant contributions to breast cancer diagnosis, medical image segmentation, and biometric security systems. He has developed innovative approaches to medical image analysis, including virtual staining techniques and privacy-preserving explanation methods for medical AI systems. His recent publications (2023-2025) reveal a strong emphasis on explainable AI in medical contexts, with multiple papers addressing how to make deep learning models more transparent and trustworthy for healthcare applications. He has also made significant contributions to face recognition technology, video anomaly detection, and specialized medical imaging techniques for breast cancer and neonatal EEG analysis. Among his scientific contributions are numerous collaborations with researchers across Portugal and internationally. His work has appeared in top-tier journals including IEEE Access, Medical Image Analysis, and Neurocomputing, reflecting the high impact of his research. Cardoso has supervised numerous students who have become established researchers in their own right, including Ricardo P. M. Cruz, Kelwin Fernandes, and Ana Filipa Sequeira. His research group appears to focus on the intersection of deep learning, medical imaging, and biometrics, with strong connections to clinical applications.

Marc Chaumontمشاهده پروفایل

دانشیار

• Visual Data Analysis
• Remote Sensing
• Steganography
• +۱۰ مورد دیگر

Marc Chaumont is an Associate Professor at the University of Nîmes since 2005 and a senior researcher at LIRMM Montpellier. He holds an HDR (Habilitation à Diriger des Recherches) and is an IEEE Senior Member. Since October 2024, he has been an associate collaborator at IRISA laboratory in Vannes, France. His career spans academic research, teaching, and interdisciplinary collaborations with institutions like MARBEC, CIRAD, and CNRS. PhD in Computer Science from IRISA Rennes (2003) Engineer Diploma from INSA Rennes (1999) His research focuses on visual data analysis , remote sensing , and digital forensics , particularly in steganography and steganalysis . He has contributed to creating large-scale databases like LSSD , a 2 million JPEG image dataset for deep-learning steganalysis. His work extends to AI applications in marine ecology, medical imaging, and poverty estimation from satellite data. Recent publications highlight collaborations across disciplines, including transformer models for socioeconomic indicator prediction, 3D fish tracking in coral reefs, and self-supervised encoder pretraining for chronic wound segmentation. He has supervised interns like Ayman El Mannouy (2024) and Abir Zahi (2023) in projects related to weakly supervised segmentation and marine biodiversity. IEEE Senior Member (2020) Top-3% Best Reviewer at IEEE ICIP 2020 Marc has taught courses ranging from signal processing to image compression since 1999. His technical contributions include software tools for image databases and steganalysis algorithms. He actively participates in European projects like DFUC'2024 and ConvEntion for astronomical data classification.

• Geoinformatics
• Computer Graphics
• Computer Vision
• +۵ مورد دیگر

Prof. Dr. rer. nat. habil. Stephan Kopf is a faculty member at the Faculty of Spatial Information, where he holds the Professorship in Informatics and Geoinformatics. He actively contributes to research in geographic information systems (GIS), computer graphics, computer vision, and multimedia technologies. His academic leadership roles include serving as Dean and as a member of the Faculty Council, with advisory roles in the Senate. His research spans crowd simulation , image/video retargeting , augmented reality platforms , and interactive learning systems . He focuses on algorithm development for GIS, temporal effects in re-captured video, and scalable solutions for classroom interactivity. His work frequently integrates machine learning, mobile computing, and geospatial data. For teaching, he oversees courses such as Internet Technologies, Informatics, and Programming in degree programs like Geoinformatics/Management and Surveying. He supervises theses involving programming-centric topics like mobile app development , VR applications , and astronomical analysis of geospatial data .

