جست‌وجوی استاد و پژوهشگر

Qianni Zhangمشاهده پروفایل

مدرس ارشد

• Machine learning
• Computer vision
• Medical image computing
• +۲ مورد دیگر

Qianni Zhang is a Senior Lecturer and PhD/PDRA Engagement Lead at the Centre for Multimodal AI within the School of Electronic Engineering and Computer Science at Queen Mary University of London. She leads research initiatives at the intersection of artificial intelligence and healthcare applications, with a particular focus on medical image analysis. Dr. Zhang's research interests center on machine learning and computer vision with specific applications in medical image computing . Her work spans multiple domains including medical imaging analysis for cardiology, ophthalmology, and oncology. She has developed numerous deep learning approaches for image segmentation, registration, and analysis, particularly focused on intravascular ultrasound, optical coherence tomography, and MRI applications. Her research bridges theoretical computer vision with practical clinical needs, creating tools that have potential to improve diagnostic accuracy and treatment planning in healthcare settings. Her recent publications demonstrate a clear trajectory toward increasingly sophisticated multimodal AI approaches for medical imaging. The trend shows progression from basic image segmentation techniques to complex fusion networks that combine multiple imaging modalities and incorporate anatomical knowledge. Many of her papers focus on cardiovascular applications, particularly intravascular ultrasound analysis, while others address ophthalmological and general medical imaging challenges. The consistent theme across her work is the application of cutting-edge deep learning techniques to solve real-world medical imaging problems with clinical relevance. ARCNET (Barts and the London Charity, £15,496, 2025-2026) Deep Learning based Multispectral Image Analysis for Retinal Lesion Detection (Royal Society, £12,000, 2022-2025) Anatomical And Functional Analysis Of The Soft Palate (Barts Health NHS, £132,620, 2022-2025) Machine learning for improved graphics in sport broadcast (Innovate UK, £214,061, 2022-2024) Dr. Zhang supervises a diverse group of PhD students working on various aspects of medical image computing and multimodal AI. Her students' projects span multiple healthcare domains including cardiovascular imaging, ophthalmology, dentistry, and general medical diagnostics. She has established herself as a leading researcher in applying deep learning to medical imaging challenges, with particular expertise in segmentation, registration, and multimodal fusion techniques for clinical applications.

• Coastal engineering
• Floating offshore wind
• Ocean waves
• +۳ مورد دیگر

Vincenzo Nava is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Environmental, Land and Infrastructure Engineering (DIATI). His work focuses on coastal engineering , floating offshore wind , and ocean wave dynamics. Coastal engineering Offshore renewable energy Structural health monitoring Teaching roles include: Course Instructor for Coastal Engineering (2025/26) in Environmental and Land Management Engineering Lecturer in Technical Hydraulics for Building Engineering Recent research explores machine learning applications for concrete strength estimation, variational deep learning for offshore damage detection, and Bayesian optimization in mooring systems. Publications span journals like Wind Energy Science , Ocean Engineering , and Renewable Energy . Scientific recognition includes: Open badge for public science communication (2025) Collaborations include researchers from marine energy projects, with expertise in CFD simulations , neural networks , and mooring optimization .

