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Majid ZamaniView profile

Lecturer

• Biomedical Engineering
• Artificial Intelligence
• Neuroscience
• +3 more

Dr Majid Zamani is a Lecturer in the School of Electronics & Computer Science at the University of Southampton. His research focuses on implantable and wearable biomedical devices, applied AI in biomedical engineering, and hardware-efficient processing frameworks. Member of: Digital Health and Biomedical Engineering Institute for Life Sciences Member of: Centre for Internet of Things and Pervasive Systems Member of: Centre for Health Technologies Member of: UKRI AI Centre for Doctoral Training in AI for Sustainability (SustAI) Current research addresses key challenges in scalability, signal processing, sensing, energy efficiency, and miniaturization for next-generation implantable brain-machine interfaces (iBMI). He explores hardware-efficient computational platforms for biomedical applications, including AI-driven algorithms for neural signal/image processing, augmented navigation in constrained anatomical spaces, and low-power real-time processors in 180/90/45 nm CMOS technologies. Recent publications focus on deep learning for spike sorting, binarized neural networks, noise-aware speech enhancement, and AI integration in surgical navigation. His work bridges biomedical engineering with AI and hardware optimization. Teaching: Digital system design (ELEC6236) 42+ publications in journals like IEEE Transactions on Medical Imaging, Journal of Neural Engineering, and IEEE Access

Nico LangView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Remote Sensing
• +4 more

Nico Lang is an Assistant Professor at the University of Copenhagen's Department of Computer Science, associated with the Pioneer Centre for AI and Global Wetland Centre. He holds a PhD from ETH Zurich where he developed methods for global forest structure mapping. His research bridges computer vision, machine learning, and remote sensing to address environmental challenges like deforestation monitoring and climate change mitigation. Lang's research focuses on: Developing probabilistic deep learning models for global canopy height estimation Advancing open-set recognition under adversarial conditions Creating multi-modal representation learning frameworks for geospatial data Applying computer vision to biodiversity monitoring and conservation His work frequently appears in top venues like Nature, CVPR, and ECCV. His publications show strong emphasis on: environmental applications of AI, uncertainty-aware deep learning, and global-scale geospatial analysis. Recent work explores vision-language models and fine-grained open-set recognition. Awards & Honors: Culmann Prize for outstanding doctoral thesis (2023) Outstanding Reviewer for CVPR 2023 U.V. Helava Award for best paper in ISPRS Journal (2019) Associate PhD Fellow at Max Planck ETH Center (2018) Collaborations & Labs: Directs research at the intersection of computer vision and environmental science. Key affiliations include NASA GEDI mission, Swiss Federal Institute for Forest, Snow and Landscape Research, and the Pioneer Centre for AI. Organizes workshops like FGVC at CVPR and SSL4EO summer schools.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +3 more

William Henrich Due serves as a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Machine Learning section. His work intersects with the SCIENCE AI Centre and leverages the department's high-performance compute cluster for research in quantum computing, sustainable AI, and medical applications. Research focuses span quantum machine learning (biomolecular simulations, photonic processors), sustainable AI systems (energy efficiency, climate impact), and clinical applications (EEG analysis, medical imaging). His recent publications reveal strong activity in quantum-classical hybrid systems, with 8/15 recent papers addressing quantum computing challenges. The work emphasizes practical implementations in medical imaging and resource-constrained environments. His research aligns with DIKU's Machine Learning section priorities including medical imaging biomarkers and sustainable computing. Key infrastructure includes TreeSense for remote sensing and the department's dedicated compute cluster. No scientific awards were explicitly documented in the provided materials. Due contributes to DIKU's teaching mission as a Lecturer while engaging with the SCIENCE AI Centre's interdisciplinary initiatives. His work connects with medical imaging applications and quantum computing infrastructure development. Active in the Machine Learning section's research ecosystem, his work intersects with medical imaging analysis and quantum computing applications, utilizing specialized resources like TreeSense for environmental monitoring.

Jon SporringView profile

Professor

• Computer Science
• Mathematical Image Processing
• Medical Image Analysis
• +6 more

Jon Sporring is a Professor at the Department of Computer Science, University of Copenhagen, specializing in theoretical and applied image processing, stochastic geometry, and biomedical imaging. He leads research in mathematical and medical image analysis, computer graphics, and pattern recognition. Education: Ph.D. in Computer Science (1998), Master in Computer Science (1995), both from University of Copenhagen Affiliations: Pioneer AI section, Applied Geometry Lab, and Faculty of Science External Roles: Visiting professor at McGill University (2012-13), co-founder of DigiCorpus Aps (2012-16) His research integrates scale-space theory, statistical shape analysis, and advanced imaging techniques for applications in medical diagnostics, materials science, and neuroscience. Recent work focuses on 3D reconstruction, persistent homology for bias correction, and AI-driven biomedical analysis. Jon teaches computer science at all academic levels, currently offering courses in bioimaging, signal processing, and deep learning. He emphasizes collaborative projects with external partners and has held administrative roles including Vice-Chair for Research at DIKU. His 15 most recent publications reflect expertise in medical imaging, 3D modeling, and AI applications, with subfields spanning neurodegenerative disease analysis, mitochondrial ultrastructure, and multi-scale image processing. Articles demonstrate interdisciplinary impact across medicine, biology, and materials science.

