جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Natural Language Processing
• Quantum Computing
• +۷ مورد دیگر

Dimitris Gkoumas is a Postdoctoral Research Assistant at Queen Mary University of London (QMUL), specifically within the School of Electronic Engineering and Computer Science. His role involves advancing interdisciplinary research at the intersection of computer science, artificial intelligence, and healthcare. His research interests focus on AI-driven healthcare solutions, quantum computing-inspired machine learning, natural language processing (NLP), and multimodal data fusion. Key areas include developing longitudinal datasets for dementia diagnosis, applying large language models (LLMs) to clinical tasks like suicidality risk detection, and exploring quantum theory for decision-making systems in information retrieval and sentiment analysis. His work bridges technical innovation with societal impact, such as analyzing parliamentary debates (ParlaMint) and investigating xenophobic attitudes via social media. He also contributes to collaborative platforms like the European Language Grid, promoting cross-lingual NLP advancements. While no formal awards are listed, his publications reflect a strong commitment to cutting-edge research. His advising and grants activities remain unspecified in the provided texts. Dimitris has contributed to educational technology through projects like HistoryLand, a game for primary education history learning.

• Natural Language Processing
• Artificial Intelligence
• Mental Health Analysis
• +۳ مورد دیگر

Jenny Chim is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. Her research focuses on Natural Language Processing (NLP), Multilingual Language Models, and their applications in mental health analysis, biomedical informatics, and social media data. She contributes to large-scale projects like the BigScience initiative, developing open-access multilingual models and evaluating synthetic data generation techniques. Her work spans cross-cultural linguistic challenges, clinical insights from social media timelines, and benchmarking code generation systems. Key contributions include the BigScience ROOTS Corpus and the Bloom model, emphasizing ethical AI and global accessibility. She actively organizes shared tasks like clpsych to advance clinical NLP applications, particularly in identifying mental health risks through user-generated content. Research interests also include hierarchical VAE integration with LLMs for clinical timeline summarization, contamination detection in shared tasks, and frameworks for biomedical NLP like BigBio. Her work bridges technical innovation with real-world healthcare applications, advocating for culturally inclusive AI systems.

• Neuroimaging
• Brain Mapping
• Computational Neuroscience
• +۴ مورد دیگر

Paul M. Thompson, PhD is a Professor at the Keck School of Medicine of the University of Southern California, with appointments across 8 departments including Neurology, Psychiatry, Biomedical Engineering, and Quantitative & Computational Biology. He leads the ENIGMA Consortium , a global network of 2500+ scientists across 47 countries conducting large-scale neuroimaging-genetics studies of 30+ brain disorders. His research integrates neuroimaging, mathematics, computer science , and clinical medicine to map brain diseases and develop AI tools for early detection. Education: MA in Mathematics & Classics (Oxford), PhD in Neuroscience (UCLA) Labs: Director of the USC Imaging Genetics Center (50+ researchers) and $11M NIH ENIGMA Center for Worldwide Medicine, Imaging & Genomics Key research areas include: Alzheimer’s Disease : Identifying genetic risk variants and AI-driven subtyping for novel treatment targets Psychiatric Disorders : Mapping brain changes in schizophrenia, depression, OCD, and bipolar illness Developmental Neuroscience : Tracking brain growth in children and fetal programming effects Global Health : India ENIGMA Initiative studying brain aging in diverse populations His recent publications focus on deep learning for MRI analysis , diffusion models for synthetic imaging , and genetic correlates of brain structure in journals like Nature Genetics , Science , and Nature . Major Awards : 2023 Society for Brain Mapping & Therapeutics: Pioneer in Medicine Award 2021 U.S. Alzheimer Association: Zenith Award 2015 Kent Innovations in Academia Award Leadership : Director, ENIGMA Center for Worldwide Medicine ($11M NIH) Director, AI4AD ($18M NIH Initiative) Founder, India ENIGMA Initiative

Frank van Harmelenمشاهده پروفایل

استاد

• Artificial Intelligence
• Semantic Web
• Knowledge Representation
• +۳ مورد دیگر

