جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Wei Jia is a Professor at the School of Computer and Information, Hefei University of Technology, China. Their research focuses on artificial intelligence, machine learning, computer vision, and robotics, with contributions to knowledge graphs, biometric systems, and autonomous systems. They have co-authored over 130+ publications in top-tier journals and conferences, including venues like IEEE Transactions, CVPR, and AAAI. Research interests span deep learning techniques, graph neural networks, and optimization for large-scale systems. Notable work includes entity extraction frameworks, safety analysis in engineering systems, and swarm control algorithms for unmanned vehicles. Contributions also extend to data management systems, such as the TierBase key-value store and the OVERLORD data loader for foundation models. Publications highlight interdisciplinary applications in cybersecurity, robotics, and biomedical imaging. Their work often bridges theoretical advancements with practical implementations, addressing challenges in both software and hardware systems. No specific awards or grants are listed in the provided text.

• Biomedical Informatics
• Machine Learning
• Graph Neural Networks
• +۴ مورد دیگر

Yi-Jia Zhang is a Professor at the School of Computer Science and Technology, Dalian University of Technology. They hold affiliations with multiple institutions, including Zhejiang Sci-Tech University and Jilin University. Their research focuses on biomedical informatics, machine learning, and healthcare applications, with significant contributions to medical knowledge fusion, drug recommendation systems, and multimodal analysis. Over 140 publications since 2011 reflect expertise in areas like graph neural networks, natural language processing, and medical image analysis. Key collaborations include work with Hongfei Lin, Mingyu Lu, and Jian Wang on projects such as drug-repositioning models and radiology report generation frameworks. A notable emphasis is placed on applying AI techniques to solve biomedical challenges, including ICD code classification and sentiment analysis in healthcare contexts. No formal awards are listed, but their extensive publication record underscores academic impact.

• Machine Learning
• Artificial Intelligence
• Data Mining
• +۴ مورد دیگر

Feng Jiang is a Professor at Nanjing University's School of Computer Science and Technology, Department of Computer Science and Technology. With an extensive publication record spanning from 1999 to 2025, Professor Jiang has established himself as a prominent researcher in multiple computer science domains. His research interests include Machine Learning, Artificial Intelligence, Data Mining, Software Engineering, and Computer Vision. Professor Jiang's work demonstrates significant interdisciplinary applications, ranging from healthcare and bioinformatics to industrial manufacturing and agricultural technology. His research approach often combines theoretical advancements with practical implementations, addressing real-world challenges across diverse sectors. Analysis of his recent publications reveals a strong focus on deep learning applications, graph neural networks, and ensemble methods. His work shows consistent contributions to both theoretical frameworks and practical implementations, with particular emphasis on software engineering applications, computer vision systems, and biomedical informatics. The breadth of his research indicates active collaborations across multiple institutions and disciplines. Professor Jiang has supervised numerous research projects resulting in publications across top-tier journals including IEEE Access, Expert Systems with Applications, and Knowledge-Based Systems. His work has been consistently published in high-impact venues, demonstrating sustained research productivity and relevance in the field.

• Data Science
• Computer Vision
• Artificial Intelligence
• +۷ مورد دیگر

Alessandro Sebastian Russo is a PhD student in Computer and Systems Engineering at Politecnico di Torino , with a focus on scalable architectures for neurosymbolic AI in scene interpretation and generation. He is affiliated with the Department of Control and Computer Science (DAUIN) and part of the GRAINS group (Graphics and Intelligent Systems). His academic role includes Lecturer responsibilities as an external teaching assistant. His educational background includes a Master's degree in Data Science (2021) from Politecnico di Torino, with thesis work on Machine Learning and Transformer Neural Networks. Prior to his PhD, he completed a one-year research fellowship in medical machine learning. Russo's research bridges neural and symbolic AI through Logic Tensor Networks (LTNs) , aiming to enhance scene understanding for autonomous driving applications. Current work explores integration of Large Language Models (LLMs) and Commonsense Knowledge Bases to improve logical consistency. His teaching experience includes collaborations on courses in Mobile Application Development and Innovation Management . Key publications span medical imaging ( Medical Image Analysis ), neurosymbolic learning (NeSy workshops), and computer vision (IJCNN, ISVC). He is also an inventor on the national patent Deep Neural Networks for Multi-View Mammography Classification . No scientific awards are currently listed in the available information.