Rahul Mouryaمشاهده پروفایل

مدرس

• Machine Learning
• Computer Vision
• Inverse Problems
• +۶ مورد دیگر

Rahul Mourya is a Lecturer in Computer Science at the Faculty of Science and Engineering , University of Wolverhampton, UK. He joined in October 2023 and is affiliated with the Digital Innovations and Solution Centre (DISC) , which focuses on fundamental and applied research with societal and economic impact. PhD & MSc in Computer Science (Signal/Image/Vision) from Université Jean Monnet Saint-Etienne, France BEng in Electronics & Telecommunications from University of Pune, India His research develops foundational tools for machine learning, computer vision, inverse problems , and signal/image processing , with applications in autonomous systems, robotics, and sensor networks . Teaching includes computational mathematics, robotics engineering, and deep learning modules. Collaborations span institutions like Heriot-Watt University, Telecom ParisTech, and interdisciplinary projects in underwater acoustics and astronomical imaging. Current research explores measurement-consistent neural networks and optimization algorithms for inverse problems.

• Surface Metrology
• Additive Manufacturing
• Precision Engineering
• +۴ مورد دیگر

Professor Jane Jiang is a distinguished academic at the University of Huddersfield , affiliated with the School of Computing and Engineering and the Department of Engineering . She specializes in surface metrology, precision engineering, and advanced manufacturing technologies. Her work focuses on optimizing measurement techniques for additive manufacturing, X-ray computed tomography, and optical systems. Research Interests Her research spans surface texture analysis , 3D vision systems , and metrology for smart manufacturing . She develops novel methods for characterizing complex surfaces using techniques like phase measuring deflectometry and chromatic confocal sensors. Her contributions bridge engineering and materials science, emphasizing practical applications in aerospace, biomedical, and industrial sectors. Recent Contributions Her 2025 work includes advancements in freeform optics design, vibration-resistant microscopy, and neural network-based surface characterization. Her studies on XCT measurement for additive manufacturing parts address critical challenges in precision and data analysis. Affiliations & Projects She leads the Centre for Precision Technologies and actively collaborates on EU-funded projects. She organized the 1st International Conference on Metrology and Standard (2024) and contributes to interdisciplinary initiatives in bio-engineering and advanced materials. Grants & Teams Her team includes researchers like Shan Lou and Paul Scott , focusing on metrology for medical implants and aerospace components. She supervises PhD students exploring AI-driven surface specification and manufacturing process optimization.

• Computer Vision
• Machine Learning
• Document Analysis
• +۵ مورد دیگر

Ujjwal Bhattacharya is affiliated with the Indian Statistical Institute, India. His primary research focuses on computer vision, machine learning, and document analysis with a strong emphasis on multimodal perception systems and deep learning applications. He has published extensively in top-tier venues like ICPR, ICDAR, CVPR, and BMVC, contributing to advancements in autonomous driving, image processing, and privacy-aware machine learning. His work spans from developing robust pedestrian detection systems using multimodal sensors to enhancing degraded document image processing through domain adaptation and advanced neural architectures. Recent contributions include semi-supervised 3D object detection frameworks and privacy-preserving clustering techniques. Key research areas include: Multimodal sensor fusion for autonomous systems Deep learning for document analysis and OCR Privacy-aware metric learning Efficient neural network compression techniques Image enhancement and restoration His publication trends reflect a focus on solving real-world challenges in autonomous driving, degraded document processing, and privacy-sensitive machine learning applications.

Naonori Uedaمشاهده پروفایل

استاد پژوهشی

• Machine Learning
• Artificial Intelligence
• Physics-Informed Deep Learning
• +۵ مورد دیگر

