جست‌وجوی استاد و پژوهشگر

• Vascular Imaging
• Image and Signal Processing
• Biomedical Engineering
• +۲ مورد دیگر

Dr. D. Geoffrey Vince is a Researcher at the Cleveland Clinic's Department of Biomedical Engineering. He holds a PhD in Clinical Engineering from the University of Liverpool (1989) and completed a Fellowship in Biomedical Engineering at Cleveland Clinic (1995). His research focuses on vascular imaging, signal processing, and translational medical technologies. Key areas include coronary atherosclerosis imaging, intravascular ultrasound, and artificial intelligence applications in cardiology. Education: PhD in Clinical Engineering, University of Liverpool (1989) Fellowship in Biomedical Engineering, Cleveland Clinic (1995) MSc in Medical Sciences & Chemistry, Leicester DeMontford University (1985) Research & Industry: Dr. Vince has contributed to over 60 publications on vascular imaging and diagnostic technologies. He participates in the NIH Centers for Accelerated Innovations Program, translating research into clinical care. He holds royalty interests with Volcano Corporation for commercialized inventions. His work bridges biomedical engineering with clinical cardiology, emphasizing innovation in diagnostic modalities and AI-driven imaging solutions. Affiliations: NIH-funded research initiatives Industry collaborations with medical device companies Key contributor to imaging guideline development (e.g., intravascular ultrasound standards)

• Machine Learning
• Deep Learning
• Biomedical Signal Processing
• +۷ مورد دیگر

Yue Zhang is a Professor at Westlake University's School of Engineering with an exceptionally active research program spanning multiple disciplines. With dozens of publications in high-impact venues during 2025-2026 alone, Dr. Zhang demonstrates leadership in interdisciplinary research connecting computer science with practical applications in healthcare, environmental science, and education. Affiliation: Westlake University, School of Engineering Research Areas: Machine Learning, Biomedical Engineering, Computer Vision, Environmental Monitoring Publication Output: 90+ papers indexed in dblp (1996-2026), with remarkable productivity in recent years Dr. Zhang's research interests center on applying advanced machine learning techniques to solve real-world problems across multiple domains. The work demonstrates particular strength in biomedical signal processing (EEG, ECG, HRV analysis), environmental monitoring (mangrove forests, crop yield estimation, nitrogen content), and educational technology (AI integration in classrooms). The research approach typically combines deep learning architectures with domain-specific knowledge to create interpretable and effective solutions. The publication record reveals a strong trend toward multimodal and interdisciplinary approaches, with increasing focus on practical applications of AI in healthcare diagnostics, environmental conservation, and educational transformation. Recent work shows particular innovation in attention mechanisms, tensor-based optimization, and knowledge distillation techniques adapted for specialized domains. Dr. Zhang's collaborative network spans multiple institutions and disciplines, with frequent co-authorship patterns suggesting established research groups in both computer science and application domains. The work appears well-funded given the range of sophisticated applications and high publication output across top venues.

Natalia S Ivascuمشاهده پروفایل

استاد

• Anesthesiology
• Medical Ethics
• Cardiac Surgery Outcomes
• +۳ مورد دیگر

Natalia S Ivascu is a Professor of Medical Ethics and Clinical Anesthesiology at Weill Cornell Medical College, Cornell University. She holds dual appointments in the Department of Medicine and Department of Anesthesiology. Her research focuses on cardiac anesthesiology, critical care, and translational medicine, with a strong emphasis on ethical dilemmas in medical practice and the application of artificial intelligence in clinical decision-making. Dr. Ivascu earned her M.D. from Wayne State University School of Medicine (2001) and a B.S. from the University of Michigan (1997). Her work spans clinical practice, ethics, and innovation, including studies on perioperative outcomes, blood transfusion strategies, and gender disparities in healthcare. Her research interests include optimizing surgical and ICU care through technology integration, improving patient safety via interdisciplinary huddles, and addressing systemic inequities in cardiac care. Notable recent work includes trials on pathogen-reduced cryoprecipitate, AI applications in anesthesiology, and gender-based disparities in cardiogenic shock management. No scientific awards are explicitly listed. Her advisory roles and grants are not detailed in the provided text, though her prolific publication record reflects sustained academic engagement. She contributes to the Cardiac Anesthesia and Critical Care teams at Weill Cornell, focusing on translational research bridging clinical practice and ethical frameworks.