Angus Robertsمشاهده پروفایل

مدرس ارشد

• Natural Language Processing (NLP)
• Health Informatics
• Electronic Health Records (EHR)
• +۶ مورد دیگر

Dr. Angus Roberts is a Senior Lecturer in Health Informatics at King's College London's Institute of Psychiatry, Psychology & Neuroscience (IoPPN), within the Biostatistics & Health Informatics department. He joined in January 2018 after transitioning from biomedical science to computer science and NHS software development. His research focuses on applying natural language processing (NLP) to electronic health records (EHR), particularly in mental health contexts. Education: PhD in NLP from University of Sheffield, with prior work on the GATE NLP framework. Career: Former NHS biomedical scientist and software developer, leading to an interest in medical terminology and EHR analysis. Research Interests: NLP applications in healthcare, knowledge representation, and EHR textual data analysis. Specific projects include developing pain assessment models, analyzing violence reports in mental health records, and creating clinical decision support systems like CogStack. Collaborations include the RE-STAR project on adolescent mental health resilience and the VISION consortium on violence health impacts. Key Publications (2023-2025): Systematic reviews on multimodal EHR analysis, NLP models for pain and recovery detection, and frameworks like VIEWER for mental health visual analytics. Over 977 citations reflect contributions to clinical NLP and health informatics. Grants/Projects: Lead or co-investigator on projects including Advance Choice Documents Implementation, VISION: Violence, Health and Society, and the UK's HDR UK National Text Analytics initiative. Lab/Teams: Active in the Biostatistics & Health Informatics department and CogStack development team, collaborating across King's Health Partners and NHS trusts.

Tao Wangمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Health Data Science
• Multimodal Learning
• +۳ مورد دیگر

Dr. Tao Wang is a Research Fellow at King’s College London (KCL), affiliated with the Institute of Psychiatry, Psychology & Neuroscience (IoPPN) and the Department of Biostatistics & Health Informatics. He holds honorary research positions at leading UK hospitals and serves on Meta/Facebook’s Global Safety Policy Advisory Board on Mental Health and King’s AI Institute. His research focuses on advancing Natural Language Processing (NLP), Network Science, Multimodal Data Modeling, and AI applications in healthcare, public health, sociology, and law. He completed his PhD at the University of Southampton (2015–2018) and has contributed to over 45 publications, securing prestigious awards like the Alan Turing Fellowship and the HSJ Partnership Award for Best Mental Health Partnership. Education: PhD in Health Informatics, University of Southampton (2015–2018), supported by the UK ESRC Research Exchange at the University of Western Australia Research Interests: Natural Language Processing (NLP) for healthcare data Knowledge Graphs and Causal Inference in health analytics Clinical Decision Support Systems Multimodal Learning and AI ethics Key Projects: VIEWER: a visual analytics framework for mental healthcare Clinical Decision Support Systems using CogStack Pain analysis via knowledge graph embeddings Awards: Chair’s Prize at NIHR Maudsley BRC conference (2019) Early Career Research Award (2022) HSJ Best Mental Health Partnership Award (2022) HETT Best Data Innovation Award (2022) Labs & Teams: Precision Health Informatics Data Lab (IoPPN), King’s AI Institute.

Daniel Beanمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Electronic Health Records
• Medical Informatics
• +۳ مورد دیگر

Daniel Bean is a researcher specializing in Natural Language Processing (NLP) and Electronic Health Records (EHRs) , with a focus on Medical Informatics and Clinical Data Modeling . His work intersects Artificial Intelligence and Healthcare Innovation , particularly in extracting value from large-scale patient data. Research Themes Transformer-based generative models for patient timeline prediction Scalable NLP systems for hospital-wide EHR summarization Knowledge distillation frameworks for hospitalization outcome prediction Statistical methodologies in trial emulation for medical research Project Leadership : Bean leads research initiatives funded by BHF (British Heart Foundation, 2023-2025) for AI in heart failure detection MRC (Medical Research Council, 2 scholarly years) on knowledge graph learning EPSRC (short-term 2021 grant) for patient outcome modeling Academic Impact : With over 1000 citations , his work appears in top journals like The Lancet Digital Health , PLOS Digital Health , and IEEE Journal of Biomedical and Health Informatics . His research has been blogged , retweeted by 60+ social media users , and discussed in 32 news outlets .

