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Xuekun LuView profile

Senior Lecturer

• Lithium-ion batteries
• Operando imaging
• 3D microstructure-resolved electrochemical modeling
• +6 more

Dr. Xuekun Lu is a Senior Lecturer in Green Energy at Queen Mary University of London's School of Engineering and Materials Science, where he also serves as Deputy Chair of the Undergraduate Subject Exam Board. His academic career focuses on the intersection of materials science, electrochemistry, and sustainable energy technologies, with particular emphasis on advanced characterization and modeling of energy storage systems. Dr. Lu's research primarily centers on three interconnected areas: (1) revealing the interplay between 3D microstructure and performance/failure mechanisms of electrochemical energy devices (lithium-ion batteries, fuel cells) using correlative operando characterization techniques (X-ray CT, ptychography, XRD-CT, FIB-SEM, optical microscopy, TEM); (2) multiscale multiphysics modeling of mass transport and electrochemical performance at electrode, device, and system levels; and (3) developing scalable and sustainable engineering solutions to fabricate advanced electrodes with tunable microstructure to enhance energy density, power density, and cycle life of next-generation lithium-ion batteries. His publication record includes 57 total papers with an h-index of 27 and 2,713 citations, featuring 14 first-authored papers in high-impact journals including Nature Communications, Energy & Environmental Science, and Joule. His work demonstrates a clear progression toward increasingly sophisticated multimodal characterization approaches combined with advanced computational modeling to address fundamental challenges in energy storage technology, with particular focus on microstructure-performance relationships in battery electrodes. ESI Highly Cited Paper (citation > 400) Editor's Highlights in Nature Communications 2020 Top 50 Chemistry and Materials Sciences Articles The Top 25 Chemistry and Materials Sciences Articles of 2023 Invited Reviewer for EPSRC Open Postdoctoral Fellowship Dr. Lu currently mentors PhD students Alex Han (researching 'Fabricating Novel-Structured Lithium-Ion Batteries') and Daqing Li (working on 'Advanced Manufacturing of Novel-Structured Next-Generation Automotive Lithium-Ion Battery Electrodes'). He has secured significant research funding, including a £448,598 EPSRC grant for 'From 2D to 4D: correlative imaging and modelling for next-generation automotive' research. His work has been translated into industry R&D for lithium-ion batteries, demonstrating strong academic-industrial collaboration. His research group employs a comprehensive approach combining cutting-edge imaging techniques with advanced computational modeling to tackle critical challenges in energy storage technology, with particular focus on understanding and optimizing the relationship between electrode microstructure and battery performance across multiple spatial and temporal scales.

Zhilin GuoView profile

Researcher

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +5 more

Zhilin Guo is a PhD student in the Department of Computer Science and Technology at the University of Cambridge , associated with the Core Lab and supervised by Prof. Cengiz Öztireli . His research focuses on AI algorithms for high-fidelity 3D scene understanding , reconstruction , and editing , driven by a passion for enabling machines to interpret and recreate the real world. His academic background includes a Master's degree in Computer Science from Columbia University , where he worked on spatial-temporal 3D hand pose estimation for Parkinson's Disease evaluation under Prof. Zhiguo Cao and Prof. Yang Xiao . He holds a Bachelor's degree from Boston University , where his foundation in Computer Science and Artificial Intelligence was established. Research Interests: Artificial Intelligence, Computer Vision, Deep Learning, 3D Scene Reconstruction, Neural Radiance Fields, Medical Image Analysis Experience: Neuromorphic Computing (2023-present), Hand Pose Estimation (2022-2023), Self-driving Algorithm Research (2020-2022), fMRI Data Analysis (2019). Publications: 4+ peer-reviewed articles at conferences like NeurIPS , ECCV , and journals including IEEE Journal of Biomedical and Health Informatics . Outside research, he enjoys tennis , jogging , and miniature painting . He actively seeks academic collaborations and can be contacted at zg296@cam.ac.uk .

