جست‌وجوی استاد و پژوهشگر

Kunihiro Matsushitaمشاهده پروفایل

استاد

• epidemiology
• cardiovascular disease
• peripheral artery disease
• +۶ مورد دیگر

Kunihiro Matsushita, MD, PhD is a Professor at Johns Hopkins University with dual appointments at the Bloomberg School of Public Health and School of Medicine. He holds a primary affiliation with the Department of Epidemiology, specifically within the Cardiovascular and Clinical Epidemiology division. Dr. Matsushita also maintains significant affiliations with the Welch Center for Prevention, Epidemiology and Clinical Research and serves as Director of the Data Coordinating Center for the Chronic Kidney Disease Prognosis Consortium (CKD-PC), which comprises over 70 cohorts totaling approximately 11 million individuals worldwide. Dr. Matsushita earned his MD in 1998 and PhD in 2007, both from Nagoya University in Japan. His academic journey began with clinical experience as an interventional cardiologist, which shaped his early research interests in diabetes as a key risk factor for coronary heart disease and its antecedent, obesity. Since joining Johns Hopkins, he has expanded his research to include kidney-related aspects, particularly investigating the complex associations among kidney disease, diabetes, and cardiovascular disease using the Atherosclerosis Risk in Communities (ARIC) Study and the CKD Prognosis Consortium. Dr. Matsushita's research program focuses on bridging medicine and epidemiological research to establish evidence that can improve treatment and prevention of cardiovascular and kidney diseases. His work spans multiple areas including cardiovascular epidemiology, chronic kidney disease prognosis, peripheral artery disease, hypertension, diabetes complications, and risk prediction modeling. He has made significant contributions to understanding how kidney function metrics relate to cardiovascular outcomes across diverse populations. Analysis of Dr. Matsushita's recent publications reveals a strong focus on the intersection of kidney and cardiovascular disease. His research employs large-scale meta-analyses of individual participant data across multiple international cohorts, with particular emphasis on glomerular filtration rate, albuminuria, and their relationship to cardiovascular outcomes. Recent work has expanded into novel areas including arterial stiffness, artificial intelligence applications in kidney disease management, and the impact of modifiable risk factors like smoking and dietary protein on kidney and cardiovascular health. Honors & Awards: American College of Physician Membership (2005) Research Fellow of the Japan Society for the Promotion of Science (2007) Best Reviewers of the Year 2012 of the Annals of Internal Medicine (2013) Japanese Society of Interventional Cardiology Clinical Research Award (2004) Aichi Foundation for Diabetes, Rheumatic Disease and Gout Research Grant (2005) Takao Fund Travel Award (2006) Early Career Travel Award from the American Heart Association (2009) The Aoyama-Kita Scholarship, Johns Hopkins Bloomberg School of Public Health (2009) Early Career Best Science Award at AHA Scientific Sessions (2013) The Sandra Daugherty Award for Excellence in Cardiovascular Disease Epidemiology (2014) Golden Apple Award for Teaching from JHSPH (2019) Dr. Matsushita's leadership extends to major collaborative projects including the Chronic Renal Insufficiency Cohort (CRIC) Study, research on weight loss with bariatric surgery and vascular changes, and national trend analyses of critical limb ischemia hospitalizations. His work with the CKD Prognosis Consortium represents one of the largest international collaborative efforts in kidney disease epidemiology, providing critical evidence for clinical guidelines worldwide.