Brett Byramمشاهده پروفایل

دانشیار

• Medical Ultrasound
• Signal Processing
• Elasticity Imaging
• +۴ مورد دیگر

Brett Byram serves as Associate Professor of Biomedical Engineering at Vanderbilt University's School of Engineering and holds the Hoy Family Faculty Fellow distinction. He maintains dual affiliations with the Vanderbilt Institute for Surgery and Engineering (VISE) and the Vanderbilt University Institute of Imaging Science (VUIIS). Currently, he is the incoming Faculty Director of ACCRE—Vanderbilt’s High Performance Compute Center—and incoming chair for the NIH’s Image Technology Development (ITD) Panel, where he has served as a standing member. Educational Background: Ph.D. in Biomedical Engineering, Duke University (2011) B.S. in Biomedical Engineering and Mathematics, Vanderbilt University Dr. Byram's research centers on medical ultrasound with emphases in signal processing, elasticity imaging, and beamforming. His work spans beamforming/image formation, motion estimation, blood flow imaging, and hardware development—frequently integrating deep learning. The BEAM Laboratory, which he founded in 2013, develops ultrasound solutions for clinical challenges including transcranial imaging, placenta analysis, and kidney stone characterization. His methodology bridges advanced signal processing with tangible medical applications, particularly through AI-enhanced imaging techniques. Recent publications (2024-2025) reveal three dominant trends: deep learning integration for image reconstruction/stitching (e.g., SynStitch, LOTUS), clinical metric refinement (gCNR for lesion detectability), and specialized applications in obstetrics (placenta segmentation) and neuroimaging (transcranial ultrasound). These works consistently address data scarcity through synthetic training and domain adaptation while advancing quantitative assessment frameworks. Scientific Awards: Hoy Family Faculty Fellow Dr. Byram leads the BEAM Laboratory (Biomedical Elasticity and Acoustic Measurement), established in 2013 as part of Vanderbilt's Biomedical Engineering Department with VISE affiliation. His NIH ITD Panel leadership and ACCRE directorship reflect significant institutional trust. While specific grants aren't detailed, his 11+ years of continuous lab operation indicate sustained funding for projects spanning Siemens Healthcare collaborations, transcranial imaging development, and deep learning beamformer research—often translating academic innovations to clinical tools. The BEAM Lab functions as Vanderbilt's nexus for ultrasound innovation, maintaining strong VISE/VUIIS ties while pursuing hardware/software co-design. Current initiatives include real-time deep learning beamformers for echocardiography, transcranial functional ultrasound validation, and placenta segmentation systems. The lab's industry partnerships (notably with Siemens) and NIH panel involvement position it at the forefront of translating ultrasound research into clinical practice, with emerging work on diffusion models for 3D imaging and standardized biomechanical phantoms.

• Algorithms
• Theory & Logic
• Programming Languages & Verification
• +۴ مورد دیگر

Ali Zarezade is a researcher at the Max Planck Institute for Software Systems (MPI-SWS), focusing on interdisciplinary research spanning algorithms, machine learning, and social computing. His work integrates theoretical foundations with practical applications in cyber-physical systems, distributed networks, and human-centric AI. Key research areas include human-in-the-loop machine learning, optimal control strategies for social networks, and spatio-temporal modeling of social media activity. His methods often combine stochastic control theory, sparse regression techniques, and online algorithm design to address challenges in education technology, security, and information diffusion. Prior contributions involve developing algorithms like RedQueen and Cheshire for optimizing social network activity, probabilistic models for hyperspectral unmixing, and visual tracking systems resilient to occlusions. His research bridges computational theory with real-world systems, emphasizing scalable solutions for distributed and networked environments.

Graeme Mansonمشاهده پروفایل

مدرس ارشد

• Structural Health Monitoring (SHM)
• Damage Identification
• Uncertainty Analysis
• +۶ مورد دیگر

Dr Graeme Manson is a Senior Lecturer in the Department of Mechanical Engineering at the University of Sheffield, where he also serves as Course Director for all MSc courses and Final Year BEng/MEng Tutor and Project Co-ordinator. He holds a BEng(Hons) from Heriot-Watt University (1992) and a PhD in Mechanical Engineering from the University of Manchester (1996). His research focuses on structural health monitoring (SHM), including damage identification strategies, uncertainty analysis, hybrid optimization, and nonlinear system dynamics. Notably, he received an EPSRC Advanced Research Fellowship (2002–2007). Research interests span advanced signal processing, robust feature selection, and machine learning applications in SHM. Key projects include developing algorithms for fatigue crack detection in aircraft structures, uncertainty propagation in nonlinear systems, and optimizing sensor networks using genetic algorithms. Manson has contributed to EPSRC-funded initiatives such as 'Uncertainty Propagation in Structures' (2006–2011) and 'Smart Sensing for Structural Health Monitoring' (funded by EPSRC). His teaching includes MEC319 (Sensors, Actuators and Controllers) and MEC415 (Condition Monitoring). Awards include the EPSRC Advanced Research Fellowship, and his work has been published widely in journals like Structural Health Monitoring , Journal of Sound and Vibration , and Materials . Manson collaborates across disciplines, integrating machine learning and statistical methods to enhance SHM reliability under operational and environmental variability.