Naonori Ueda is a Research Professor and Deputy Director at RIKEN Center for Advanced Intelligence Project. He also serves as a Visiting Fellow at NTT Communication Science Laboratories, Research Supervisor for Mathematical Information Platform at Japan Science and Technology Agency (JST), and Visiting Professor at Kobe University's Graduate School of System Informatics. His distinguished career spans academia, government research institutions, and industry collaboration, with significant contributions to advancing artificial intelligence and machine learning applications across multiple scientific domains. Dr. Ueda's research interests focus on the intersection of machine learning, artificial intelligence, and physical sciences. He specializes in physics-informed deep learning approaches that integrate governing physical equations with neural network architectures. His work spans geophysical data analysis, remote sensing applications, computational seismology, and environmental monitoring systems. He has pioneered methods for crustal deformation modeling, earthquake prediction, tsunami inundation forecasting, and satellite imagery analysis using advanced machine learning techniques. His research demonstrates how AI can solve complex scientific problems by bridging the gap between data-driven approaches and physical domain knowledge. His publication record reveals a strong trend toward applying machine learning to solve real-world geophysical and environmental challenges. His recent work shows increasing sophistication in physics-informed neural networks that incorporate domain-specific knowledge into deep learning architectures. The publications span high-impact journals like Nature Communications, demonstrating the interdisciplinary significance of his work. His research consistently focuses on practical applications of AI for disaster prevention, environmental monitoring, and scientific discovery. Fellow of IEICE (Institute of Electronics Information and Communication Engineers) Member of Japan Prize field review committee Selection Committee Member for Brilliant Female Research Award (The Jun Ashida Award) Member of Kyoto Prize Selection Committee Dr. Ueda has secured substantial research funding through multiple government-sponsored projects including RIKEN Pioneering Project 'Prediction Science,' JST AIP Acceleration Research projects on weather prediction and drug discovery, and AMED-funded medical research initiatives. His leadership extends to serving as Sub-project Director for Japan's Moonshot R&D Project. He actively mentors researchers through his roles at RIKEN, NTT, and various academic institutions, fostering the next generation of AI scientists. As Deputy Director of RIKEN Center for Advanced Intelligence Project, Dr. Ueda leads one of Japan's premier AI research initiatives. He also serves on the Advisory Board of Kobe University's Mathematical and Data Science Center and Kyoto University's Graduate School of Informatics. His leadership extends to coordinating the AI Seminar at Osaka Industrial Association and supervising the Keihanna 'Edison Society' at the International Institute for Advanced Studies, demonstrating his commitment to bridging academic research with industrial applications.

Peter Moarمشاهده پروفایل

استاد مدعو

• Aerospace Engineering
• Astronomical and Space Instrumentation
• Automation and Control Engineering
• +۴ مورد دیگر

Peter Moar is an Adjunct Professor in Engineering at La Trobe University with over 25 years of international experience in technology design, research, and consulting across telecommunications, automotive, aerospace, and nanotechnology sectors. His career spans from establishing global startups to optimizing operations for Fortune 500 companies, with a focus on bringing complex products from design to mass manufacture. His educational background includes: Bachelor of Engineering (Honors) PhD from La Trobe University Moar possesses extensive expertise in Aerospace Engineering , Astronomical and Space Instrumentation , Automation and Control Engineering , and Photonics and Electro-Optical Engineering . His research focuses on Industrie 4.0 applications in automotive, aerospace, and telecommunications sectors, with a notable 17-year collaboration with the German Aerospace Centre (DLR) on the FIREBIRD micro satellite missions for global bushfire monitoring. His recent publications demonstrate a shift from foundational optical fiber research to cutting-edge space applications, particularly in machine learning for orbital deployment systems and Earth observation technologies. Professional leadership includes: Chair of Engineers Australia National Committee for Space Engineering (since January 2023) Co-chair of same committee (October 2017 - January 2023) Moar has developed industry-focused educational programs including 'Work Integrated Learning (Engineering)' (March 2016) and secured mission-critical capabilities for the DLR's Earth Sensing Imaging Spectrometer (DESIS) global space instrumentation mission. His funded research includes the 'Heavy Vehicle Turntable Use on Building Construction Sites' project with The Australian Turntable Co. Pty Ltd (2019-2020), demonstrating his strong industry connections and practical application focus.

Jonathan Paxmanمشاهده پروفایل

استاد

• Space Systems Engineering
• Meteor Tracking and Analysis
• Planetary Crater Detection Algorithms
• +۴ مورد دیگر

