جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Control Systems
• Computer Vision
• +۳ مورد دیگر

Jian Sun is a researcher affiliated with the Chinese Academy of Sciences' Institute of Computing Technology. His work spans interdisciplinary fields including machine learning, control systems, robotics, and signal processing. He collaborates with institutions globally to advance theoretical and applied research in computational methods and their real-world applications. Research interests focus on machine learning for healthcare diagnostics, optimization algorithms for complex systems, and sensor fusion technologies for autonomous systems. Recent work emphasizes applications in cognitive impairment detection, UAV-based communication systems, and crystal structure prediction. Publications highlight advancements in adaptive control methodologies, cybersecurity in control systems, and 3D reconstruction techniques. His contributions bridge theoretical foundations with practical implementations in robotics, aerospace, and environmental monitoring.

Xiao Guمشاهده پروفایل

پژوهشگر

• AI for Healthcare
• Biomedical Signal Processing
• Pervasive Healthcare
• +۳ مورد دیگر

Xiao Gu is a Researcher at the Computational Health Informatics Lab, Department of Engineering Science, University of Oxford. He holds a PhD in Computing from Imperial College London (2023), an MRes in Medical Robotics and Image Guided Intervention (2019), and a BEng in Electronic Engineering from Fudan University (2018). His research focuses on integrating pervasive sensing and deep learning for healthcare applications, addressing challenges in generalization and data scarcity. He has been recognized with awards including the BMVC Outstanding Reviewer (2024) and IEEE ICRA Student Travel Grant (2023). Key research interests include AI-driven healthcare solutions, biomedical signal processing, and the development of robust models for real-world medical applications. His work spans datasets like CFP for biomedical sensing and contributions to workshops on wearable intelligence. Xiao Gu has authored over 30 peer-reviewed publications, with recent work focusing on medical imaging, domain adaptation, and vision-language models. He actively contributes to academic activities, including editorial roles and conference reviewing.

Jorma Laaksonenمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Jorma Laaksonen is a Senior Lecturer at the Department of Computer Science, Aalto University (School of Science). His research focuses on artificial intelligence, machine learning, computer vision, and multimedia systems, with notable contributions to cross-lingual evaluation, hyperspectral imaging, and audio-visual attention mechanisms. He holds a Doctoral degree in Engineering and Technology from Helsinki University of Technology (1997). Education: Doctoral degree, Engineering and Technology, Helsinki University of Technology (1997) Licentiate degree, Engineering and Technology, Helsinki University of Technology (1991) Master's degree, Engineering and Technology, Helsinki University of Technology (1990) His research interests span AI-driven visual computing, human-computer interaction, and multimedia systems. Key projects include USSEE (audio-visual scene understanding) and ARTISDIG (AI for forest spectral analysis). He has published over 280 papers and supervised multiple theses. Awards include the IEEE Best Paper Award (2017) and Microsoft's Best Practice Award (2016). Recent work includes cross-cultural language modeling, efficient text-to-video retrieval, and open-world video instance segmentation. He leads the Lecturer Laaksonen Jorma group and actively participates in editorial roles and doctoral thesis evaluations.

Tarique Anwarمشاهده پروفایل

مدرس

• Data Science
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Tarique Anwar is a Lecturer in the Department of Computer Science at the University of York, UK, since 2021. He holds a PhD in Data Science from Swinburne University of Technology, Australia. As Programme Leader for both MSc and BSc Data Science programs, he oversees admissions and curriculum development. His academic roles include Data Science Admissions Tutor and membership in the Artificial Intelligence Group. Research interests span Data Science , Machine Learning , Artificial Intelligence , and Social Media Analysis , with a focus on solving real-world problems using structured/unstructured data. Notable areas include mental health detection via social media, network analysis, and explainable AI. His work addresses challenges in: Depression severity assessment using attention mechanisms Rumor source detection in social networks Eating disorder classification through multimodal learning Geographic-social-temporal pattern mining He supervises MS/PhD students in Data Science and Analytics, emphasizing interdisciplinary research. His publications (2020–2025) demonstrate expertise in AI applications for health, social networks, and optimization. Labs/Teams: Artificial Intelligence Group at the University of York, collaborating with industry and academic partners globally.