• Computer Vision
• Facial Expression Recognition
• Deep Learning
• +۴ مورد دیگر

Kévin Bailly is a Lecturer at Sorbonne University, affiliated with the Institute of Intelligent Systems and Robotics (ISIR) and part of the Machine Learning and Artificial Intelligence (MLIA) team. His research focuses on computer vision, deep learning, and their applications in facial expression recognition, neural network optimization, and medical imaging. Dr. Bailly's research interests span multiple areas including: Computer Vision and Image Analysis Deep Learning and Neural Network Optimization Facial Expression and Action Unit Recognition Model Compression and Quantization Techniques Medical Applications of Artificial Intelligence His recent publications demonstrate a strong focus on neural network optimization, with particular emphasis on quantization, pruning, and compression techniques that maintain model performance while reducing computational requirements. His work spans both theoretical advancements in deep learning and practical applications in healthcare, human-computer interaction, and affective computing. He has developed novel approaches like PowerQuant for non-uniform quantization, RULe for real-time face alignment in degraded conditions, and RED++ for data-free pruning of deep neural networks. Dr. Bailly has published extensively in top-tier venues including ICLR, NeurIPS, IEEE TPAMI, and IEEE TAC, with a consistent output of high-impact research from 2022-2024. His work bridges theoretical computer vision with practical applications, particularly in medical diagnostics and human-computer interaction systems. He actively collaborates with researchers across multiple institutions, including Arnaud Dapogny, Edouard Yvinec, and Matthieu Cord, and has contributed to interdisciplinary projects that apply AI techniques to medical domains such as fracture classification and obstetrics.

Paul Brindleyمشاهده پروفایل

مدرس ارشد

• Digital Representations of Landscape
• GIS Mapping
• Health Inequality
• +۵ مورد دیگر

Dr. Paul Brindley is a Senior Lecturer at the School of Architecture and Landscape , University of Sheffield, specializing in geospatial analysis of human-landscape interactions. His work combines GIS, statistics, and digital data to address urban greenspace equity, rural-urban classification, and health-environment linkages. Senior Lecturer in GIS and Spatial Analysis Key researcher in the NERC-funded Improving Wellbeing through Urban Nature (IWUN) project Co-author of official Rural-Urban Classification for England and Wales Research focuses on: Urban Greenspace Accessibility using GPS and social media data Health Inequality Mapping through epidemiological GIS studies Vague Geographic Objects modeling of subjective spatial boundaries Computational Neighborhood Mapping via web data mining Project leadership includes: 2016-2019: IWUN Work Package 1 (Sheffield health-green space analysis) 2013-2014: Rural-Urban Classification updates for 2011 Census 2015: Real-Time Bus Data Mapping system development Teaching modules: LSC119: The Changing Landscape LSC336: Landscape Planning Toolkits LSC5020: Rural Landscape Planning GIS Workshops Professional contributions: Co-developer of Crime Map Analyst toolkit for non-GIS experts Joint Director of Learning and Teaching Links Member of Creative Spatial Practices initiative

• Internal Medicine
• Breast Cancer Research
• Clinical Informatics
• +۳ مورد دیگر

Deborah Rhodes is a Professor in General Internal Medicine at the Yale School of Medicine , with a focus on clinical informatics, breast cancer research, and health equity. She serves as the Enterprise Chief Quality Officer for Yale Medicine, Northeast Medical Group, and Yale New Haven Health System. MD (1992) from Weill Cornell Medical College BA (1986) in History and Literature from Harvard College Her research integrates clinical pathways for alcohol use disorder management and breast cancer screening accessibility, particularly for underserved populations. Recent work includes randomized trials on breast density notification impacts and electronic care pathway implementation. Key collaborations include studies with Benjamin Judson , David Fiellin , and Luming Li , focusing on interdisciplinary applications of clinical informatics and quality improvement initiatives.

Denis Mestivierمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• Data Analysis
• +۳ مورد دیگر

Denis Mestivier serves as a Professor of Bioinformatics at the University of Paris-Est Créteil, where he leads the bioinformatics platform of the Mondor Institute of Biomedical Research (IMRB) under the Faculty of Health. His multidisciplinary expertise bridges computational and biological sciences, with a career-long focus on health-related applications of data-intensive methods. Education: Master's degree in Molecular Genetics PhD in Biomathematics Research Interests: Mestivier develops computational models and bioinformatics pipelines for large-scale biological datasets, specializing in microbiome-cancer interactions and molecular biomarker discovery. His work integrates statistical analysis, metagenomics, and epigenetic modeling to address clinical health problems, particularly in gastrointestinal and liver diseases, through active collaboration with experimental research teams. Publication Trends (2011-2021): His research demonstrates a clear trajectory from metabolic modeling (2011) to translational microbiome studies in colorectal cancer (2019-2021), emphasizing bias mitigation in metagenomics and epigenetic mechanisms linking microbiota to oncogenesis. All works showcase applied bioinformatics in health contexts, with consistent focus on clinical biomarker identification and computational solutions for complex biological systems. Scientific Awards: No awards, fellowships, or medals are documented in the provided text. Teaching and Leadership: Since the 1990s/2000s, Mestivier has pioneered biology-computing curriculum development at the university level, intensifying health-focused teaching after his Faculty of Health appointment. He directs IMRB's bioinformatics platform but specific advising roles or grant funding details are not disclosed. Labs and Teams: As head of IMRB's bioinformatics platform, he oversees computational infrastructure for biomedical research, facilitating cross-disciplinary projects with experimental teams at the Mondor Institute while specializing in health data analysis.