• Medical Imaging
• Cybersecurity
• Federated Learning
• +3 more

Gouenou COATRIEUX is a full Professor at IMT Atlantique Bretagne-Pays de la Loire, affiliated with the Department of Image and Information Processing (ITI) and the LaTIM Inserm U1101 laboratory . His research focuses on medical imaging security , cybersecurity in healthcare systems , and federated learning . Key interests include DNA-based data storage, privacy-preserving AI, and intrusion detection in cyber-physical systems. Research activities span medical image steganography , secure federated learning frameworks , and biomedical data anonymization . Notable projects address GDPR compliance in clinical data warehouses and watermarking techniques for AI model integrity. He has collaborated on digital twin technologies for anomaly detection in critical infrastructure. Recent work emphasizes non-IID federated learning defense mechanisms , CT imaging enhancement , and homomorphic encryption applications. Over 150 peer-reviewed publications demonstrate expertise in interdisciplinary topics like medical IoT security and deep learning for radiology. No scientific awards are listed, though his contributions to healthcare cybersecurity are widely recognized in academic circles. Advising and grant details are not explicitly stated in available materials. Primary affiliation is with IMT Atlantique's Brest campus.

Yue CaoView profile

Professor

• Radiation Oncology
• Medical Imaging
• Artificial Intelligence in Healthcare
• +5 more

Yue Cao, Ph.D., is a Professor in the Department of Radiation Oncology BME at the University of Michigan. His work focuses on integrating advanced imaging techniques with radiation therapy to improve cancer treatment outcomes. He leads research in MRI-guided radiotherapy, adaptive therapy optimization, and AI-driven predictive modeling for personalized oncology. Key research areas include real-time motion tracking via MRI, biomarker-driven response assessment, and multi-modal imaging fusion. His clinical trials investigate de-escalation strategies using PET/MRI biomarkers in HPV-related oropharyngeal cancers and hepatocellular carcinoma. He has pioneered techniques like sparse sampling MRI for volumetric tracking and neural network-based image reconstruction. Publications emphasize innovations in radiotherapy planning quality assurance, survival prediction using longitudinal imaging, and overcoming interobserver variability in tumor segmentation. His team collaborates on large-scale datasets for brain metastasis detection (e.g., nnU-Net extensions) and liver function assessment through dynamic contrast-enhanced MRI. Current efforts include developing AI-enhanced knowledge-based systems for NRG Oncology trials and exploring synergies between 90Y SIRT and SBRT therapies. His work bridges cutting-edge imaging science with clinical implementation to reduce treatment toxicity while maximizing tumor control.

Marcello CarioniView profile

Assistant Professor

• Optimal Transport
• Inverse Problems
• Machine Learning
• +4 more

Marcello Carioni is an Assistant Professor in Mathematics at the University of Twente, affiliated with the Mathematics of Imaging & AI (MIA) group and the SACS group within the Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on optimal transport theory, inverse problems, calculus of variations, and their applications in machine learning and imaging. He holds a PhD in Mathematics from the Max-Planck Institute for the Mathematics in the Sciences (Leipzig), preceded by a M.S. and B.S. in Mathematics from the University of Parma. Education: B.S. (2009, University of Parma), M.S. (2012, University of Parma), PhD (2017, Max-Planck Institute) Research interests include: Dynamic optimal transport and preferential path analysis Sparsity in variational regularization Applications to medical imaging and neural networks Generalized conditional gradient methods Recent work emphasizes sparsity optimization for dynamic inverse problems, Lipschitz spaces in neural networks, and end-to-end reconstruction techniques. His 2025 preprints explore Wasserstein curves and distributionally robust optimization. He has advised PhD students including Christian Amend (2024) and Floor van Maarschalkerwaart (2024).