Federico Nanniمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Information Retrieval
• Digital Humanities
• +۳ مورد دیگر

Dr. Federico Nanni is a Researcher at the University of Mannheim's Data and Web Science Group. His work focuses on applying Natural Language Processing (NLP) and Information Retrieval (IR) techniques to Digital Humanities and Computational Social Science, particularly in analyzing political communication, historical texts, and interdisciplinary research. His research spans topics such as event modeling in historical documents, election intervention analysis, and computational methods for evaluating interdisciplinarity in academic contexts. His research interests include political text scaling, computational semantics, and the application of NLP to historical studies. He has contributed to projects such as event-based access to war memoirs and methodologies for reconstructing digital archives. His work bridges computational methods with social and historical inquiry, emphasizing interdisciplinary approaches. Dr. Nanni has published extensively on topics ranging from political communication during the German refugee crisis to computational analysis of university histories. His articles reflect a consistent focus on leveraging computational tools to address complex societal and historical questions. Despite no listed awards, his contributions to digital humanities and computational social science are well-recognized within his field. He is affiliated with the Data and Web Science Group at the University of Mannheim, where he collaborates on projects that integrate NLP and IR with humanities and social science research. No information is explicitly provided regarding student advising or grants, though his research collaborations imply active engagement in academic networks.

• Construction Robotics
• Automation Engineering
• Machine Learning
• +۳ مورد دیگر

Antti Kolu is a Doctoral Researcher in the Faculty of Engineering and Natural Sciences, focusing on automation and robotics in construction machinery. He holds a Master of Science (Technology) in Automation Engineering (2010). His research integrates machine learning, sensor data, and BIM technology to enhance productivity and safety in construction and railway maintenance operations. Key interests include excavator cycle analysis, real-time terrain mapping, and semi-autonomous machinery control. Education: Master of Science (Technology), Automation Engineering (2010) Research emphasizes practical applications of robotics in construction, such as improving excavator productivity through supervised learning and inertial measurement units (IMUs). Collaborations span international projects on railway inspection tools and autonomous bulldozer systems. He received the Best Paper Award for Robotics Application in 2016 for work on vision-guided autonomous forklifts. Active in media engagement, he discussed advancements in augmented reality applications in 2013. Grants/Awards: Best Paper Award (2016) Leverages interdisciplinary approaches combining mechanical engineering, data science, and BIM to address real-world challenges in heavy machinery automation.

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۴ مورد دیگر

Fabrizio Palumbo is an Associate Professor at the Department of Information and Communication Technology, University of Agder (UiA). His research spans artificial intelligence, machine learning, energy systems optimization, and computational neuroscience, with notable contributions to text classification, cybersecurity protocols, and zebrafish behavioral studies. He actively publishes in interdisciplinary venues including AI conferences and neuroscience journals. Key research areas include developing efficient neural network architectures for text analysis, exploring multilingual BERT models, optimizing energy storage systems via neural networks, and investigating neural mechanisms in zebrafish using behavioral protocols. His work bridges theoretical advancements with applied contexts, such as AI in investigative journalism and cybersecurity applications. Palumbo collaborates across disciplines, contributing to both technical and applied research. Notable recent publications address AI project methodologies in journalism and energy system optimization strategies.

