جست‌وجوی استاد و پژوهشگر

• Systems Biomedicine
• Digital Medicine
• Computational Biology
• +۳ مورد دیگر

Isabel Schwaninger serves as a Research Fellow at the Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, operating within the Digital Medicine group at the Belval campus in Esch-sur-Alzette. Her role focuses on advancing interdisciplinary research at the convergence of biomedical science and computational innovation, contributing to LCSB's mission of decoding complex biological systems through data-driven methodologies. Her research spans critical domains in modern biomedicine, with primary emphasis on: Systems-level analysis of disease mechanisms Digital health applications and medical informatics Computational modeling of biological networks Integration of multi-omics data for personalized medicine Artificial intelligence in clinical decision support Biomedical data standardization and interoperability LCSB provides her collaborative environment, featuring state-of-the-art facilities for high-throughput experimentation and bioinformatics. As part of this center, she engages with cross-disciplinary teams developing novel approaches to understand neurodegenerative disorders and metabolic diseases through systems biology frameworks, leveraging Luxembourg's strategic position in European biomedical research infrastructure.

Ying Dingمشاهده پروفایل

استاد

• Data Science
• AI in Healthcare
• Knowledge Graph
• +۵ مورد دیگر

Dr. Ying Ding serves as the Bill & Lewis Suit Professor at the School of Information, University of Texas at Austin, with a joint appointment in the Department of Population Health at Dell Medical School. She co-chairs the AI Health Lab, bridging information science and clinical medicine to advance AI applications in healthcare. Previously, she was a professor and director of graduate studies for the data science program at Indiana University's School of Informatics, Computing, and Engineering. Her research spans AI in healthcare, knowledge graphs, semantic web technologies, and the science of science. She has pioneered work in applying graph mining techniques to drug discovery and pandemic response, particularly through her development of the PubMed Knowledge Graph and analysis of COVID-19 research dynamics. Her work integrates computer vision, natural language processing, and knowledge representation to address healthcare challenges. Dr. Ding has published over 240 papers and secured funding from NIH, NSF, and European Union projects. She co-edits the Semantic Web Synthesis book series and serves as co-editor-in-chief for Data Intelligence published by MIT Press. Her research has been featured in Nature and The Atlantic, particularly her work on research novelty during the pandemic. Amazon Machine Learning Research Award (2020) World's Top 2% Scientists 2020 by Stanford University Top 20 most read JASIST paper (2017-2018) Top 25% cited PLOS Computational Biology articles (2017) As an educator, she teaches courses on AI in Health and Data Semantics, emphasizing hands-on learning with real-world healthcare data. She has organized major conferences including The Web Conference 2023 in Austin and multiple Generative AI for Healthcare workshops at NeurIPS. She is also the co-founder of Data2Discovery, a company advancing AI technologies in drug discovery and healthcare.

Zong-Liang Yangمشاهده پروفایل

استاد

• Land-Atmosphere Interaction
• Climate Modeling
• Hydrology
• +۵ مورد دیگر

Dr. Zong-Liang Yang is a Professor at the University of Texas at Austin's Jackson School of Geosciences, leading the Department of Earth and Planetary Sciences. He holds the John A. and Katherine G. Jackson Chair in Earth System Sciences and directs the Center for Integrated Earth System Science. His research focuses on land-atmosphere interactions, climate modeling, and hydrological extremes, with contributions to models like Noah-MP used globally. Education: Ph.D. (Atmospheric Sciences, Macquarie University), MS (Meteorology, University of Melbourne). He has advised 16 Ph.D. and 3 MS students, focusing on land surface processes and climate applications. Notable awards include the NCAR CCSM Distinguished Achievement Award (2008) and the Joseph C. Walter Jr. Excellence Award (2007). Research emphasizes predictive models for extreme weather (e.g., Hurricane Harvey) and societal impacts, coupled with innovative downscaling methods for regional climate assessments. His work bridges theoretical, observational, and computational approaches, with over 170 peer-reviewed articles and an h-index of 53 (Web of Science). Leadership roles include Co-Chair of NCAR's Land Working Group (2008-2013) and Adjunct Professorships at Chinese institutions (2008-2020). Teaching includes courses on climatology and land-atmosphere dynamics, reflecting his commitment to education and climate literacy.