Professor Jonathan Paxman is a faculty member in the School of Civil and Mechanical Engineering at Curtin University, affiliated with the Faculty of Science and Engineering and the Office of the Provost. He holds a PhD (Cantab.) and is a Fellow of the Institute of Engineers Australia (FIEAust) and the Society for Higher Education in Australia (SFHEA). His research focuses on space systems engineering, meteor detection, planetary crater analysis, assistive technologies, and autonomous robotics control. Education: PhD in Engineering from the University of Cambridge (Cantab.), MPhil, and professional certifications in engineering and higher education. Teaching: Courses include Microcontroller Project and Linear Systems and Control . His research innovations include the Desert Fireball Network (DFN), a continental-scale meteor tracking system, and the Fireballs in the Sky citizen science app. He has pioneered automatic crater detection algorithms for Mars surface dating and developed control systems for autonomous spacecraft and robots. Key awards include the 2021 Research Team of the Year (Binar Space Program), 2016 Eureka Prize for Innovation in Citizen Science, and multiple teaching excellence citations. His work bridges academia and industry through projects like the Binar lunar mission series and assistive technologies for disability support. Grants and collaborations: Extensive funding for space exploration and planetary science projects. His team’s work has led to meteorite recoveries (e.g., Murrili) and contributed to Mars surface age mapping. Active in STEM outreach and curriculum innovation, including transforming pedagogy in science and engineering education. Labs/Teams: Leads the Desert Fireball Network and collaborates with NASA, ESA, and industry partners on space systems and planetary research initiatives.

Piero Fraternaliمشاهده پروفایل

استاد

• Web Engineering
• Model-Driven Development
• Human Computation
• +۳ مورد دیگر

Piero Fraternali is Full Professor of Web Technologies at Politecnico di Milano, where he leads research on software engineering methodologies for web applications and environmental intelligence systems. His work spans model-driven development, human computation, and AI-powered computer vision. He co-founded WebRatio (commercializing model-driven web development tools) and Servitly (focused on social business process management). His PeakLens app has over 2 million users. Current research explores environmental crime detection through geospatial AI and waste monitoring via satellite imagery. Earlier contributions include the OMG-standardized Interaction Flow Modeling Language (IFML) for application design. He has led several EU projects including PERIVALLON (combating environmental crime) and enCOMPASS (behavioral energy saving). His group develops deep learning methods for gravitational lens analysis and industrial predictive maintenance.

Patrizio Campisiمشاهده پروفایل

استاد

• Digital Signal Processing
• Image Processing
• Biometrics
• +۱۲ مورد دیگر

Patrizio Campisi is a Full Professor in the Section of Applied Electronics at the Department of Engineering, Roma TRE University, Rome, Italy. He leads cutting-edge research in digital signal and image processing with applications to secure multimedia communications and biometrics. He has held visiting positions at the University of Toronto, Beckman Institute (UIUC), and École Polytechnique de Nantes, and has been a Marie Curie Fellow (2010–2014). His educational background includes a Laurea (summa cum laude) in Electronic Engineering from Sapienza University of Rome and a Ph.D. in Electrical Engineering from Roma TRE University. His research focuses on secure biometric recognition (signature, keystroke, EEG, vein), digital watermarking, blind image deconvolution, HDR imaging, and privacy-preserving technologies. He has contributed significantly to template protection, multimodal biometrics, and forensic image analysis. His work bridges engineering, computer science, and security, with strong applications in mobile authentication, border control, and social media safety. The recent publications reflect a strong trend in biometrics, image forensics, and privacy-enhancing technologies, with a focus on real-world applications in mobile systems, healthcare, and law enforcement. Key themes include secure authentication, de-identification, and computational imaging. IEEE Second International Conference on Biometric Systems 2008 Best Student Paper Award IEEE Biometric Symposium 2007 Best Paper Award IEEE International Conference on Image Processing 2006 Best Student Paper Award Marie Curie Fellow (2010–2014) NATO-CNR Advanced Fellowship (2003) IEEE Senior Member Prof. Campisi has supervised numerous students and leads the BioMedia4n6 lab. He has secured major EU grants including H2020 projects AMBER, COSMOS, and ENCASE. His editorial leadership includes serving as Editor-in-Chief of IEEE Transactions on Information Forensics and Security (2018–2020) and Chair of the IEEE Information Forensics and Security Technical Committee (2017–2018). He is actively involved in research networks such as COST Actions on de-identification and biometrics-forensics integration, and has contributed to EU policy via the BEST Thematic Network on biometrics and fundamental rights. His lab, BioMedia4n6, focuses on biometrics, multimedia forensics, and privacy-aware systems.