Dhananjay Tomarمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Histopathology
• Domain Generalization
• +۳ مورد دیگر

Dhananjay Tomar is a Doctoral Research Fellow at the University of Oslo , affiliated with the Faculty of Mathematics and Natural Sciences . He is part of the Research Group for Digital Signal Processing and Image Analysis (DSB) , focusing on Deep Learning applications in histopathology , including domain generalization, giga-pixel image processing, and semi-supervised learning. His work also integrates software development for practical implementations. Research Interests: Domain Adaptation in Medical Imaging High-resolution Image Analysis Efficient Deep Learning Models Publications highlight contributions to synthetic data generation for training datasets and feature selection via autoencoders. No scientific awards are explicitly listed. His current role involves no formal advisees, and grants or funding details are not disclosed. He collaborates within the Digital Signal Processing and Image Analysis research group, contributing to interdisciplinary projects at the intersection of computer science and biomedical engineering.

• Computer Vision
• Machine Learning
• Video Surveillance
• +۷ مورد دیگر

Professor Tao Xiang holds the position of Professor of Computer Vision and Machine Learning at the University of Surrey, where he is also a Distinguished Chair. He leads the Body Behaviour Group at Samsung AI Centre, Cambridge. His research focuses on computer vision, including video surveillance, activity analysis, and sketch-based recognition, alongside machine learning domains like zero-shot learning and domain generalization. He has published over 160 papers (h-index 60) and secured £3.9M+ in grants. Key affiliations include the Faculty of Engineering and Physical Sciences and the Centre for Vision, Speech and Signal Processing (CVSSP). His work spans generative models for sketches, 3D shape retrieval from VR sketches, and virtual try-on systems. Notable contributions include the FS-COCO dataset for scene sketches and advancements in temporal action detection via vision-language prompting. Research interests emphasize cross-modal tasks (e.g., SBIR/FG-SBIR), domain adaptation, and transformer-based vision models. Collaborations with industry (Samsung AI) and foundational contributions to datasets (e.g., VR sketch collections) underscore his interdisciplinary impact. Future work includes creative object generation (PartCraft), explainable AI for sketches, and scalable generative models.

• Artificial Intelligence
• Explainable AI
• Recurrent Neural Networks
• +۵ مورد دیگر

Isel Grau Garcia is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Information Systems group within the Department of Industrial Engineering and Innovation Sciences. Her research focuses on explainable AI, recurrent neural networks, time-series analysis, and bias mitigation in healthcare and business applications. She holds a Ph.D. in Computer Science from Vrije Universiteit Brussel (VUB) and has co-authored over 75 peer-reviewed publications. Key roles include co-promotor for three PhD students and involvement in the ENFIELD project on trustworthy AI. Education: Ph.D. in Artificial Intelligence, Vrije Universiteit Brussel (2020) M.Sc. in Artificial Intelligence, Central University of Las Villas (2014) B.Sc. in Computer Science, Central University of Las Villas (2011) Research Interests: Her work emphasizes interpretability of machine learning models, semi-supervised classification, and interdisciplinary applications in healthcare. Recent projects include developing Fuzzy Cognitive Map models without training data and quantifying trust in clinical decision support systems. Key Contributions: Her articles explore topics like bias detection via SHAP and FCMs, sparsity-optimized feature importance, and recurrent neural networks for temporal dynamics. These contributions align with UN SDG goals for innovation and health. Awards: Best Demo Award at BNAIC/BENELearn 2021 Industria Students Award – Best Talent (2024) Advising & Grants: Supervises three PhD students and collaborates on the ENFIELD project (€ funding). Active in organizing workshops and reviewing for top venues like ECAI and IEEE Transactions on Fuzzy Systems. Labs/Teams: Engaged in interdisciplinary collaborations with institutions like Warsaw University of Technology and the University of Lisbon, focusing on AI ethics and trustworthy decision systems.