David Koslickiمشاهده پروفایل

دانشیار

• Bioinformatics
• Genomics
• Computational Biology
• +۴ مورد دیگر

David Koslicki is an Associate Professor at the Department of Biology, Pennsylvania State University, with affiliations in Computer Science and Engineering and the Huck Institute of the Life Sciences. He holds a BS in Theoretical Mathematics from Washington State University (2006) and a PhD in Mathematics from Penn State (2012). His postdoctoral training includes roles at Drexel University and the Mathematical Biosciences Institute. His research focuses on developing algorithms for analyzing high-throughput genomic data, particularly metagenomics. Key interests include compressive sensing, probabilistic methods, and optimization for microbial community analysis. He leads the CAMI benchmarking initiative and co-organizes the Computational Genomics Summer Institute. Notable awards include the 2018 College of Science Impact Award and teaching recognitions from Oregon State and Penn State. Education: BS in Theoretical Mathematics, Washington State University (2006) PhD in Mathematics, Penn State (2012) His lab's work spans algorithm development for metagenomic analysis, microbial community dynamics, and synthetic microbiota studies. Recent projects include FracMinHash-based similarity metrics, Prokrustean graph methods, and tools like sourmash v4. Collaborations emphasize interdisciplinary computational biology and standardization efforts. Awards: 2018 College of Science Impact Award (Oregon State) 2016 Carter Award Nominee (Oregon State) 2011 Teaching Recognition (Penn State) His contributions include software frameworks for metagenomic benchmarking (CAMI) and translational medicine tools (ARAX, RTX-KG2). Active in knowledge graph development and drug discovery applications through initiatives like the Biomedical Data Translator Program.

Jian Quمشاهده پروفایل

استاد

• Knowledge Graphs
• Question Answering
• Natural Language Processing
• +۲ مورد دیگر

Professor Jian Qu is affiliated with the University of Hamburg as a Petra Kappert Fellow at the Centre for the Study of Manuscript Cultures (CSMC). His research focuses on computational approaches to manuscript analysis, particularly knowledge graph question answering, ontology learning, and text-graph representations. He has made significant contributions to natural language processing techniques applied to scholarly data and biomedical text analysis. Qu's research bridges artificial intelligence with cultural heritage preservation, developing methods to extract and structure knowledge from historical documents. His recent work involves hybrid prompt learning for knowledge graph generalization and benchmarking question answering systems across diverse datasets. The interdisciplinary nature of his work spans computational linguistics, semantic web technologies, and digital humanities. His publications demonstrate consistent focus on improving knowledge extraction and question answering systems, with recent emphasis on generalization across datasets and domains. Methodological innovations include attention-based neural networks for biomedical classification and unsupervised approaches for ontology construction from textual data.

• Deep Learning
• Medical Imaging
• Artificial Intelligence
• +۳ مورد دیگر

Chengjia Wang is an Assistant Professor at the School of Mathematical & Computer Sciences, Heriot-Watt University, within the Computer Science department. He joined the Edinburgh Centre for Robotics in 2021 after working at the BHF Centre for Cardiovascular Science (CVS), University of Edinburgh. He holds a PhD from the University of Edinburgh and Toshiba Medical Visualization System, funded by the Scottish Universities Physics Alliance (SUPA). His education includes a European Erasmus Mundus Master in Vision and Robotics, along with two MSc degrees from Heriot-Watt University and the University of Burgundy. His research focuses on deep learning, medical imaging, and AI applications in healthcare, contributing to UN Sustainable Development Goals. His research interests span medical image analysis, machine learning methodologies, and their applications in healthcare diagnostics. Recent work includes studies on bone marrow adiposity using deep learning and genome-wide association analyses, as well as reviews on attention mechanisms in medical imaging. He is actively involved in editorial roles, such as with Frontiers in Computational Neuroscience , and has published widely on topics like image segmentation, graph neural networks, and cross-modal learning frameworks. No scientific awards are explicitly mentioned in the provided texts. Dr. Wang is accepting PhD students and has contributed to collaborative projects at the intersection of robotics, cardiovascular science, and computational neuroscience. His work emphasizes interdisciplinary approaches to advancing medical AI and healthcare technology.