Prof. Frank van Harmelen is a Full Professor at Vrije Universiteit Amsterdam, holding positions in the Faculty of Science (Artificial Intelligence), Network Institute, and Knowledge Representation and Reasoning department. He also serves as an Adjunct Professor at Wuhan University of Science and Technology and Wuhan University Faculty of Information Sciences since 2014 and 2015, respectively. He earned his PhD in Artificial Intelligence from the University of Edinburgh (1985–1989), focusing on meta-level inference efficiency. His research spans Semantic Web, Knowledge Representation, Ontology Engineering, and neurosymbolic AI, with contributions to UN Sustainable Development Goals related to technological innovation. His recent work emphasizes knowledge graphs (e.g., KG4NH for nutrition/health, OfficeGraph for IoT), neurosymbolic integration, and applications in healthcare, sustainability, and education. He has led projects like the HumanE AI Network and contributed to datasets such as MetaLink and equivalence class analyses in LOD. Awards: Fellow of European AI Society (2007), Member of Royal Dutch Academy of Sciences (2017), Ten Year Impact Award (2012) Activities: Steering roles in international conferences, member of prestigious academies, and involvement in EU/US Agent Markup initiatives. Teaching: Courses on AI in Health and Introduction to AI. Grants/Projects: Principal Investigator for HumanE AI Network (2020–2024), contributed to IoT and smart grid solutions. His 292+ publications include foundational texts like the Semantic Web Primer and recent advancements in knowledge graph completion and guideline-informed reinforcement learning.

Anders Eklundمشاهده پروفایل

دانشیار

• Medical Image Analysis
• Machine Learning
• Medical Informatics
• +۳ مورد دیگر

Anders Eklund is a Senior Associate Professor at Linköping University, affiliated with both the Department of Biomedical Engineering (IMT) and the Department of Computer and Information Science (IDA). He holds roles in the Center for Medical Image Science and Visualization (CMIV), focusing on developing advanced methods for medical image analysis, including applications in pediatric brain tumor classification, synthetic image generation, and federated learning frameworks. His research bridges biomedical engineering, artificial intelligence, and data science to address clinical challenges such as improving diagnostic accuracy and reducing healthcare resource bottlenecks. Education: Master of Science (2007), PhD in Medical Informatics (2012), Postdoc at Virginia Tech (2012–2014), Docent (2016), Associate Professor (2021). Research Interests: Medical image processing, deep learning for healthcare, federated learning for sensitive data, synthetic medical image generation, and statistical validation of neuroimaging techniques. Notable contributions include exposing flaws in fMRI analysis software and advancing AI-driven solutions for radiation treatment planning. Publications: Recent work emphasizes deep learning applications in medical imaging, including tumor classification, federated learning for privacy-preserving models, and synthetic image techniques to address data scarcity. His articles reflect a focus on interdisciplinary solutions for healthcare challenges, such as reducing queues post-pandemic through automated brain image analysis. Grants & Advising: Secured a 3.6M SEK grant from the Swedish Research Council (2016). Supervised numerous PhD students, including main supervision of Xuan Gu (2019) and co-supervision of Per Sidén (2020). Active in training the next generation of biomedical engineers and AI researchers. Labs & Collaborations: Leads projects within CMIV and collaborates across departments at Linköping University. Involved in initiatives like the ASSIST project, which aims to automate brain image analysis to free clinician time.

• Medical Statistics
• Clinical Trial Design
• Longitudinal Data Analysis
• +۴ مورد دیگر

Nick Parsons is a Professor (Research Focussed) at the University of Warwick's Warwick Medical School, affiliated with the Warwick Clinical Trials Unit. He holds a MSc in Statistics from Warwick University and a PhD in Applied Mathematics from Queen Mary, University of London. His research focuses on medical statistics, clinical trial design, and applications in Trauma & Orthopaedics. He has over 20 years' experience as a statistical consultant, collaborating across biological sciences, medicine, and industry. Key research interests include experimental design/analysis, longitudinal data analysis, ordinal regression models, R programming, and digital image processing using neural networks. He has contributed to major studies like START:REACTS, WHiTE, and FASHIoN RCT, evaluating surgical and therapeutic interventions. His work integrates statistical rigor with clinical practice, emphasizing adaptive trial designs and cost-effectiveness analyses. He has led numerous grants including NIHR-funded projects on clinical trials, fracture management, and musculoskeletal outcomes. Active in professional societies (International Biometric Society, Royal Statistical Society), he advocates for methodological innovation in healthcare research. Key Projects: START:REACTS (rotator cuff trials), WHiTE (hip fracture evaluation), FASHIoN RCT (hip impingement) Grants: NIHR awards for clinical trials, trauma studies, and musculoskeletal research Labs/Teams: Warwick Clinical Trials Unit, NDORMS Oxford collaborations