• 3D Face/Head Modeling
• Computer Vision for Self-Driving Vehicles
• Robot Manipulators
• +۴ مورد دیگر

Professor Nick Pears is a faculty member in the Department of Computer Science at the University of York. He holds roles including Deputy Head of Department (Research) and Chair of the Departmental Research Committee. His academic career spans postdoctoral research at the University of Oxford, a Fellowship at the University of Cambridge, and progressive roles at York from Lecturer to Professor (since 2022). He holds a BSc in Engineering and a PhD in Robotics from 'Dunelm' (likely Durham University). His research focuses on 3D facial modeling, computer vision for autonomous systems, robotic manipulation, and medical imaging. He leads the Vision, Graphics and Learning research group and has contributed to advancements in morphable models, gaze estimation, and medical anomaly detection. His work bridges theoretical computer vision with practical applications in healthcare and robotics. Recent publications emphasize generative models for medical imaging, 3D face reconstruction techniques, and adaptive robotics. His research often intersects AI ethics, emphasizing safe and explainable systems for autonomous vehicles and surgical applications. He actively collaborates with clinicians and engineers to translate vision algorithms into real-world solutions. As a leader in academic research governance, he oversees York's REF submissions and departmental strategy, fostering interdisciplinary innovation. His lab maintains an open-access repository of 3D morphable models and benchmark datasets for robotic motion planning.

• Data Science
• Computer Graphics
• Data Visualization
• +۳ مورد دیگر

Professor Mark Jones is a faculty member at the School of Mathematics and Computer Science , Swansea University , specializing in Computer Science . He serves as a full-time professor and is available for postgraduate supervision. His research interests span Data Science , Computer Graphics , Data Visualization , Time-Series Analysis , Machine Learning , and 3D Rendering . He has contributed to advancements in photon mapping , ray tracing , global illumination , and volume rendering . His recent publications focus on point cloud completion , text-based person re-identification , graph neural networks , and deep learning for time-series data . These works highlight trends in 3D reconstruction , neural network optimization , and medical imaging compression . Professor Jones has secured significant research grants, including EPSRC Grant EP/N028139/1 (2016–2019) and Leverhulme Grant RPG-2013-190 (2013–2015), and has held roles such as Member of the Editorial Board for Computer Graphics Forum (2014–2017). He supervises PhD students in areas like 3D medical image compression , face reenactment , and urban modeling visualization , and has contributed to Computational Foundry , a research facility at Swansea University.

• Machine Learning
• Causal Reasoning
• Mechanism Design
• +۷ مورد دیگر

Dr. Shanfeng Hu is an Assistant Professor in the Department of Computer and Information Sciences at Northumbria University, UK. He obtained his PhD from the same department in 2020 and joined the faculty as a Lecturer immediately after. During his PhD (2018-2020), he worked as a Senior Data Scientist in the financial sector. His research focuses on machine learning, causal reasoning, and mechanism design, with funded projects in advanced manufacturing (Innovate UK KTP), supply chain optimization (Digital Catapult, Innovate UK ISCF), and digital health (EU Erasmus+, EU MSCA). Education: PhD in Computer Science (2020) from Northumbria University, specializing in Metric Representations for Shape Analysis and Synthesis. Research interests include computer vision applications (e.g., defect detection, pose estimation), predictive maintenance, inventory management, health monitoring systems, and human-in-the-loop machine learning. He has collaborated on projects involving 3D printing (KTP-funded), steel surface analysis, and elderly health monitoring. His recent work addresses challenges in digital transformation for manufacturing, integrating predictive maintenance with inventory systems, and developing explainable AI for control systems. Projects are supported by Innovate UK, EU programs, and Digital Catapult. Lead researcher on Innovate UK KTP project: 3D printed Models Contributions to UN Sustainable Development Goals through education and healthcare projects Labs/Teams: Collaborates with industry partners in manufacturing, healthcare, and robotics sectors through funded initiatives.

Honglei Liمشاهده پروفایل

استادیار

• Smart Cities
• Virtual Communities
• Electronic Business
• +۵ مورد دیگر

Honglei Li is an Assistant Professor at the Computer and Information Sciences Department of Northumbria University. He holds a PhD in Information Systems from the Chinese University of Hong Kong (2007), an MPhil in Information Systems from Nanjing University (2000), and a BSc in Mathematics from Nanjing University (1997). His research focuses on virtual communities, smart cities, electronic business systems, and AI applications. Key themes include loyalty development in health communities, peer-to-peer energy trading platforms, and social media business models like TikTok's livestream commerce. He explores behavioral psychology in online environments and blockchain for e-government security. Recent work highlights include developing LSTM-based personality recognition models (2025), analyzing object localization in VQA systems (2024), and studying mobile health community dynamics in China (2024). His 2018 Science and Technology Progress Award recognizes contributions to systems analysis methodologies. He chairs the UK Academy for Information Systems and supervises PhD projects on AI-driven smart city applications. Current research integrates machine learning with energy market analysis and digital health interventions.