Pierre Monninمشاهده پروفایل

پژوهشگر

• Knowledge Graphs
• Semantic Web
• Graph Embedding
• +۴ مورد دیگر

Pierre Monnin is a Junior Fellow in AI at Université Côte d'Azur , conducting research within the Wimmics team at the I3S Laboratory . He also teaches within the EFELIA Côte d'Azur program. His work spans multiple institutions through funded projects like SHACKLE (EU Horizon), ECLADATTA and AT2TA (ANR), with collaborations at Télécom Paris , Università di Bari , and INESC-ID in Lisbon. Previous roles include temporary lecturer at TELECOM Nancy (2023-2024) and researcher at Orange (2020-2023). Research Interests focus on the knowledge graph lifecycle (construction, matching, refinement, mining, discovery) from neurosymbolic AI and analogical reasoning perspectives. He explores Domain knowledge injection into ML models Symbolic-semantics for graph embeddings Zero-shot bootstrapping techniques Context-aware semantic annotation Link prediction with constraint enrichment Life sciences applications Recent scientific awards include: Best Paper Award at ESWC 2024 (Student & Resource Papers) Best Thesis Award from French Association EGC (2022) 1st Prize (Accuracy Track) at Semantic Web Challenge (2021) His teaching portfolio covers AI fundamentals for foreign languages, marketing, and adult education programs, with specialized courses in Semantic Web technologies NoSQL databases XML tools Compiler implementation He supervises multiple PhD students and interns on topics involving neurosymbolic refinement , knowledge reconciliation , and analogical reasoning . Key software contributions include: KGPrune - Web application for thematic Wikidata subgraph extraction PyGraft - Synthetic knowledge graph generation tool DAGOBAH UI - Semantic table interpretation interface He also maintains datasets like PGxLOD and YAGO4-LP for pharmacogenomics and link prediction.

• Digital Health
• Mobile Computing
• Human-Computer Interaction
• +۴ مورد دیگر

Katarzyna Wac is a researcher at the University of Geneva affiliated with the Faculty of Economics and Management and the Information Science Institute . Her work bridges Digital Health , Mobile Computing , and Human-Computer Interaction , focusing on leveraging wearable devices, smartphones, and AI for health and quality of life (QoL) quantification. Research Themes: Digital biomarkers for Alzheimer's and migraines, QoL assessment via ubiquitous computing, peer- and self-reported behavioral data, and QoE of mobile applications. Labs: Leads the mQoL Lab , a platform for interactive, mobile, and wearable-based studies. Her recent publications explore Transformer models for health data analysis, social robots in homecare, and ethical frameworks for digital mental health. She has contributed to standards for proxy-reported QoL measures and personalized drug delivery systems in digital health. The multimodal integration of emotional signals and context-aware QoS/QoE provisioning for m-health services are recurring technical themes. Key collaborations include the MobiHealth project and COPD24 , translating future internet technologies into telemonitoring solutions. Her work spans from foundational studies on mobile cognition to applied ambulatory assessment of affect and health risks.