Mohsen Fatehifarمشاهده پروفایل

پژوهشگر

• Speech Recognition
• Text-to-Speech Technologies
• Automatic Speech Recognition
• +۵ مورد دیگر

Mohsen Fatehifar is a Researcher in the Division of Psychology Communication and Human Neuroscience. His work focuses on interdisciplinary research at the intersection of speech technologies, healthcare, and biomedical informatics. He holds a Doctor of Philosophy and has published peer-reviewed articles in journals like the International Journal of Audiology and the Journal of Biomedical Informatics. Education: PhD in Psychology Communication and Human Neuroscience. Research Interests: Mohsen’s research spans Speech Recognition, Text-to-Speech systems, and their clinical applications in hearing assessment. He also explores machine learning techniques for drug interaction analysis. His work integrates engineering principles with healthcare challenges, such as sensory aid development and automated diagnostic tools. Recent Work Trends: Recent articles highlight advancements in automated hearing tests using ASR and TTS technologies, as well as innovative methods for drug interaction extraction via attention mechanisms. These studies emphasize practical applications in healthcare and biomedical informatics. Collaborations: Collaborations involve multidisciplinary teams, though specific institutional partnerships remain unspecified. Labs/Teams: No dedicated lab or team is explicitly mentioned, though his research suggests involvement in interdisciplinary groups.

• Biomedical informatics
• mHealth
• Artificial intelligence in medicine
• +۳ مورد دیگر

Alex Anh-Tuan Bui serves as Professor of Radiological Sciences and Associate Dean of Biological Chemistry at the University of California Los Angeles (UCLA) David Geffen School of Medicine. He is the Director of the Medical & Imaging Informatics (MII) group and holds the David Geffen Chair in Informatics. Dr. Bui leads UCLA's Bridge2AI Coordination Center and serves as Senior Associate Director for Informatics at UCLA's Clinical and Translational Science Institute (CTSI). PhD in Computer Science, UCLA (2000) BA in Computer Science, UC Berkeley (1995) Dr. Bui's research focuses on biomedical informatics, mHealth, and artificial intelligence in medicine. His work bridges contemporary computational approaches with biomedical observations and electronic health records (EHRs), addressing translational challenges in healthcare. He develops distributed information architectures, methodological approaches for AI/ML in healthcare, and data visualization techniques. His research spans multiple domains including predictive modeling for chronic diseases, natural language processing of clinical text, and real-time health monitoring systems. Analysis of Dr. Bui's recent publications (2021-2024) reveals a strong emphasis on applying machine learning to clinical problems across diverse medical domains. His work demonstrates expertise in developing predictive models for renal disease, cardiovascular outcomes, cancer screening, and substance use disorders. A recurring theme is the application of advanced AI techniques to electronic health records, with particular attention to data quality issues, model interoperability, and addressing health disparities. His research increasingly incorporates real-time data collection through mobile and wearable technologies. Dr. Bui has led numerous significant research initiatives including the NIH-funded Los Angeles PRISMS Center focused on mHealth informatics, and currently directs UCLA's Bridge2AI Coordination Center. He serves as Program Director for multiple NIH training grants (TL1/T15/T32) in biomedical informatics and data science, and is Director for the Medical Informatics Home Area in UCLA's Graduate Program in Biosciences. As Director of the Medical & Imaging Informatics group, Dr. Bui oversees research on the Biomedical REAl-Time Health Evaluation (BREATHE) platform and other initiatives focused on integrating AI with clinical workflows. His team develops tools for clinical decision support, patient portals, and imaging informatics that bridge the gap between computational methods and practical healthcare applications.