Stef De Sabbataمشاهده پروفایل

دانشیار

• Geographical Artificial Intelligence (GeoAI)
• Urban Analytics
• Generative Artificial Intelligence
• +۳ مورد دیگر

Stef De Sabbata is an Associate Professor of Geographical Information Science at the University of Leicester, holding dual affiliations with the School of Geography, Geology and the Environment and the Institute for Digital Culture. She serves as Chair of the Geographic Information Science Research Group at the Royal Geographical Society and previously chaired GISRUK 2018. Her research focuses on GeoAI, urban analytics, and cultural informatics, with notable contributions to graph neural networks in geodemographic classification and urban form analysis. Education includes a PhD in Geography from the University of Zurich (2013), and BSc/MSc in Computer Science from the University of Udine. Prior to Leicester, she held roles at the Oxford Internet Institute (OII) including Researcher (2013-2015), Junior Research Fellow at Wolfson College (2014-2015), and Research Associate (2015-2021). Her work explores spatially-explicit approaches to urban systems and the application of large language models in geographic contexts. Professional roles include membership in the International Cartographic Association's Commission on Location-Based Services and leadership in interdisciplinary initiatives like the GeoNet project examining Sub-Saharan Africa's knowledge economies. No specific awards are explicitly listed in the provided materials.

• Machine Learning
• Signal Processing
• Artificial Intelligence
• +۳ مورد دیگر

Akila Hewa Thondilege is a Postdoctoral Research Fellow at Queensland University of Technology (QUT), affiliated with the School of Electrical Engineering & Robotics. She is part of the Signal Processing, Artificial Intelligence, and Vision Technologies group. Her qualifications include a PhD from QUT and a B.Sc.Eng(Hons) in Computer Science and Engineering. Her research focuses on applying machine learning principles to solve complex problems in communications, defense, healthcare, sports, and infrastructure. Her work spans radar signal processing, medical imaging, and robotics applications. Recent contributions include developing radar spectrum detection datasets and multi-task learning frameworks for signal characterization. Earlier research explored semantic segmentation in multimodal images and aspect-based analysis of restaurant reviews. Akila’s publications reflect her expertise in machine learning, signal processing, and computer vision. She collaborates with teams addressing challenges in defense systems, healthcare monitoring, and robotic vision. While no academic awards are listed, her research consistently bridges theoretical advancements with practical applications in emerging technologies.

Vladimir Puzyrevمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Geophysics
• Numerical Modeling
• +۳ مورد دیگر

Vladimir Puzyrev is an Adjunct Research Fellow at Curtin University’s School of Earth and Planetary Sciences (EPS) and a Senior Research Scientist at Schlumberger-Doll Research. His work bridges geophysics, numerical modeling, and machine learning. He specializes in deep learning applications for geophysical inversion, electromagnetic exploration, and seismic facies classification. Research interests include: deep learning for geophysical data analysis, 3D modeling and inversion, isogeometric and finite element methods, and sparse linear solvers. His interdisciplinary approach integrates computational geosciences with advanced numerical techniques. Publications focus on applying neural networks to mineralization prediction, seismic facies classification, and electromagnetic inverse problems. Key contributions include frameworks for generative adversarial networks in geophysical modeling and Monte Carlo dropout for uncertainty quantification in geochemical data. Affiliations: Curtin University (Adjunct Research Fellow), Schlumberger Limited (Senior Research Scientist). Active in professional societies like SEG and EAGE. His work spans academic and industrial sectors, emphasizing real-world applications of computational geoscience.

• Graph Machine Learning
• Artificial Intelligence
• Deep Learning
• +۲ مورد دیگر

Yizhen Zheng is a Research Fellow at Monash University affiliated with Alfred Hospital, conducting interdisciplinary research at the intersection of artificial intelligence and medical applications. His core research interests include: Graph Machine Learning Artificial Intelligence Deep Learning Graph Neural Networks Molecular Property Prediction Analysis of his 2022-2025 publications reveals a focused trajectory in graph representation learning, with pioneering work on contrastive learning techniques that address both homophily and heterophily challenges in graph structures. His recent expansion into applying large language models for molecular property prediction demonstrates significant cross-disciplinary impact, with publications in top-tier venues including Nature Machine Intelligence and IEEE Transactions on Neural Networks and Learning Systems, accumulating substantial citations and media attention.