• Implementation Science
• Health Systems Research
• Maternal and Newborn Health
• +۱۰ مورد دیگر

Claudia Hanson is a Senior Lecturer (Docent) at Karolinska Institutet and Professor of Implementation Science and Perinatal Epidemiology at the London School of Hygiene and Tropical Medicine. She is also a visiting researcher at the Aga Khan University in Nairobi. Her career integrates clinical expertise in obstetrics/gynecology, epidemiology, and implementation science. MD, MSc in International Health (Charité, Germany), MSc in Epidemiology, and PhD in Epidemiology (LSHTM) Specializes in maternal and newborn health in LMICs, with fieldwork in East Africa Principal Investigator for ALERT, QUALI-DEC, and SISTer’s Care projects Her research focuses on implementation science, health systems strengthening, quality of maternal care, and perinatal mortality reduction. Recent publications address vaccine confidence, stillbirth prevention, anaemia management, and climate change impacts on birth outcomes. Scientific awards include the SIGHT award (2019), WHO and FIGO advisory roles, and leadership in the Doctoral Programme in Biology of Infections and Global Health (BIGH). She has supervised 10 PhD students (4 main, 8 co-supervised) and leads the Sweden–Tanzania PhD training program in reproductive health.

Rahul Siddharthanمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Regulatory Genomics
• Chromatin Structure
• +۶ مورد دیگر

Rahul Siddharthan is a computational biologist at The Institute of Mathematical Sciences (IMSc), Chennai , with research interests spanning bioinformatics algorithms, regulatory genomics, chromatin structure, evolutionary biology, and machine learning applications in clinical outcomes. He holds a PhD in Physics from the Indian Institute of Science, Bangalore, and transitioned to biology during a postdoc at the Rockefeller University, New York. At IMSc, he has led the Computational Biology group since 2013 and contributed to a new PhD program in this field. PhD: Physics, Indian Institute of Science, Bangalore Postdoc: Rockefeller University, New York Current Affiliation: IMSc, Chennai Leadership: Computational Biology group (since 2013) Research Interests include: Bioinformatics Algorithms Regulatory Genomics Chromatin Structure Evolutionary Biology Machine Learning in Clinical Data His recent publications focus on machine learning for fetal growth prediction , clustering methods for clinical data , and evolutionary analysis of fungal pathogens . He actively supervises PhD students and collaborates with medical institutions like Apollo Hospitals and Warwick Medical School. Rahul also organizes interdisciplinary workshops such as 'Machine Learning for Health and Disease' at ICTS Bengaluru and 'Aspects of Gene and Cellular Regulation' at IMSc. Advising and Collaborations involve mentoring current PhD students Sankarsan Dutta and Akash Sivaraman, with past advisees including Pavitra S (defended 2025), Chandrani Kumari (submitted 2024), and Rakesh Netha Vadnala (PhD 2023). Key collaborators include Dr. Uma Ram (Seethapathy Clinic), Leelavati Narlikar (IISER Pune), and Ponnusamy Saravanan (Warwick). Technical Contributions include developing algorithms like THiCweed for ChIP-seq motif discovery and ClaID for Candida auris clade identification. His group engages in projects related to chromatin organization , transcription factor interactions , and genomic data analysis .

Nai-Ching Chiمشاهده پروفایل

دانشیار

• Data Science
• Health Technology
• Dementia Care
• +۴ مورد دیگر

Dr. Nai-Ching Chi is a tenured Associate Professor at the University of Iowa College of Nursing. She is a nurse scientist and informaticist conducting pioneering research at the intersection of data science, technology, and health, focusing on dementia care, multimorbidity management, pain management, family caregiving, and healthy aging. PhD in Biobehavioral Nursing and Health Informatics (University of Washington) MS in Clinical Informatics and Patient-Centered Technologies (University of Washington) MSN (University of California, San Francisco) BSN (Tzu Chi University, Taiwan) Dr. Chi's research integrates advanced data science and AI to develop digital interventions for vulnerable populations. Notable projects include the PACE-app for dementia caregiver pain management, AI-powered nursing communication tools, and NIH-funded studies on multimorbidity patterns in Alzheimer's patients. Her work informs clinical guidelines and leverages predictive modeling for early risk detection. Recent publications focus on AI-driven pain assessment, machine learning in multimorbidity analysis, usability of caregiving apps, and rural age-friendly ecosystems. Her work spans comorbidity indices, geriatric pain, and health informatics innovations. National Academy of Medicine Emerging Leader Forum nomination NIA Butler-Williams Scholar NIA Aging Initiative MCCs Scholar Hospice and Palliative Nurses Association Emerging Leader Award Dr. Chi mentors students in aging research and technology-driven healthcare. She serves on editorial boards, grant review panels, and university committees, while volunteering in nursing homes and international medical missions.