Curtis Weissمشاهده پروفایل

استادیار مدعو

• Implementation Science
• Data Science
• Network Analysis
• +۳ مورد دیگر

Curtis Weiss is an Adjunct Assistant Professor in the Department of Chemical and Biological Engineering at Northwestern University. He is also a practicing pulmonary and critical care physician, serving as Head of Quality in the Division of Pulmonary, Critical Care, Allergy, and Immunology at NorthShore University HealthSystem, and Medical Director of the NorthShore Glenbrook Hospital Intensive Care Unit. His research focuses on implementation science in critical care medicine, leveraging data-driven methods like network analysis and computational modeling to improve the adoption of evidence-based therapies. Education: BS in Molecular Biophysics and Biochemistry, Yale University MS in Molecular Biophysics and Biochemistry, Yale University MD, Yale University School of Medicine MS in Clinical Investigation, Northwestern University Research Interests: Dr. Weiss investigates barriers to adopting therapies like low tidal volume ventilation (LTVV) for acute respiratory distress syndrome (ARDS). He has secured grants from the NHLBI (R01 and K23 awards) and the Department of Defense, focusing on computational models of clinician adoption and web-based interventions. He collaborates nationally, including co-chairing the American Thoracic Society's Research Statement on Implementation Science. Grants & Awards: R01 Grant (NHLBI) K23 Career Development Award (NHLBI) Co-PI on Department of Defense Grant (2014) Labs/Teams: Active in national committees on implementation science, co-chairing the ATS ad hoc committee. Collaborates with NorthShore University HealthSystem and Northwestern's clinical teams to translate research into practice.

Dirk Eddelbuettelمشاهده پروفایل

استاد بالینی

• Statistical Computing
• R Programming
• Parallel Computing
• +۴ مورد دیگر

Dirk Eddelbuettel is a Clinical Professor in the Department of Statistics at the University of Illinois Urbana-Champaign (UIUC), where he teaches courses such as STAT 447: Data Science Programming Methods. He holds a PhD from EHESS (France, 1997), an MA from EHESS (France, 1992), and an MSc from Karlsruhe Institute of Technology (Germany, 1992). His research focuses on statistical computing, R and C++ integration, and open-source software development. He is renowned for creating the Rcpp package, which bridges R and C++, and co-creating the Rocker Project (Docker containers for R). He also leads the r2u initiative, providing CRAN packages as Ubuntu binaries. His work emphasizes reproducible research, high-performance computing, and statistical software engineering. Notable contributions include the RcppArmadillo, RProtoBuf, and RInside packages. He actively contributes to the R community through publications, workshops, and open-source projects. His teaching and research highlight interdisciplinary approaches, combining statistical theory with computational methods. He maintains a personal website (dirk.eddelbuettel.com) and is active on social platforms discussing R, C++, and open-source technologies.

• Cryptography
• Data Privacy
• Cloud Security
• +۱ مورد دیگر

Yudi Zhang is a Lecturer in the School of Computing and Information Technology at the University of Wollongong. She researches cryptography and secure systems, with publications on privacy-preserving techniques for cloud computing, health records, and IoT applications. Currently supervising PhD candidates in 'Pragmatic Cryptography' and 'Secure and Practical Cloud Computing,' she also leads projects on interoperable cloud data protection funded by internal university grants. Her work emphasizes practical cryptographic solutions for lightweight clients and distributed environments.

Paul Allenمشاهده پروفایل

استاد

• Psychosis Neuroimaging
• Neuroscience
• Mental Health
• +۴ مورد دیگر

Professor Paul Allen is a Professor of Psychosis Neuroimaging at King's College London's Department of Neuroimaging within the School of Neuroscience. He leads the Psychosis Shared MRI Data Resource (Psy-ShareD) funded by the MRC and co-leads a University of Lille project using fMRI neurofeedback to reduce hallucinations in schizophrenia. His research focuses on psychosis risk, auditory hallucinations, cannabis effects on the brain, and the impact of childhood trauma and anxiety on brain networks. Key collaborators include Dr. Matthew Kempton (King's College), Prof. Veena Kumari (Brunel University), and Prof. Renaud Jardri (University of Lille). He has published over 150 peer-reviewed papers and secured funding from the MRC, Wellcome Trust, and industry partners. His work spans neuroimaging techniques like fMRI, arterial spin labeling, and MRI spectroscopy. Research interests emphasize psychosis neurobiology, including hippocampal glutamate interactions, dietary influences on brain chemistry, and real-time neurofeedback interventions. His recent studies explore cannabidiol's effects on brain function in at-risk populations and the neurochemical signatures of cerebral blood flow alterations in schizophrenia. Notable projects include the ENIGMA consortium's global neuroimaging studies on schizotypy and the development of open-access MRI resources for psychosis research. He also investigates how early-life trauma shapes structural and neurochemical brain patterns related to mental health outcomes. Grants include MRC funding for Psy-ShareD and a University of Lille Internal Chair Award. His work bridges clinical neuroscience with translational applications, aiming to improve early psychosis identification and treatment through neuroimaging innovations.