Reza Rafieeمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +۳ مورد دیگر

Dr. Reza Rafiee is a Lecturer (Education) in Data Analytics and Machine Learning at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, part of the Faculty of Engineering and Physical Sciences. He holds a PhD in Machine Learning from Newcastle University and is a Fellow of the Higher Education Academy. Education: PhD in Machine Learning, Newcastle University (2013) MSc in Artificial Intelligence and Robotics, Iran University of Science and Technology (2001) BEng (Hons.) in Computer Engineering, Iran Azad University (1998) Research Interests: AI in Education: Developing personalized learning tools, virtual reality for healthcare training, and data science pedagogy. AI in Health: Cancer genomics, tumor subgroup classification, and clinical diagnostic software (e.g., MIMIC and ICST). High-Throughput Genomic Analysis: RNA-Seq, DNA methylation, and bioinformatics pipeline development. Recent Article Trends: Focus on AI-driven solutions in medulloblastoma research (subgroup classification, relapse prediction) and educational innovations (VR-based learning, personalized pathways). His work bridges machine learning with clinical and educational applications. Scientific Awards: Fellow of the Higher Education Academy (2019) Queen's University Belfast Innovation in Teaching Fund (2025) STAR Recognition Award (2023) Teaching & Leadership: Leads the EEECS Academic Quality Assurance (2025) and advises the MSc AI program. Teaches courses like AI for Health and Principles of Machine Learning . Labs/Teams: Active in GitHub repositories including ICST (immune-based tumor classification) and MIMIC (DNA methylation diagnostics).

• Natural Language Processing
• Computational Linguistics
• Machine Translation
• +۷ مورد دیگر

Eneko Agirre is a Professor in the Department of Computer Science and Artificial Intelligence at the Faculty of Informatics, University of the Basque Country. He is a leading researcher in Natural Language Processing with a strong focus on multilingual systems, particularly for the Basque language, and has published extensively in top NLP conferences including ACL, EMNLP, and NAACL. His research spans multiple areas of computational linguistics including word sense disambiguation, machine translation, cross-lingual learning, and information extraction. Agirre has pioneered work on zero-shot learning approaches, data contamination issues in LLM evaluation, and low-resource language processing. His recent work includes developing the Latxa family of Basque language models and creating novel methods like WiCkeD for more challenging benchmarks and GUIDEX for zero-shot information extraction. Analysis of his recent publications reveals a strong trend toward addressing fundamental challenges in large language model adaptation, evaluation reliability, and cross-lingual transfer learning. His work often combines theoretical insights with practical applications, particularly for under-resourced languages like Basque. His research demonstrates how linguistic typology impacts cross-lingual performance and how to overcome data limitations through innovative methodology. Agirre has mentored numerous researchers who have become active contributors to the NLP field, including Oscar Sainz, Jon Ander Campos, and Iker García-Ferrero. His collaborative work spans institutions across Spain and internationally, reflecting his standing in the global NLP community.

Brendan Wrightمشاهده پروفایل

پژوهشگر

• Photovoltaics
• Machine Learning
• Computational Modelling
• +۲ مورد دیگر

Dr. Brendan Wright is a researcher at the University of New South Wales , affiliated with the Faculty of Engineering and the School of Photovoltaic and Renewable Energy Engineering . His work bridges photovoltaics and machine learning , focusing on electrochemical defect mechanisms in silicon solar cells and generative representation learning for complex physical systems. With a PhD in Physics (2018) and a B. Nanotechnology Honours (2011) from the University of Wollongong, his research spans charge carrier dynamics in organic PV systems to computational chemistry. Research Interests: Machine learning applications for utility-scale photovoltaic systems State-space dynamics modelling using generative algorithms End-of-life classification of solar panels Deep learning for electroluminescence image analysis Surface polarization effects in silicon solar cells Grants: Australian Renewable Energy Agency (ARENA) R&D Project Grant (RG220681, 2022) Australian Centre for Advanced Photovoltaics (ACAP) Industrial Collaboration Grant (2022) Scientific Contributions: Developed polarizable force field models for polymer electrolytes Created deep learning frameworks for photovoltaic defect analysis Innovated perspective correction algorithms for solar module imaging Applied latent ODE networks to predict PV degradation trends Advising: Currently supervises Chukwuka Madumelu in photovoltaic engineering and provides secondary guidance to doctoral candidates. His mentorship extends to computational material science and AI-driven energy systems research.