• Machine Learning
• Deep Learning
• Signal and Image Processing
• +5 more

BOHI Amine is a Lecturer-Researcher at CESI , affiliated with the Engineering and Numerical Tools research team. He holds a PhD in Computer Science from the University of Toulon (2013–2017), focusing on Fourier descriptors inspired by the human visual cortex for maritime surveillance. His academic background includes a Research Master in Computer Science (University of Fez, Morocco, 2009–2011) and a Bachelor's in Mathematics and Computer Science (University of Fez, 2006–2009). Research Interests: Machine Learning, Deep Learning, Signal and Image Processing, 3D Shape Analysis, and applications in Computer Vision and Digital Health. Current projects include developing emotional interaction solutions for neurodegenerative patients (collaboration with VyV3 Bourgogne) and multimodal emotion recognition systems. Education Activities: Teaches Computer Science and Engineering Sciences at various levels, including Integrated Preparatory Cycle, Engineering Cycle, and Specialized Bachelor's Degree. Specializes in Algorithms, Programming, Operations Research, and Web Development. Advising & Collaborations: Supervised over 10 research internships across institutions like the University of Limoges, Marrakech, and Aix-Marseille University. Notable projects include AI-driven facial emotion recognition, multimodal pain assessment, and EEG/ECG monitoring systems on Raspberry Pi. Collaborates with institutions such as the Timone Neuroscience Institute and Institut Fresnel. Publications: Authored 5 peer-reviewed journal articles and 9 conference papers (2017–2024). Key contributions include deep learning approaches for emotion recognition in elderly patients, medical imaging segmentation, and biomechanical modeling of cortical folding patterns.

JOSE CARLOS CABALEIRO DOMINGUEZView profile

Professor

• High Performance Computing
• Parallel Systems Architecture
• Parallel Algorithms
• +7 more

Prof. José Carlos Cabaleiro Domínguez is a Full Professor at the Department of Electronics and Computing, University of Santiago de Compostela. He holds a BS (1989) and PhD (1994) in Physics from the same university. His academic journey includes roles as an associate professor at the University of A Coruña (1990–1994) and associate professor at US圣地亚哥 de Compostela until 2022. He is affiliated with the Center for Research in Intelligent Technologies (CiTIUS) and leads the ARQCOMP research group in Computer Architecture. His research focuses on high-performance computing, parallel systems architecture, LiDAR data processing, and cloud computing. Notable contributions include optimizing parallel algorithms for sparse matrices, developing frameworks like BigOPERA for big data, and advancing LiDAR-based applications in infrastructure mapping and autonomous systems. He has led over 20 national/international projects, including HiPerHC2DA (2023–2026) and TLIX2 (2017–2018). Prof. Cabaleiro’s work spans 150+ peer-reviewed publications, with recent emphases on digital forensics (e.g., Android private browsing analysis), graph-based LiDAR segmentation, and energy-efficient thread migration strategies for NUMA systems. His research integrates hardware performance analysis, parallel algorithm design, and real-world applications in geomatics, bioinformatics, and cybersecurity.

Abhirup BanerjeeView profile

Research Fellow

• Cardiovascular Imaging
• Artificial Intelligence
• Computational Modelling
• +3 more

Dr. Abhirup Banerjee is a Royal Society University Research Fellow and Principal Investigator at the University of Oxford's Department of Engineering Science, affiliated with Wolfson College. His research focuses on Multimodal Medical Data Integration & Analysis , combining artificial intelligence (AI), computational modeling, and cardiovascular science to develop clinical tools for diagnostics and interventions. He leads the Multimodal Medical Data Integration & Analysis (MultiMeDIA) Lab , which specializes in 3D/4D cardiac reconstruction from imaging data (e.g., MRI, angiography) and real-time clinical applications like the SMART-ALERT study for atrial fibrillation monitoring. Dr. Banerjee holds a PhD in Computer Science from the Indian Statistical Institute (ISI), Kolkata, and previously served as a Postdoctoral Researcher under Prof. Robin Choudhury. He also served as a Stipendiary Lecturer at Mansfield College, Oxford. His work includes patented AI pipelines for coronary reconstruction and infarction modeling, collaborating with Oxford University Innovation. He is an Innovation Champion and Mental Health First Aider , and actively engages in public science outreach. Key achievements include over 80 peer-reviewed publications, the Young Scientist Award (2017), and editorial roles in international journals. Current projects involve digital heart models for cardiac electromechanics and rough-probabilistic modeling for brain MRI analysis.