Marcus Klangمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Marcus Klang is a Research Engineer at the Department of Computer Science , Lund University , affiliated with the Robotics and Semantic Systems research group. He contributes to initiatives like eSSENCE (e-Science Collaboration) and ELLIIT (Linköping-Lund IT and mobile communication initiative). His work focuses on automatic methods for identifying and connecting entities in natural language text, leveraging multilingual open data sources like Wikipedia and knowledge graphs like Wikidata. Research Areas: Natural Language Processing, Machine Learning, Entity Linking, Knowledge Graph Construction Key Contributions: Development of scalable text processing systems for biomedical domains His research aligns with UN Sustainable Development Goals in health and information systems. He actively collaborates in projects such as AI-based text mining for medicine and leads technical development in named entity recognition frameworks like EasyNER. Recent projects include: Artificial intelligence-based text mining for medical domains Biomedical text mining for systems biology AI Lund research network He has participated in public outreach activities like Robotics Week for schools and interdisciplinary workshops on AI ethics.

Pietro Panzarasaمشاهده پروفایل

استاد

• Social Networks
• Innovation Studies
• Health Informatics
• +۴ مورد دیگر

Pietro Panzarasa is a Professor of Networks and Innovation at Queen Mary University of London (QMUL), School of Business and Management. He also holds visiting professorships at IMT School for Advanced Studies Lucca (Italy), Imperial College London’s Centre for Complexity Science, and Cambridge Networks Network (CNN). He earned his PhD in Business Administration from Bocconi University. His research focuses on social capital, innovation, and network science, with applications to healthcare, urban dynamics, and global trade. Education: PhD in Business Administration and Management, Bocconi University, Italy Research Interests: Structural foundations of social capital in networks Online health communities and digital health Global value chains and trade networks Geo-social networks and urban sociology Network methodology (e.g., rich-club effect, multiplex networks) Key Themes in Articles: His work bridges computational social science, network theory, and big data. Recent themes include early warning systems for pandemics via social media, startup success prediction, Brexit’s impact on research collaboration, and urban social diversity. Awards: 2018 NHS Trust Collaboration Award (runner-up) 2016 Best Paper Award (Imperial College SIAM) 2009 Best Paper at International Conference on Complex Sciences Grants & Partnerships: He has secured funding from Wellcome Trust, EPSRC, and others. Collaborators include AstraZeneca, HealthUnlocked, and the Royal College of Surgeons. Labs/Teams: He directs the MRes Programme and co-leads the Business Ecosystems Research Group (BERG) and Centre for Globalisation Research (CGR).

• Data Management
• Reproducibility in Science
• Semantic Web Technologies
• +۳ مورد دیگر

Dr. Tomasz Miksa is a Senior Scientist at TU Wien's Department of Data Science, specializing in machine-actionable data management plans (maDMPs), reproducibility frameworks, and data stewardship. His work bridges semantic web technologies, automated workflows, and interdisciplinary data governance. He leads projects like OS Trails (2024–2027) and FAIR-AI , focusing on synchronizing research data services and ensuring FAIR (Findable, Accessible, Interoperable, Reusable) principles. Miksa holds a PhD in Computer Science from TU Wien (2016) and has supervised numerous theses on topics like reproducible query processing and DMP integration. He actively contributes to initiatives such as FAIR Data Austria and openEO API standardization, emphasizing sustainable data practices across disciplines. Research interests include semantic representation of DMPs, automated validation tools, and frameworks for auditable privacy-preserving data analysis. His work on data citation mechanisms and evolving database schemas ensures long-term reproducibility in fields like environmental science and earth observation. Miksa collaborates internationally, co-editing special collections on data management planning and publishing in journals like ACM Transactions on Management Information Systems and IEEE eScience . Key contributions include the DCSO ontology for DMPs, the WellFort platform for privacy-aware data analysis, and frameworks for identifying software dependencies in biomedical workflows. His teaching focuses on data stewardship, with courses like Data Stewardship VO/UE addressing national and institutional strategies. Ongoing efforts aim to integrate machine-actionable DMPs with institutional repositories and cloud-based research infrastructures.