Livia F. Krugerمشاهده پروفایل

پژوهشگر ارشد

• Radiology
• Thoracic Imaging
• Medical Imaging
• +۳ مورد دیگر

Livia F. Kruger, M.D., is a Research Fellow in the Department of Radiology at the Mayo Clinic College of Medicine, affiliated with the Mayo School of Graduate Medical Education. She specializes in thoracic imaging, nuclear medicine, and diagnostic radiology, with a focus on clinical and research applications in thoracic radiology and oncological imaging. Education: MD - Medical Doctor Degree, Federal University of Ceará - School of Medicine (2008) Internship - Internal Medicine internship, Richmond University Medical Center (2018) Residency - Radiology, Fortaleza General Hospital (2014) Fellowship in Cardiothoracic Imaging, Mayo School of Graduate Medical Education (2023) Research Interests: Dr. Kruger’s work focuses on advanced imaging techniques such as MRI protocols for thoracic endometriosis, PET/CT applications in oncology and rheumatology, and the integration of machine learning into diagnostic radiology. Her research emphasizes improving diagnostic accuracy and patient outcomes through innovative imaging strategies. Publications: Her recent work spans studies on pulmonary nodules, interstitial lung diseases, and the use of PET/CT in evaluating non-infectious aortitis. She contributes to advancing clinical imaging practices through interdisciplinary collaborations. Awards: 2022 Certificate of Merit - American Roentgen Ray Society Professional Roles: She currently serves as the Nuclear Medicine Residency Liaison at Mayo Clinic and holds memberships in the Society of Thoracic Radiology, American Society of Nuclear Cardiology, and other prominent medical imaging societies. Labs/Teams: Engaged in collaborative research within the Mayo Clinic’s Radiology Department, focusing on translational imaging research and clinical innovation.

Zhaohan Xiمشاهده پروفایل

استادیار

• AI Security
• Clinical AI
• Large Language Models (LLMs)
• +۶ مورد دیگر

Zhaohan Xi is an Assistant Professor in the School of Computing at Binghamton University, SUNY, focusing on AI security/privacy and clinical AI in the context of large language models (LLMs). His research spans cybersecurity strategies, healthcare applications, and advanced AI techniques like graph learning and AutoML. He holds a PhD from Pennsylvania State University, with a visiting scholar stint at Stony Brook University's Computer Science Department. Education: PhD: Pennsylvania State University (2020–2024) Visiting Scholar: Stony Brook University (2023–2024) Master’s: Lehigh University (2016–2018) Bachelor’s: Nanjing University of Aeronautics and Astronautics (2012–2016) Research interests include: AI Security/Privacy: Backdoor attacks, adversarial defense, and LLM vulnerabilities Clinical AI: Cardiologist-level diagnostics, drug repurposing, and ECG analysis Cybersecurity: Threat hunting, red/blue teaming, and threat intelligence Graph Learning: Knowledge graphs, GNNs, and decision-making systems Recent publications emphasize LLM robustness (e.g., standardized testing benchmarks), adversarial knowledge extraction (e.g., stealing knowledge graphs via APIs), and cybersecurity applications (e.g., LLMs as threat intelligence tools). His work bridges theoretical AI security with practical healthcare and defense systems. Notable awards include ICLR Notable Reviewer (2025) and Binghamton’s Outstanding Service and Support Award (2025) . He has served as a NSF panelist (AI/cybersecurity) and reviewer for venues like ARR/EMNLP, ACL, and KDD. Internships include roles at Sony AI (2024), Microsoft (2023), and Uber (2022), focusing on AI research and software engineering. No advising details or grants are explicitly mentioned in the provided texts.

• Functional & Molecular Imaging
• Image-based Biomarkers for Precision Medicine
• Image-based Systems Biology
• +۳ مورد دیگر

Arvind Pathak is a Professor of Radiology, Radiological Science, Oncology, Biomedical Engineering, and Electrical Engineering at Johns Hopkins University School of Medicine. He directs the Image-based Systems Biology Laboratory, focusing on transforming healthcare through advanced imaging technologies. He holds affiliations with Sidney Kimmel Comprehensive Cancer Center, Institute for Computational Medicine, and Translational Tissue Engineering Center. His research integrates engineering, medicine, and design to develop tools for disease insights, biomarker discovery, and health impact. Education: BS in Electronics Engineering from University of Poona, India; PhD in Functional Imaging from Medical College of Wisconsin and Marquette University. Postdoctoral training in Molecular Imaging at Johns Hopkins. Recognized globally for contributions to functional and molecular imaging, with awards including the ISMRM Outstanding Teacher Award and Susan Komen Career Catalyst Award. Research Interests: Functional & Molecular Imaging, Image-based Biomarkers for Precision Medicine, Neuroimaging, Tissue Engineering, Vascular Systems Biology. His lab develops hardware/software tools like VascuViz and HemoSYS, advancing vascular phenotyping and tumor hemodynamics analysis. Recent work includes miniature microscopes for in vivo imaging and oxygen-generating scaffolds for bone regeneration. Leadership & Mentoring: Over 100 mentored students/staff, diversity initiatives, and editorial roles. Awards include 125 Hopkins Hero recognition for educational/research contributions. Active in imaging review panels and funding agencies. Lab & Collaborations: Pathak Lab emphasizes interdisciplinary approaches, with open postdoctoral/fellowship positions. Collaborates on benchtop models for interventional radiology and deep learning for behavioral predictions from neuronal data.