• Computer Science
• Database Systems
• Business Process Management
• +5 more

Jianwen Su is a Professor at the University of California, Santa Barbara, USA, with a distinguished career spanning over three decades in Computer Science , particularly in Database Systems , Business Process Management , and Web Services . Their research bridges theoretical foundations with practical applications, focusing on data-centric process modeling , formal verification , and spatio-temporal data analysis . Key contributions include the design of artifact-centric workflow models , temporal constraint languages , and query systems for uncertain data . Recent work integrates machine learning with proteomic analysis for stress biomarker discovery and LLM-based extraction of structured data from clinical reports. Notable scientific recognition includes the 2019 ACM PODS Alberto O. Mendelzon Test-of-Time Award . Collaborations span institutions globally, with frequent co-authorship in journals like Information Systems , ACM Transactions on Management Information Systems , and conferences such as BIBM and TIME .

Anas AbboodView profile

Researcher

• Vision and Visualization
• Human-Computer Interaction
• Computer Integrated System in Health
• +3 more

Anas Abbood, Ph.D., is a researcher in the Department of Mathematics and Computer Science at Saint Louis University (Madrid Campus). He holds a Ph.D. in Computer Science and Information Technology (2017) from University Putra Malaysia, along with advanced degrees in computer science and data security from Baghdad institutions. Ph.D. in Computer Science and Information Technology, University Putra Malaysia M.Sc. in Computer Science and Data Security, University of Technology, Baghdad High Diploma in Computer Science and Data Security, National Computer Center, Baghdad B.Sc. in Operation Research, Mansour University College, Baghdad His research spans Vision and Visualization , Human-Computer Interaction , Medical Imaging Systems , and Encryption/Security . Recent work focuses on biometric liveness detection and automated cardiac analysis via echocardiographic imaging. Key contributions include patents in ventricular volume measurement and advancements in 3D/4D image processing. He has published extensively in journals and conferences on topics like stroke volume measurement , heart anatomy modeling , and low-resolution biometric authentication . His work bridges computer science and medical diagnostics through interdisciplinary techniques. Silver Award, PRPI-UPM (2012) Silver Award, MTE-PWTC, KL (2013) No formal advisee lists or lab/team affiliations are specified, though his patent and publications imply collaborative research.

Lakshmish RamaswamyView profile

Professor

• Big Data
• Pervasive Computing
• Internet of Things
• +6 more

Lakshmish Ramaswamy is a Professor of Computer Science at the University of Georgia's School of Computing, part of the Franklin College of Arts & Sciences. He holds a Ph.D. from Georgia Institute of Technology (2005), an M.S. from Indian Institute of Science (1999), and a B.E. from University of Mysore (1996). His research focuses on Big Data, IoT, Cloud Computing, and Environmental Informatics, with notable contributions to resilient machine learning systems, geospatial analytics, and sustainable sensor networks. He has led projects funded by NSF, Army Research, and industry, including initiatives like CyanoTRACKER for harmful algal bloom monitoring. Dr. Ramaswamy has received a Best Paper Award (2010) and secured grants such as an NSF/NIFA award for GeoAI and an Accenture grant for data harmonization. His work bridges distributed systems, edge computing, and environmental sustainability, with collaborations across disciplines like geography and forestry. He has advised numerous interdisciplinary projects and maintains an active presence in conferences and journals, contributing to both theoretical and applied computing advancements.

• Computer Vision
• Robotics
• Machine Learning
• +5 more

Dr. Hamid Rezatofighi is a Senior Lecturer in the Department of Data Science & AI at Monash University's Faculty of Information Technology in Australia. He holds a PhD from the Australian National University (2015) and a Master's in Biomedical Engineering from the University of Tehran (2009). Previously, he was an Endeavour Research Fellow at Stanford University and a Senior Research Fellow at the University of Adelaide's Australian Institute for Machine Learning (AIML). His research focuses on computer vision, robotics, and deep learning, with emphasis on robot visual perception in dynamic environments. Key contributions include multi-object tracking, autonomous systems, and neuro-symbolic AI frameworks. He has led over $17M in research funding, including DARPA projects and an ARC Discovery Project. His work contributes to UN SDG goals related to innovation and infrastructure. Notable projects include the Neuro-Symbolic Systems (HARNESS) project ($5M+), real-time safety intervention systems, and semantic 3D reconstruction frameworks. He has published >80 peer-reviewed papers in top venues like CVPR/ICCV/ECCV and serves as an Area Chair for major AI conferences. His datasets (JRDB series) are widely used benchmarks for robotic perception. Current research spans autonomous navigation, multi-sensor fusion, and socially-aware human-robot interaction.