• Substance Use Disorders
• Alcohol Use Disorders
• Tobacco Use Disorders
• +۵ مورد دیگر

Walter Roberts is an Assistant Professor in the Psychiatry Department at Yale School of Medicine with a secondary appointment in Biomedical Informatics & Data Science. A licensed clinical psychologist specializing in substance use treatment, particularly alcohol and tobacco use disorders, he combines clinical expertise with technological innovation in his research program. Dr. Roberts received his PhD in Clinical Psychology from the University of Kentucky in 2016. His research program employs multiple methodologies to identify the causes and consequences of substance use in humans, with a focus on developing mobile biosensor platforms that can passively detect and predict substance use behaviors. These systems, built using machine learning and informatics techniques, aim to improve assessment and targeted delivery of interventions in clinical and research settings. He also investigates risk factors for hazardous alcohol use using clinical and epidemiological data and maintains an active interest in research ethics related to substance use research. Dr. Roberts' publication record reveals a strong focus on alcohol use disorders, with increasing integration of digital phenotyping and AI technologies in recent years. His work spans from traditional clinical studies to cutting-edge investigations using wearable technology and machine learning. The research shows a clear trajectory toward developing objective, real-time assessment methods for substance use behaviors, with particular attention to sex and gender differences in alcohol-related outcomes. Recent publications demonstrate growing interest in ethical considerations surrounding digital technologies in substance use research. MD2K Training Institute Summer Scholar (2023) Early Career Scientist Award from CHADD (2015) Enoch Gordis Award Finalist from Research Society on Alcoholism (2013) Dr. Roberts serves as a Sub Investigator on multiple clinical trials related to alcohol addiction and mental health, including studies examining the effects of acute stress and inflammation on drinking behavior, combining varenicline and guanfacine for smoking cessation, and investigating whether propranolol attenuates stress-induced drinking. His collaborative network includes frequent co-authors such as Sherry McKee, Terril Luce, MacKenzie Peltier, Yasmin Zakiniaeiz, and Bubu Banini. His research has been featured in YaleNews, highlighting work on using smartwatches to better understand psychiatric illness.

Haoyu Wangمشاهده پروفایل

پژوهشگر

• Event-Centric NLP/NLU
• LLM Reasoning and Planning
• Knowledge Graph
• +۵ مورد دیگر

Haoyu Wang is a Researcher in the Computer and Information Science department at the University of Pennsylvania . He previously held research positions at Shanghai Jiao Tong University and interned at Google DeepMind , Amazon AWS , ByteDance , AI2 , Tencent AI Lab , and Goldman Sachs . Education : PhD in Computer and Information Science (2021–Present), MS in Computer and Information Science (2019–2021), BS in Computer Science (2015–2019). His research focuses on Event-Centric NLP/NLU , LLM Reasoning and Planning , Knowledge Graph , and Pose Estimation in Computer Vision . His work includes event causality identification, semantic classification in context, and synthetic control for temporal reasoning. He has contributed to multimodal hallucination analysis and safety in reasoning models through projects like RESIN-11 and Devil's Advocate . His publications span venues like EMNLP , EACL , and ACL . His recent articles analyze LLM limitations in NP-hard problems , clinical trial prediction , event causality , and hallucination in vision-language models . He has served as PC Member for conferences including ACL , NAACL , NeurIPS , and EMNLP since 2019.