Farzad Rahimianمشاهده پروفایل

استاد

• Sustainable Built Environment
• Digital Transformation
• Construction Management
• +۵ مورد دیگر

Farzad Rahimian is a Professor (Research) in Engineering and the Centre Lead for the Centre for Sustainable Engineering (CSE) at Teesside University's School of Computing, Engineering and Digital Technologies (SCEDT). He holds a PhD in Sustainable Architectural Studies and an integrated BSc/MSc in Architectural Engineering. As an academic leader, he oversees over 100 researchers and has secured over £20m in research projects, including the H2021-funded RECONSTRUCT initiative (€7.8M). His research focuses on sustainable built environments, digital transformation, circular construction, and Industry 4.0/5.0 technologies. He has pioneered work in BIM, digital twins, AI applications, and offsite construction. His work aligns with UN SDGs, particularly Goals 9 (Infrastructure), 11 (Sustainable Cities), and 12 (Responsible Consumption). Rahimian has led 14 successful PhD students and collaborated with global industry leaders like Bouygues Group and ARaymond. He received awards such as the Scottish Knowledge Exchange Innovation of the Year Award 2018 and has filed patents in building energy optimization. His projects emphasize interdisciplinary collaboration, bridging academia and SMEs in Tees Valley. Key contributions include developing blockchain and digital twin frameworks for facility management, AI-driven energy performance analysis, and virtual training environments. He co-leads the Construction Innovation & Research (CCIR) group and chairs international conferences like SASBE. His research addresses global challenges in sustainability, digitalization, and urban resilience.

• Artificial Intelligence
• Healthcare Informatics
• Data Science
• +۵ مورد دیگر

Wajahat Ali Khan is an Associate Professor in Artificial Intelligence at the University of Derby's College of Science and Engineering. His research focuses on AI-driven solutions for healthcare informatics, cybersecurity, and data integration. He leads interdisciplinary projects involving knowledge graphs, semantic web technologies, and clinical decision support systems. Research interests include applying machine learning to healthcare challenges such as disease subtyping, health data interoperability, and personalized medicine. He also explores cybersecurity in network systems and SDN environments. Key contributions include platforms like the Ubiquitous Health Profile (UHPr) for health data interoperability and the Intelligent Medical Platform for dialogue-based healthcare services. His publications span topics from anti-DDoS models to sentiment analysis for depression detection. Collaborations with institutions like Sungkyunkwan University (South Korea) highlight global impact. Khan’s work emphasizes bridging semantic gaps in healthcare systems through adaptive mediation frameworks and data-driven knowledge acquisition methods.

• Digital Health
• Artificial Intelligence
• Generative AI (GenAI)
• +۸ مورد دیگر

Associate Professor Georg Grossmann is affiliated with the School of IT and Mathematical Sciences at the University of South Australia (UniSA), within the Advanced Computing Research Centre. He holds roles as the Research Degree Coordinator for Maths & Stats and Systems Engineering at UniSA STEM, and co-directs the Digital Health Innovation and Clinical Informatics (DHICI) Lab. His research focuses on Digital Health, Artificial Intelligence (AI), Industry 4.0, and interoperability solutions for complex systems. He is also a member of the AI & Software Engineering Lab in the Industrial AI Research Centre, addressing challenges in data integration and business process modeling. Research Interests: Georg's work spans process and data interoperability, AI-driven systems, generative AI (GenAI), and digital transformation. He explores applications in healthcare (e.g., clinical decision support systems), industrial IoT (IIoT), and cultural heritage management. His methodologies include multi-level modeling, knowledge graphs, and semantic frameworks to enhance system interoperability and decision-making. Labs & Collaborations: He leads the DHICI Lab and collaborates with institutions like MIMOSA, Practera, and BAE Systems. His projects include frameworks for predictive maintenance in Industry 4.0, adaptive clinical decision systems, and NLP-based artifact management in museums. He has secured grants from the South Australian Premier’s Research and Industry Fund and the Australian Government’s Department of Industry. Teaching & Supervision: As a Research Degree Supervisor, he guides students in areas like enterprise modeling, software engineering, and AI applications. He also contributes to courses on Business Process Management and Software & Data Modeling.

Shang-Ming Zhouمشاهده پروفایل

استاد

• Health Data Science
• AI in Healthcare
• Electronic Health Records
• +۴ مورد دیگر

Shang-Ming Zhou is Professor of e-Health at the University of Plymouth, directing the Centre for Health Technology and NHS Kernow Datalab. He holds affiliations with Health Data Research UK. His research develops AI methods for health informatics, focusing on explainable machine learning for electronic health records analysis. Funded by HDRUK, MRC, and NSF, his projects address disease phenotyping, polypharmacy patterns, and clinical decision support systems. Recent publications demonstrate innovations in Parkinson's diagnosis, tuberculosis detection, and XAI frameworks. His teaching includes machine learning for healthcare and health data analytics courses for nursing and computing students. Outstanding Reviewer Awards (Journal of Biomedical Informatics, IEEE Transactions) Springer Nature Best Paper Award RCP Best Poster Prize