Yi PanView profile

Professor

• Parallel and Cloud Computing
• Wireless Networks
• Bioinformatics
• +3 more

Dr. Yi Pan is a Distinguished University Professor and Chair of the Department of Computer Science at Georgia State University. He holds a B.Eng. and M.Eng. from Tsinghua University (China) and a Ph.D. from the University of Pittsburgh (USA). His research focuses on parallel computing, wireless networks, bioinformatics, and machine learning, with over 330 publications and 39 authored/edited books. Dr. Pan has organized international conferences and delivered over 40 keynote speeches globally. Education: B.Eng., Computer Engineering, Tsinghua University, 1982 M.Eng., Computer Engineering, Tsinghua University, 1984 Ph.D., Computer Science, University of Pittsburgh, 1991 Research Interests: Dr. Pan’s work spans parallel and cloud computing, wireless networks, and bioinformatics. His contributions include scalable algorithms for functional module discovery in protein networks, self-defense mechanisms for cyber threats, and efficient routing protocols for sensor networks. Recent efforts integrate AI into healthcare, such as lesion localization in OCT imaging and autism spectrum disorder diagnosis via EEG analysis. Awards & Recognition: IEEE Distinguished Speaker (2000–2002) Yamacraw Distinguished Speaker (2002) Listed in Who’s Who in America and Who’s Who in Computational Science Awards from NSF, AFOSR, IBM, and Mellon Foundation Advising & Leadership: As department chair, Dr. Pan oversees interdisciplinary collaborations between Computer Science and Computer Information Systems. He has advised numerous students and holds editorial roles in 15 journals, including several IEEE Transactions. His grants focus on advancing AI-driven healthcare, cybersecurity, and computational biology. Labs & Teams: Leads research initiatives in bioinformatics, medical AI, and parallel computing, collaborating with industry partners like IBM and Shell Oil. Active in organizing conferences such as the IEEE International Conference on Bioinformatics and Biomedicine.

• Medical Imaging
• Deep Learning
• Cardiac Ultrasound
• +7 more

Dr. Qingjie Meng is a Researcher in the Department of Computing at Imperial College London, affiliated with the Faculty of Engineering. Their work focuses on advancing AI-driven medical imaging technologies, particularly in cardiac ultrasound, fetal anomaly detection, and MRI analysis. Meng's research emphasizes privacy-preserving data solutions and generative models for healthcare applications. Research interests include deep learning for medical image synthesis, motion tracking in MRI, and real-time AI applications in ultrasound screening. Recent work explores foundational models like EchoFlow for cardiac imaging and SACB-Net for medical registration tasks. Publications highlight trends in generative AI for medical data, robust segmentation techniques, and multi-view cardiac tracking. Meng's contributions address challenges in fetal anomaly detection and respiratory motion correction in MRI scans. No awards or advising roles are explicitly listed in the provided text. Their work contributes to labs focused on biomedical AI and healthcare informatics, though specific lab affiliations are not detailed.

• Machine Learning
• Computer Vision
• Signal Processing
• +6 more

Giacomo Boracchi is a researcher at the Polytechnic University of Milan , focusing on machine learning, computer vision, and signal processing. His work spans anomaly detection, change detection in data streams, 3D imaging, and biomedical applications. Key Collaborations : Diego Carrera, Luca Magri, Cesare Alippi Industries : Embedded systems, medical imaging, environmental monitoring Recent research explores adaptive Kalman filtering for battery estimation, zero-shot anomaly detection, and explainable AI for vision-language models. Publications highlight applications in waste sorting, histological data generation, and cardiac monitoring. His technical focus includes convolutional networks, ensemble learning, and domain adaptation. Boracchi's work bridges theoretical advancements with real-world systems in fraud detection, semiconductor manufacturing, and wearable health devices.

Haidar SametView profile

Researcher

• Power Systems
• Electrical Engineering
• Renewable Energy
• +4 more

Haidar Samet is a Researcher in the Department of Electrical Engineering at Eindhoven University of Technology, focusing on power systems, electric arc furnaces, and fault detection. He previously held academic roles at Shiraz University, including Professor (2018–2020), Head of Department of Power and Control (2017–2019), Associate Professor (2014–2018), and Assistant Professor (2008–2014). His research bridges electrical engineering fundamentals with practical applications in renewable energy and grid reliability. Research Interests: Electric Arc Furnace Modeling and Optimization Fault Detection in Power Systems and Photovoltaic Systems Grid Protection Mechanisms Renewable Energy Integration and Smart Grid Technologies Power Quality Analysis Publications highlight advancements in arc fault modeling, transformer protection algorithms, and renewable energy system optimization. His work frequently addresses challenges in grid stability, energy efficiency, and predictive maintenance. No scientific awards or grants were explicitly mentioned in the provided materials.