Arda GencView profile

Researcher

• Materials Science
• Nanotechnology
• Electron Microscopy
• +3 more

Dr. Arda Genc is the Microscopy and Microanalysis Facility Supervisor at the University of California, Santa Barbara (UCSB), affiliated with the Department of Materials within the College of Engineering. His research focuses on advanced microscopy techniques, nanomaterials characterization, and energy storage materials. He holds a Researcher academic rank and has contributed extensively to the fields of materials science and nanotechnology through his work on electron microscopy, machine learning applications in materials analysis, and catalytic systems. Key research interests include high-throughput analysis of nanomaterials using AI-driven methods, defect engineering in alloys, and thin film characterization. His work bridges computational approaches with experimental microscopy, addressing challenges in energy storage and catalytic efficiency. Dr. Genc’s articles highlight advancements in 4D STEM analysis, superconducting thin films, and battery material degradation mechanisms. His studies often integrate machine learning with electron microscopy to enhance material characterization speed and accuracy. He has collaborated on projects involving semiconductor defect dynamics, CO2 hydrogenation catalysts, and silicon anode degradation in lithium-ion batteries. As facility supervisor, he oversees cutting-edge microscopy resources supporting UCSB’s research community. His contributions span over 50 peer-reviewed publications, emphasizing interdisciplinary approaches to materials innovation.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +5 more

Hui Fang is a researcher at Loughborough University , Department of Computer Science, UK. Their work spans Artificial Intelligence , Machine Learning , and Remote Sensing , with recent focus on Medical Imaging , Natural Language Processing , and Urban Mobility . Publications highlight innovations in MRI Reconstruction , Legal Judgment Prediction , and Video SAR Analysis . Research Interests include Graph Neural Networks , Transformer Models , and Data Mining applied to domains like Agricultural Technology , Healthcare Systems , and Smart Cities . Methodologies emphasize Interpretable AI , Federated Learning , and Multimodal Integration . Key 2024-2025 Trends involve Transformer-Based Architectures for Medical Imaging , Information Bottleneck in Spatio-Temporal Networks , and Bootstrapping Techniques for Information Extraction . Collaborative projects with Dongdong Weng , Zhu Xiao , and Yong He demonstrate interdisciplinary applications in Transportation , Healthcare , and Agriculture . Recent Co-authored Publications span IEEE Transactions , Neurocomputing , and Remote Sensing Journals , addressing Dynamic Phasor Modeling , 4D Facial Capture , and Virtual Reality Usability .

• Robotics and Automation
• Plant Disease Detection
• Spectral Imaging
• +3 more

Gerrit Polder is a Researcher at Wageningen University & Research, affiliated with the Robotics and Automation department (PE&RC). His work focuses on advancing technologies for plant disease detection, spectral imaging, and robotics in agricultural systems. He has contributed to projects such as OPTIMA, which aims to improve disease management in perennial crops and open-field vegetables using integrated pest management and smart camera systems. His research interests include machine learning applications in agriculture, non-destructive plant phenotyping, and the development of automated systems for crop monitoring. Key areas include spectral imaging for quality assessment, deep learning for disease detection, and 3D plant segmentation techniques. PhD supervision: Co-promotor for projects on 4D plant phenotyping, pre-symptomatic disease detection, and machine vision in hydroponic agriculture. Key contributions: Over 160+ publications, including patents on 3D plant reconstruction and smart camera systems. Affiliations: Active in international collaborations, such as EU-funded OPTIMA, and frequently presents at conferences on spectral imaging and precision agriculture. Recent work emphasizes the integration of spectral imaging and AI for early disease detection in crops like grape, apple, and carrot, as well as automated quality assessment in tomatoes and kiwifruit. His research bridges robotics, computer vision, and agricultural science to enhance sustainable farming practices.