Pranav Rajpurkarمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Medical Imaging
• Clinical Decision Making
• +۴ مورد دیگر

Pranav Rajpurkar is an Associate Professor of Biomedical Informatics at Harvard Medical School, leading the Rajpurkar Lab. His research focuses on developing AI technologies for clinical decision-making, particularly in medical imaging and clinician-AI collaboration. He holds a PhD in Computer Science from Stanford University and has collaborated widely with clinicians across radiology, cardiology, and pathology. His work emphasizes scalable dataset curation, AI algorithms for limited data, and ethical integration of AI into healthcare workflows. Education: B.S., M.S., and Ph.D. in Computer Science from Stanford University. Research Interests: Artificial Intelligence in healthcare, medical imaging analysis, generative AI models, human-AI collaboration, and healthcare policy for AI adoption. His lab’s innovations include foundational work on chest X-ray interpretation and frameworks for medical AI certification. Key Contributions: Over 57 publications, including high-impact studies in Nature , Lancet , and NEJM . Active in global initiatives like the MAIDA data-sharing framework and clinical certification pathways for medical AI systems. Labs/Teams: Directs the Rajpurkar Lab at Harvard DBMI, collaborating with industry and academic partners to advance AI-driven healthcare solutions.

• Artificial Intelligence
• Explainable AI
• Recurrent Neural Networks
• +۵ مورد دیگر

Isel Grau Garcia is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Information Systems group within the Department of Industrial Engineering and Innovation Sciences. Her research focuses on explainable AI, recurrent neural networks, time-series analysis, and bias mitigation in healthcare and business applications. She holds a Ph.D. in Computer Science from Vrije Universiteit Brussel (VUB) and has co-authored over 75 peer-reviewed publications. Key roles include co-promotor for three PhD students and involvement in the ENFIELD project on trustworthy AI. Education: Ph.D. in Artificial Intelligence, Vrije Universiteit Brussel (2020) M.Sc. in Artificial Intelligence, Central University of Las Villas (2014) B.Sc. in Computer Science, Central University of Las Villas (2011) Research Interests: Her work emphasizes interpretability of machine learning models, semi-supervised classification, and interdisciplinary applications in healthcare. Recent projects include developing Fuzzy Cognitive Map models without training data and quantifying trust in clinical decision support systems. Key Contributions: Her articles explore topics like bias detection via SHAP and FCMs, sparsity-optimized feature importance, and recurrent neural networks for temporal dynamics. These contributions align with UN SDG goals for innovation and health. Awards: Best Demo Award at BNAIC/BENELearn 2021 Industria Students Award – Best Talent (2024) Advising & Grants: Supervises three PhD students and collaborates on the ENFIELD project (€ funding). Active in organizing workshops and reviewing for top venues like ECAI and IEEE Transactions on Fuzzy Systems. Labs/Teams: Engaged in interdisciplinary collaborations with institutions like Warsaw University of Technology and the University of Lisbon, focusing on AI ethics and trustworthy decision systems.

• Medical Genetics
• Pediatrics
• Genetic Disorders
• +۳ مورد دیگر

Linda M. Randolph, MD, is a Clinical Associate Professor in the Department of Pediatrics at the University of Southern California (USC). Her work focuses on medical genetics, neonatal care, and pediatric genetic disorders. She is affiliated with Children’s Hospital Los Angeles (CHLA), where she contributes to clinical and translational research in areas such as genetic diagnostics, prenatal screening, and the management of congenital disorders. Her research emphasizes leveraging genomic technologies—such as exome sequencing and deep learning—for early diagnosis and treatment of conditions like mucopolysaccharidosis, mitochondrial disorders, and neurofibromatosis. She has also investigated chimerism in twins and the clinical implications of genetic variants in pediatric populations. Key contributions include studies on the economic evaluation of early exome sequencing, the application of machine learning in facial dysmorphology analysis, and the correlation between genetic mutations and clinical phenotypes. Her work bridges basic science and clinical practice to improve outcomes for children with rare genetic conditions. Dr. Randolph’s publications span over two decades, reflecting her sustained engagement in pediatric genetics, prenatal diagnosis, and the intersection of genetic medicine with emerging technologies. She has collaborated on projects addressing thalassemia management, twin discordance, and the teratogenic effects of medications.