Mohammad Amir OwrangiView profile

Associate Professor

• Radiation Oncology
• Medical Physics
• Brachytherapy
• +3 more

Mohammad Amir Owrangi serves as Associate Professor in Radiation Oncology at Baylor College of Medicine (Houston) and University of Texas Southwestern (Dallas), with prior appointments as Assistant Professor at both institutions and the University of Toronto. His expertise bridges medical physics and clinical radiation oncology, focusing on advancing precision radiotherapy through technological innovation. His academic foundation includes a BSc and MSc in Physics and Radiation-Medicine Engineering from Shiraz University (Iran), followed by a PhD in Biomedical Engineering from The University of Western Ontario (Canada), and a CAMPEP-accredited residency in Therapeutic Radiologic Medical Physics at University of Michigan Health System. Dr. Owrangi's research centers on solving critical challenges in image-guided radiotherapy, particularly MRI-based treatment planning for gynecological cancers. His work pioneers synthetic CT generation using deep learning to eliminate CT simulation needs, optimizes brachytherapy applicators for cervical cancer, and develops metal artifact reduction techniques. Recent publications demonstrate increasing integration of AI methods to improve dosimetric accuracy and workflow efficiency in radiation therapy. Analysis of his 15 most recent publications (2017-2024) reveals three dominant research thrusts: (1) MRI-only radiotherapy workflows with synthetic CT generation using deep learning/GANs, (2) brachytherapy optimization for cervical cancer through applicator design and dosimetry, and (3) artifact reduction in multi-modal imaging. His work consistently targets clinical translation, with 70% of recent publications focusing on gynecological applications. Scientific Awards: No awards explicitly mentioned in source materials Academic advising and research grant details were not provided in available documentation. His collaborative network spans Baylor College of Medicine, UT Southwestern, University of Toronto, and international institutions including Shiraz University and University of Western Ontario, with frequent co-authorship among medical physicists and radiation oncologists. While specific laboratory infrastructure isn't detailed, his research requires advanced capabilities in MRI/CT image processing, Monte Carlo simulation, and deep learning development, suggesting access to high-performance computing resources and clinical imaging datasets at his affiliated institutions.

• Predictive Maintenance
• Machine Learning
• Fault Detection
• +5 more

Dr. Lilach Goren Huber is a Senior Lecturer and R&D Projects Leader at the ZHAW School of Engineering, specializing in predictive maintenance and data-driven solutions for industrial systems. She leads multiple projects focusing on AI applications in anomaly detection (e.g., wind turbines, solar power plants), physics-informed machine learning for sensor error correction, and fault prognostics under data scarcity. Her work bridges academic research with industrial implementation, emphasizing scalable deep learning frameworks for commercial fleets and energy infrastructure. Research interests include predictive maintenance strategies, machine learning for industrial IoT, and hybrid prognostics combining domain knowledge with AI. She has published extensively in journals like International Journal of Prognostics and Health Management and Journal of Big Data , with a focus on renewable energy systems, SCADA data analysis, and cross-domain transfer learning. Current Projects: Physics-informed ML for elastomer sensors, end-to-end fault prognostics for power grids, and hybrid prognostics research. Past Contributions: Developed decision support systems for laser cutting machines, optimized hydroelectric maintenance schedules, and created risk-based frameworks for Swiss national road safety equipment. She actively contributes to the Expert Group Smart Maintenance and serves as a project leader in the ZHAW-PARC initiative. Her work addresses technical challenges like data contamination, sensor calibration errors, and real-world implementation barriers in industrial AI adoption.

• Deep Learning
• Medical Imaging
• Artificial Intelligence
• +7 more

Peter Eggenberger Hotz is a researcher at the Zurich University of Applied Sciences (ZHAW) , affiliated with the School of Engineering . His work bridges Artificial Intelligence , Medical Imaging , and Computational Modeling . As a team member in projects like Bio-Hybrid Hierarchical Organoid-Synthetic Tissues (Bio-HhOST) and DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning , he focuses on Deep Learning applications in Computed Tomography and Radiation Therapy .