• Critical Care Medicine
• Risk Prediction in ICU
• Chronic Critical Illness
• +۴ مورد دیگر

Jason Shahin is an Assistant Professor at the Department of Medicine , Faculty of Medicine and Health Sciences , McGill University . He serves as an Investigator at the Research Institute of the McGill University Health Centre (RI-MUHC) , specifically within the Translational Research in Respiratory Diseases Program . His academic and clinical work bridges critical care medicine and respiratory medicine at the McGill University Health Centre (MUHC) . Research Focus: Dr. Shahin specializes in risk prediction in intensive care units (ICU) , with particular emphasis on prediction tools for complex populations , including the chronic critically ill and potential organ donors . His work integrates clinical data science with translational approaches to improve ICU outcomes. Article Trends: His publications span critical care medicine , sepsis , mechanical ventilation , and organ donation . Recent studies focus on machine learning applications , probiotics in infection prevention , and blood conservation strategies in ICU settings. Key Collaborations: He contributes to large-scale trials like the LOVIT , PROSPECT , and FORECAST studies, collaborating with networks such as the Canadian Critical Care Trials Group and REVA Network .

• Artificial Intelligence
• Educational Technology
• Business Process Management
• +۵ مورد دیگر

Darko Etinger is an Associate Professor and currently serves as the Dean of the Faculty of Informatics at Juraj Dobrila University of Pula in Croatia. His academic career spans over two decades with significant contributions to the fields of information systems, educational technology, and artificial intelligence. He teaches undergraduate courses including Artificial Intelligence, Business Information Systems, Business Process Management, ICT Fundamentals, Information Systems, Introduction to Artificial Intelligence, and Multimedia Systems. At the graduate level, he teaches Development of IT Solutions, IT Management, Modelling and Simulation, and Project Management. He also supervises doctoral research in Management of Information Technologies in Education. Dr. Etinger's research interests span several key areas in computer science and education technology. He has made significant contributions to understanding how information and communication technologies can support children with special educational needs, as evidenced by his 2024 and 2025 books on the topic. His work also focuses on business process management, educational data mining, and the application of artificial intelligence in educational contexts. His 2024 book 'Introduction to R and RStudio' demonstrates his commitment to data science education. Analysis of his recent publications shows a strong focus on practical applications of technology in education and business. His work spans educational robotics, learning management systems analysis, business process modeling, and the application of large language models in healthcare. He has a particular interest in how technology can be made accessible and beneficial for diverse learner populations, including those with special needs. His research often takes a human-centered approach, examining not just the technology itself but how it's adopted and used by end users. His 2025 bibliometric analysis of metaverse security demonstrates his ability to tackle emerging technological challenges. Associate Professor at Faculty of Informatics, Juraj Dobrila University of Pula Current Dean of Faculty of Informatics Author of multiple publications on educational technology and information systems Supervisor for numerous graduate theses on educational technology topics Active participant in EDIH Adria project as evidenced by 2025 publications Researcher in educational robotics implementation in Croatian schools Dr. Etinger has advised numerous students completing their theses, with a focus on educational technology applications. His research has been published in various international conferences and journals including IEEE Engineering Management Review, Procedia Computer Science, and System Dynamics Review. His current research appears to be heavily focused on the EDIH Adria project, which is a European Digital Innovation Hub initiative focusing on technology transfer and business innovation. He maintains an active research program with publications spanning from 2003 to the present, demonstrating sustained scholarly contribution to his fields of expertise.

Daniel Haleمشاهده پروفایل

استاد

• Pediatric Endocrinology
• Growth Disorders
• Pubertal Disorders
• +۳ مورد دیگر

Daniel Hale is a Professor in the Department of Pediatrics and Chief of the Division of Endocrinology. His work focuses on pediatric endocrine abnormalities, particularly in children with genetic syndromes, growth disorders, and pubertal disorders. He is also active in medical student, resident, and provider education. Education: MD, University of Texas McGovern Medical School (1977) Pediatrics Residency, Medical University of South Carolina (1980) Pediatric Endocrinology Fellowship, Children's Hospital of Philadelphia (1983) Research Trends: Type 2 Diabetes in Youth Genetic Syndromes and Endocrinology Impact of CNS Stimulants on Pediatric Growth Glycemic Control and Complications in Diabetes Chromosome 18 Syndromes Collaborations: Extensive cross-institutional collaborations in diabetes, genetics, and child development. Grants: National Children's Study, GH Trial for Chromosome 18q- Syndromes, DM2 Risk in Mexican Youth, Telehealth Network Grants.