• Remote Sensing
• Machine Learning
• Environmental Science
• +۳ مورد دیگر

Mingzhe Jiang is an Adjunct Assistant Professor at the University of Waterloo, focusing on remote sensing and machine learning applications in environmental science. His work emphasizes sea ice classification, SAR imagery analysis, and deep learning models for geospatial data. He is affiliated with the Adjunct Faculty group at the university. Research interests include developing advanced algorithms for sea ice dynamics, uncertainty quantification in climate models, and automated environmental monitoring systems. His contributions span Bayesian neural networks, graph convolutional networks, and semi-supervised learning frameworks for polar and maritime applications. Key publications (2016–2025) highlight innovations in SAR data processing, including hierarchical pipelines for sea ice segmentation, dual-polarized imagery analysis, and unsupervised clustering techniques. His work often integrates satellite data from platforms like RADARSAT-2 with machine learning to improve environmental parameter estimation. No scientific awards or grants are explicitly listed in the provided information. He has not reported advisees or laboratory affiliations in the text.

Liam Burrowsمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Deep Learning
• Machine Learning
• +۳ مورد دیگر

Liam Burrows is a Researcher in the Department of Mechanical Engineering at the University of Bath. His work focuses on medical imaging and deep learning applications in pathology, particularly in tumor stroma analysis and meningioma segmentation. He holds a PhD in Mathematics from the University of Liverpool (2022), specializing in variational and deep learning methods for selective segmentation. His research addresses UN Sustainable Development Goals related to education and health. Education: Doctor of Philosophy (Mathematics) from University of Liverpool, specializing in 'Variational and Deep Learning Methods for Selective Segmentation' awarded June 2022. Research interests include combining mathematical modeling with deep learning to automate assessment of immunohistochemical stains and develop semi-automatic segmentation techniques for medical diagnoses. His recent work emphasizes histopathology image analysis and weakly supervised learning approaches. Publications (2022–2024) demonstrate expertise in variational models, hybrid pipelines, and automated lesion segmentation. No scientific awards explicitly listed but maintains active collaborations in medical imaging research. Engagement includes public talks like CMIT Summer Research Internship (June 2024) and NHSX Public Patient Information Group AI Talk (May-Nov 2024). No formal student advisement or grant details provided in available texts.

Yu Sunمشاهده پروفایل

استاد

• Multimedia Computing
• Video Compression
• Image Processing
• +۳ مورد دیگر

Yu Sun is a Professor in the Department of Computer Science and Engineering at the University of Central Arkansas (UCA). He holds a Ph.D. in Computer Science & Engineering from the University of Texas at Arlington. His research focuses on Multimedia Computing, Video Compression and Communication, Image Processing, and Wireless Videos. His work emphasizes optimizing video coding standards like HEVC, AVS2, and SHVC, with contributions to algorithms for rate control, intra prediction, and scalable video techniques. Research interests include developing efficient algorithms for 360-degree videos, generative adversarial networks (GANs) for image classification, and real-time video transmission over wireless systems. His publications span over two decades, addressing challenges in video compression efficiency, bufferless rate control, and neural network applications in multimedia systems. Dr. Sun’s articles highlight trends in hybrid coding strategies, probability-based optimization, and spatial/temporal scalability in video coding. His work bridges theoretical advancements and practical implementations for emerging technologies such as virtual reality video streaming and collaborative robotics. His academic webpage is available at https://faculty.uca.edu/yusun/ .

Angelo Sotgiuمشاهده پروفایل

استادیار

• Machine Learning Security
• Adversarial Machine Learning
• Cybersecurity
• +۳ مورد دیگر

Angelo Sotgiu is an Assistant Professor at the University of Cagliari's Department of Electronic and Computer Engineering. His research focuses on machine learning security, adversarial attacks, and malware detection. He holds an MSc in Telecommunication Engineering and a PhD in Electronic and Computer Engineering from the University of Cagliari. During his PhD (2019–2023), he conducted a visiting research period at CISPA Helmholtz Center for Information Security (Saarbrücken, Germany). He has worked as a Research Collaborator for the National Interuniversity Consortium for Informatics (CINI) from 2022–2023. His work emphasizes adversarial robustness, AI security, and practical defenses against attacks on machine learning systems. Key contributions include the ImageNet-Patch dataset for adversarial patch benchmarking, the secml Python library for secure ML, and frameworks like Fader for fast adversarial example rejection. His research bridges theoretical advancements with real-world cybersecurity applications.