Konstantinos KyriakopoulosView profile

Senior Lecturer

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +4 more

Dr. Konstantinos Kyriakopoulos is a Senior Lecturer in Cyber Intelligence, specializing in cybersecurity challenges and AI-driven solutions for converged Information and Operational Technology infrastructures. His work integrates machine learning with cyber-physical systems to enhance decision-making under uncertainty, with a focus on resilient AI algorithms and autonomous defense mechanisms. He has contributed to projects funded by Dstl, Innovate UK, and industry partners, addressing Industry 4.0 security, smart vehicles, electromagnetic attacks, and smart buildings. His research interests include adversarial machine learning, evidence theory for uncertainty modeling in ML, and autonomous cyber defense systems. He has developed novel approaches using reinforcement learning, hidden Markov models, and Bayesian optimization. His work emphasizes creating ML algorithms resistant to adversarial attacks while improving cybersecurity resilience in dynamic environments. Dr. Kyriakopoulos has also explored applications of AI in educational technology, such as automated assessment of spoken English proficiency. His recent publications focus on federated learning security, causal reinforcement learning, and anomaly detection techniques. He has experience in translating research into practical solutions, including business model pitches to investors in Canary Wharf for threat modeling tools. His contributions span over 40 peer-reviewed articles, with notable work in network intrusion detection, attack prediction via HMMs, and wavelet-based compression for network measurements. His expertise bridges academia and industry, addressing both theoretical advancements and real-world implementation challenges.

• Condensed Matter Physics
• Computational Materials Science
• Artificial Intelligence in Materials Discovery
• +11 more

Aldo H. Romero is the Eberly Family Distinguished Professor of Physics and Astronomy at West Virginia University, where he serves as Director of Research Computing . His research integrates computational materials science, artificial intelligence, and high-performance computing to advance understanding of condensed matter systems, including strongly correlated materials, 2D materials, and magnetic compounds. Education : Ph.D. in Physics and Chemistry from University of California, San Diego (1998) Awards : Eberly Family Distinguished Professorship Romero’s group specializes in Density Functional Theory (DFT) , Dynamical Mean Field Theory (DMFT) , and machine learning for materials discovery. They develop open-source tools like PyProcar , MechElastic , and ABINIT , focusing on properties such as electronic structure, elastic behavior, and magnetic interactions. Recent work explores chiral materials , AI-driven optimization , and high-throughput computational methods for material design. Key trends in publications include studies on chiral symmetry in kagome lattices, machine learning for material properties, symmetry-based structure prediction, and DFT+DMFT analysis of correlated systems. Collaborations span Germany, France, China, Spain, and Italy, with applications in energy storage, quantum technologies, and spintronics. Scientific contributions encompass: Development of Fireball and ABINIT codes Machine learning for Hubbard U parameter optimization Chiral material discovery via symmetry arguments Pioneering work on 2D Rashba systems Labs and teams include: AI WVU Discussion Group (founded by Romero) West Virginia University Computational Materials Group Collaborative efforts with international institutions Grants and proposals highlight initiatives like the NSF & NVIDIA Open Multimodal AI Infrastructure (OMAI) program and Spencer Foundation support for AI in education. The group works with over 20 graduate students and 4 postdoctoral researchers, emphasizing interdisciplinary training and global collaboration.

• Image Processing
• Machine Learning
• Medical Imaging
• +3 more

Weiwei Cui is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. She specializes in image processing, machine learning, and medical imaging applications. Her research focuses on advancing computational methods for medical diagnostics, particularly in dental imaging and ophthalmology. She teaches courses on Image Processing and Machine Learning, integrating theoretical foundations with practical applications in computer science and engineering. Her research contributions include developing novel datasets like Ctooth+ and CARDIAN, advancing semi-supervised learning techniques for medical image segmentation, and applying transformer networks to conversational AI. Key areas of expertise include deep learning frameworks, neural networks, and biomedical signal processing. She has published extensively on topics ranging from dental CT segmentation to real-time cardiovascular imaging analysis. Dr. Cui’s work bridges computer science methodologies with healthcare challenges, emphasizing practical solutions for diagnostic accuracy and clinical decision-making. Her datasets and benchmarking efforts contribute to reproducible research in medical imaging communities. She actively engages in interdisciplinary collaborations to address real-world healthcare technology challenges.

Remco DuitsView profile

Associate Professor

• Differential Geometry
• Mathematical Image Analysis
• Geometric Deep Learning
• +2 more

Remco Duits is Associate Professor at Eindhoven University of Technology, leading research in mathematical image analysis and geometric deep learning. His work develops PDE-based methods on Lie groups for medical imaging, crack detection in infrastructure, and optimal transport. Key innovations include equivariant neural networks for homogeneous spaces and geodesic tracking algorithms for vascular analysis. Dr. Duits holds an ERC Starting Grant (2013) for Lie Group Analysis in Medical Imaging and an NWO VICI Grant (2021) for Geometric Learning. His team pioneers data-driven Cartan connections for vascular tree tracking and PDE-CNN architectures with axiomatic foundations.