Hamid Jahankhaniمشاهده پروفایل

استاد

• Cybersecurity
• Information Security
• Digital Forensics
• +۲ مورد دیگر

Professor Hamid Jahankhani is a Professor at Northumbria University with expertise in cybersecurity and digital forensics. He holds a PhD from Queen Mary College, University of London, and previously served as Professor of Information Security and Cyber Criminology at the University of East London. His research focuses on cybersecurity, information security, digital forensics, and critical national infrastructure protection. His work emphasizes industrial partnerships and relevance to law enforcement agencies across the UK and Europe. Professor Jahankhani serves as Editor-in-Chief of the International Journal of Electronic Security and Digital Forensics and chairs the annual International Conference on Global Security, Safety and Sustainability (ICGS3). He has authored over 30 books and 300 publications. Notable Academic Contributions: Supervised 14 PhD/professional doctorate students to completion Overseen 67 PhD students

Arjmand Naveedمشاهده پروفایل

پژوهشگر

• Data Science
• Artificial Intelligence
• Health Informatics
• +۳ مورد دیگر

Dr. Arjmand Naveed is a Post Doc-Researcher in the Faculty of Engineering and Informatics at the University of Bradford. She holds a PhD in Computer Sciences with a focus on addressing semantic interoperability and text annotation concerns in Electronic Health Records (EHR) using Word Embedding, Ontology, and Analogy. Her research emphasizes Data Science, Artificial Intelligence, and improving Human Health, with a particular focus on semantic interoperability, EHR systems, and cybersecurity in healthcare. Her academic journey includes over 7 years as a Computer and Mathematics Lecturer, with a strong commitment to student development and process optimization. She has presented research at conferences such as the Annual Innovative Engineering and Research Conference (AIERC 2019), earning a 3rd position in oral presentations. Her work spans interdisciplinary applications, including energy-efficient healthcare monitoring systems using 5G, semantic RESTful API development for healthcare interoperability, and geophysical studies. She is affiliated with the Bradford-Renduchintala Centre for Space AI and contributes to advancing AI-driven solutions in health informatics and digital technologies.

Henrique Martinsمشاهده پروفایل

دانشیار

• Digital Health Systems
• Health Cybersecurity
• AI in Healthcare
• +۲ مورد دیگر

Henrique Martins is Associate Professor in Social and Business Sciences at Iscte and digital health strategist. His work focuses on cybersecurity, AI governance, and telehealth policy. He pioneered Portugal's health cybersecurity strategy and developed the KIWI hospital framework. Education includes: Medical Degree, NOVA University Lisbon PhD in Management, University of Cambridge MSc in HIV/AIDS, University of Barcelona MSc in Administrative Law, Catholic University of Portugal He created Portugal's national Robotics Unit and Health Cybersecurity Unit. His current EU project XpanDH develops pan-European EHR exchange frameworks.

• Human Health-focused Machine Learning/Deep Learning
• Computer Vision
• Medical Image Analysis
• +۵ مورد دیگر

Dr. Guy C. Hembroff is an Associate Professor in the College of Computing at Michigan Technological University (MTU), serving as Graduate Program Director for the MS in Health Informatics and PhD in Computational Science and Engineering programs. He holds a PhD in Computational Science and Engineering from MTU, alongside an MPA in Public Administration (Northern Michigan University) and a BS in Finance and Economics (MTU). His research focuses on human health applications of machine learning, computer vision, cybersecurity, and medical device innovation, particularly in areas like medical image analysis and mHealth solutions. He leads the Biomedical Data Science (BDS) Lab, a multidisciplinary team addressing pressing healthcare challenges through AI-driven solutions. Education: PhD, Computational Science and Engineering, Michigan Tech MPA, Public Administration, Northern Michigan University BS, Finance and Economics, Michigan Tech Research Interests: Dr. Hembroff’s work spans healthcare AI (deep learning, medical image segmentation, generative AI), cybersecurity for healthcare systems, and biometric development. His lab collaborates with medical institutions to advance disease surveillance, healthcare interoperability, and secure patient data systems. Recent projects include automated quality control of radiographs and blockchain-based patient data auditing. Advising & Grants: Advises four PhD students on topics like medical image segmentation, mental health intervention modeling, and privacy-preserving LLMs. His work integrates clinical, engineering, and cybersecurity expertise to drive practical healthcare solutions. While specific grant details are not listed, his research emphasizes translational applications in global health equity and rural healthcare access. Labs/Teams: The BDS Lab combines clinical informatics, AI, and cybersecurity to innovate in areas such as medical device development and health data security. Current projects include blockchain for secure patient data and AI-driven mHealth tools for underserved regions.