Saeed Shiry Ghidaryمشاهده پروفایل

مدرس

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

Dr. Saeed Shiry Ghidary is a Lecturer at Staffordshire University’s Digital, Tech, Innovation & Business department. He holds a Ph.D. in Robotics and Intelligent Systems from Kobe University (Japan), alongside M.Sc. and B.Sc. degrees in Computer Architecture and Electronics from Amirkabir University of Technology (Iran). His research focuses on interdisciplinary areas including Machine Learning, Robotics, AI, Computer Vision, and Cognitive Science. He has led international research teams, including chairing the Amirkabir Robotic Center, and contributed to over 20 peer-reviewed publications. **Education:** Ph.D., Robotics and Intelligent Systems, Kobe University, Japan (2002) M.Sc., Computer Architecture, Amirkabir University, Iran (1994) B.Sc., Electronics Engineering, Amirkabir University, Iran (1990) **Research & Teaching:** Dr. Ghidary specializes in AI and robotics education, teaching graduate and undergraduate courses. His research emphasizes applications like BCI systems, EEG signal processing, and kernel methods for dimensionality reduction. He has collaborated with institutions in Japan, France, Australia, and the U.S., and serves as an HEA Fellow. **Awards & Recognition:** HEA Fellowship **Consulting & Industry:** He advises industries on AI, robotics, and automation for sectors like banking, insurance, and oil production, focusing on efficiency and profitability through technological integration.

David Schneiderمشاهده پروفایل

استادیار

• Surgical Decision-Making
• Biomedical Informatics
• Machine Learning in Healthcare
• +۳ مورد دیگر

Dr. David Schneider is an Assistant Professor in the Department of Surgery (Endocrine Division) at the University of Wisconsin School of Medicine and Public Health. His research focuses on improving clinical decision-making through informatics tools, including machine learning and natural language processing applied to surgical outcomes and endocrine care. He leads the Dr. Schneider’s Lab , which collaborates with the Wisconsin Surgical Outcomes Program to optimize surgical care for endocrine conditions like hyperparathyroidism and thyroid cancer. Education: MD, MS (degree details not explicitly stated in provided texts, but inferred from lab staff listing). His work emphasizes data-driven approaches to address challenges such as delayed diagnosis of hyperparathyroidism, post-thyroidectomy hormone management, and disparities in healthcare access. Research interests include: Machine learning for predictive analytics in surgery NLP applications for clinical data extraction Quality improvement in endocrine surgery Healthcare equity analysis Grants and Awards: AAES Paul LoGerfo Award (2018–2019) ICTR Novel Methods Pilot Award (2019–2021) Labs/Teams: Principal Investigator of the Schneider Lab, which develops tools like the Thyroid Hormone Dosing App to aid clinical decisions. Collaborates with multidisciplinary teams on surgical quality improvement initiatives.

• Bioinformatics
• Machine Learning
• Health Informatics
• +۳ مورد دیگر

Dr. Matloob Khushi is an academic staff member at the University of Sydney's School of Computer Science, where he has held roles since joining in 2017. He earned his Ph.D. from the University of Sydney and has published over 50 research papers. His research focuses on big data analysis, machine learning applications in health, finance, and genomics, with notable contributions to adverse drug event prediction, bioinformatics tools, and predictive modeling in healthcare. Dr. Khushi's work has been recognized in media for innovations like the MatCol tool aiding cancer research. Education: Ph.D. in Computer Science, University of Sydney (2008). Research Interests: Machine learning for big data, biomedical informatics, financial analytics, and health informatics. Recent articles highlight advancements in graph neural networks, predictive models for chronic diseases, and tools for genomic analysis. He advises two Ph.D. students and collaborates on interdisciplinary projects.

Benjamin Brackeمشاهده پروفایل

پژوهشگر

• Machine Learning
• Medical Imaging
• Computer Vision
• +۳ مورد دیگر

Benjamin Bracke, M.Sc., is a researcher with a strong focus on interdisciplinary applications of machine learning and computer vision. His work spans medical imaging, biodiversity informatics, and educational technology. He has contributed to projects such as ImageCLEF challenges, advancing multimedia retrieval systems in healthcare and ecological contexts. Recent research includes diabetic foot ulcer diagnosis, snake species classification using multimodal data, and kidney tumor segmentation via U-Net architectures. Key research interests include machine learning pedagogy, integrating industrial projects into Master’s curricula, and developing automated solutions for medical diagnostics. His technical expertise lies in object detection, semantic segmentation, geospatial data fusion, and metadata-driven classification systems. Bracke’s publications demonstrate a decade-long commitment to medical engineering (e.g., femoral neck axis detection for hip surgeries) alongside modern AI applications. Despite no listed awards or grants, his work bridges academic research with practical clinical and ecological challenges. No lab affiliations or student advisements are explicitly mentioned in the provided texts.