Renata Raidouمشاهده پروفایل

دانشیار

• Medical Visualization
• Visual Analytics
• Uncertainty Visualization
• +۳ مورد دیگر

Renata Raidou is an Associate Professor in Biomedical Visualization and Visual Analytics at the Research Unit of Computer Graphics, Institute of Visual Computing & Human-Centered Technology, TU Wien, Austria. She previously held positions at the University of Groningen and TU Wien as a postdoc. Her research focuses on medical visualization, uncertainty visualization, and data physicalization for healthcare applications. Raidou has received prestigious awards including the Dirk Bartz Prize (2017) and EuroVis Young Researcher Award (2022). She coordinates TU Wien's BSc in Digital Health and MSc in Medical Informatics. Education: PhD in Medical Visualization (Eindhoven University of Technology, 2017), MSc in Biomedical Engineering (TU Delft), Diploma in Electrical and Computer Engineering (NTUA). Professional Roles: Editorial Board member of Computer & Graphics , Steering Committee member of EG VCBM, and member of EASAC's AI in Healthcare working group. Research Interests: Medical visualization strategies for P4 medicine, anatomical edutainment through physicalization, and uncertainty-aware visual analytics in radiotherapy. Her work bridges visual computing, machine learning, and medical applications, emphasizing clinical decision support and public health education. Awards: Over 10 awards including Best Paper recognitions at IEEE Vis and EuroVis, and the EuroVis Best PhD Award. Her contributions span visualizing anatomical variability, ensemble data exploration, and interactive medical training tools. Grants & Projects: Leads the Health Virtual Twins project (2024–2028) for personalized stroke management. Active in interdisciplinary collaborations, including the Shonan Meeting on Formalizing Biological Visualization. Labs/Teams: Head of the Visualization Group at TU Wien, developing tools like Vologram (holographic medical data physicalization) and Slice and Dice anatomical crafts. Collaborates on edutainment systems and AI-driven healthcare visualization.

Renata Georgia Raidouمشاهده پروفایل

دانشیار

• Medical Visualization
• Visual Analytics
• Uncertainty Visualization
• +۴ مورد دیگر

Renata Georgia Raidou is an Associate Professor in Biomedical Visualization and Visual Analytics at TU Wien's Institute of Visual Computing & Human-Centered Technology. She leads the Research Unit of Computer Graphics and serves as Curriculum Coordinator for the Bachelor in Informatics (Specialization Digital Health) and Master in Medical Informatics programs. She holds prestigious awards including the EuroVis Young Researcher Award (2022), Best PhD Award (2018), and Dirk Bartz Prize (2017). Her research focuses on medical applications of Visual Analytics, with emphasis on uncertainty visualization, comparative visualization, and anatomical edutainment through physicalizations. She explores how visual tools can enhance decision-making in precision medicine, particularly in radiotherapy and cancer treatment. Her work bridges visualization, machine learning, and image processing to address clinical challenges. Raidou's recent projects include developing predictive visual analytics systems for radiotherapy planning and creating tactile physicalizations for anatomy education. She actively contributes to the field through editorial roles (Associate Editor of Computer & Graphics ) and policy advising via EASAC's 'AI in Healthcare' initiatives. Key Projects: Health Virtual Twins for Stroke Management, PREVIS for Radiotherapy Decision Support, Pelvis Runner for Anatomical Variability Analysis Teaching: Courses on Medical Visualization, Data Analytics in Health Sciences, and Visual Computing Grants: European Commission-funded projects (2024–2028) Her lab develops interdisciplinary tools for clinicians, researchers, and educators, emphasizing both technical innovation and real-world impact.