Sampath Jayarathnaمشاهده پروفایل

دانشیار

• Neuro-Information Retrieval
• Data Science
• Human-Computer Interaction
• +۵ مورد دیگر

Sampath Jayarathna is an Associate Professor in the Department of Computer Science at Old Dominion University, part of the College of Science. His research focuses on Neuro-Information Retrieval, Data Science, and Human-Computer Interaction, with a particular emphasis on eye tracking, biometrics, and wearable devices. He holds a Ph.D. from Texas A&M University and has been at ODU since 2018. Jayarathna has received multiple accolades, including the NSF CAREER Award in 2021 and the ODUAA New Faculty Award. His work bridges disciplines, applying computational techniques to understand user behavior and develop adaptive interfaces for accessibility. Education: Ph.D. in Computer Science (Texas A&M University, 2016), M.S. (Texas State University, 2010), B.S. (University of Peradeniya, Sri Lanka, 2006). Prior to ODU, he was on the faculty at Cal Poly Pomona. Research Interests: Neuro-Information Retrieval (eye tracking, EEG, oculomotor systems), Data Science (multi-modal sensing, user interest modeling), Human-Computer Interaction (recommender systems, accessibility for visually impaired users). His lab develops tools like the Neuro Information Retrieval and Data Science Lab, supported by grants such as NSF CAREER and COVA-CCI. His recent work includes enhancing accessibility for blind users in e-commerce and data visualization, detecting deceptive web advertisements, and real-time cognitive load analysis via pupillary metrics. He has authored over 50 publications in top-tier venues like IEEE Transactions and ACM conferences. Awards include the 2025 COS Outreach Award, 2023 NSF S&CC-PG grant, and the 2021 Provost's Award for Mentoring. He advises numerous students, with a focus on inclusive technologies and interdisciplinary research. Labs/Teams: Leads the Neuro Information Retrieval and Data Science Lab, collaborating on projects like distributed eye-tracking systems (DisETrac) and gaze analytics dashboards. Active in grants involving disinformation detection and secure IoT applications.

• Modality
• Evidentiality
• Evaluative Language
• +۷ مورد دیگر

Marta Carretero Lapeyre is a Full Professor at the Complutense University of Madrid, specializing in the Department of English Studies, Linguistics and Literature. Her research focuses on modality, evidentiality, and evaluative language in English and contrastive studies, alongside syntactic, semantic, and pragmatic analysis of noun phrases. Supervised 3 PhD dissertations Co-supervised 2 PhD dissertations Currently supervising 1 PhD dissertation Her work spans corpus linguistics, pragmatics, and digital communication. Recent publications address topics like machine learning applications in modal analysis, cross-linguistic pragmatic markers, and epistemic stance in pandemic discourse. She welcomes proposals on pragmatics, corpus linguistics, and digital communication research. Email: mcarrete@filol.ucm.es

Hamish Cunninghamمشاهده پروفایل

استاد پژوهشی

• Internet of Things (IoT)
• Aquaponics
• Natural Language Processing (NLP)
• +۳ مورد دیگر

Professor Hamish Cunningham is a Research Professor of Internet Computing at the University of Sheffield's School of Computer Science, serving as Impact Lead and a member of the Natural Language Processing (NLP) research group. He is also part of the Institute for Sustainable Food's executive committee. His career spans over 40 years in AI, NLP, and information retrieval, with notable contributions to the GATE open-source platform and IoT innovations like the unPhone and MoPi devices. He holds a PhD in Software Architecture for Language Engineering from the University of Sheffield (2000). Research interests include aquaponics for sustainable food production, IoT privacy, and open-source technologies for resilience. He teaches the IoT course (COM3505) and has supervised numerous PhD students focusing on topics like distributed cloud mechanisms, neural authorship attribution, and ontology engineering. His work integrates multidisciplinary approaches, emphasizing economic democracy for environmental sustainability. Key grants include leadership roles in EU-funded projects like SoBigData (€6.5M) and AnnoMarket (€394K), alongside contributions to aquaponics research through COST Action EU Aquaponics Hub. Over 200 peer-reviewed publications span NLP, IoT, and sustainability, with GATE's adoption by organizations like the BBC and WHO. Outreach activities include STEM ambassador roles, Raspberry Pi educational initiatives, and exhibitions like the Ideas Bazaar. His aquaponics greenhouse and open-source ethos reflect a commitment to democratizing technology for societal resilience.