• Biomedical Engineering
• Obstetric Monitoring
• Fetal Diagnostics
• +۵ مورد دیگر

Professor S.G. Oei at Eindhoven University of Technology's Electrical Engineering school leads research in biomedical diagnostics and perinatal technology through the MedTech Innovation Center and Biomedical Diagnostics Lab. His work bridges electrical engineering principles with obstetric challenges, focusing on signal processing systems for fetal monitoring. Affiliated with Center for Care & Cure Technology Eindhoven Active in projects until 2026 (MEDUSA) and recently completed MEDICAID ENGINE Research interests center on: Non-invasive fetal electrophysiological monitoring Electrohysterography for uterine dynamics AI applications in cardiotocogram interpretation Medical simulation for obstetric training Extracorporeal life support system design Project leadership includes: MEDUSA (2020-2026): Medical Data Utilization Solutions Accelerator MEDICAID ENGINE (2018-2023): MedTech Solutions for Cardiovascular Disease

• Sleep Medicine
• Neuroscience
• Biomedical Engineering
• +۳ مورد دیگر

Merel van Gilst is an Assistant Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). She leads the Advanced Sleep Monitoring Group within the Signal Processing Systems research group and collaborates with the Sleep Expertise Center Kempenhaeghe. Her work bridges technical sleep monitoring device development with clinical applications, focusing on sleep disorders and the clinical validation of new technologies. She established the SOMNIA database, integrating polysomnography with experimental modalities in routine patient care. Education: PhD in Neurology from Radboud University Medical Center (2016), Postdoc at TU/e and Kempenhaeghe (2016). Certified Somnologist via European Society of Sleep Research (2017). Undergraduate in Cognitive Artificial Intelligence & Neuroscience at Utrecht University (2011). Research Interests: Clinical sleep technology, sleep stage classification, wearable devices for sleep monitoring, circadian rhythm adaptation, and sleep disorder diagnostics. Her research emphasizes translating technical innovations into clinical practice for conditions like obstructive sleep apnea and insomnia. Grants & Projects: Involved in initiatives including MEDEIA (medical engineering innovations), MEDUSA (medical data utilization), and OP-SLEEP (EEG patch development). Supported by grants from TKI-HTSM, Philips, and Dutch Ministry of Economic Affairs. Labs/Teams: Member of the Biomedical Diagnostics Lab, Eindhoven MedTech Innovation Center (e/MTIC), and Signal Processing Systems group. Active in cross-disciplinary collaborations between TU/e, Philips, and clinical partners.

Arul Earnestمشاهده پروفایل

استاد

• Biostatistics
• Health Economics
• Clinical Registries
• +۴ مورد دیگر

Professor Arul Earnest is a leading academic research biostatistician at Monash University's School of Public Health & Preventive Medicine, where he serves as Deputy Head of the Clinical Outcomes Data Reporting and Research Program (CORRP). He holds a Professor (Research) appointment in Biostatistics, Data Analytics/Modelling, and Health Economics. His expertise spans Bayesian spatio-temporal modelling, machine learning applications in healthcare, and clinical registries analysis. He coordinates the MPH5270 unit on Advanced Statistical Methods for Clinical Research and developed the 'Clinical Registries Data Analysis using Stata' course. His research collaborations include government bodies, patient advocacy groups, and international organizations, with a focus on improving healthcare data quality and patient outcomes. Professor Earnest has authored over 245 peer-reviewed publications, a textbook on biostatistical collaboration, and earned 19 scientific awards. He manages a statistical team supporting ~30 clinical registries and leads national projects on ovarian cancer management, real-time patient outcome measures, and chronic disease priority-setting strategies. His work contributes to UN Sustainable Development Goals addressing health and well-being (SDG 3), and he actively participates in grant review panels, ethics committees, and statistical conferences.

Arya Rahgozarمشاهده پروفایل

استاد مدعو

• Natural Language Processing (NLP)
• Data Science
• Machine Understanding
• +۳ مورد دیگر

Arya Rahgozar is an Adjunct Professor at the School of Electrical Engineering and Computer Science , University of Ottawa, and a scientist at the Ottawa Hospital Research Institute. He holds a PhD in Digital Transformation and Innovation from the University of Ottawa, specializing in NLP for multilingual semantic tasks, alongside M.Eng. (University of Waterloo, Management Sciences) and B.A.Sc. (Tehran Polytechnic, Engineering Design). His research focuses on Natural Language Processing (NLP) applications in healthcare, medicine, epidemiology, and digital humanities. Notable projects include AI-driven systematic reviews in the Brain-Heart Interconnectome, NLP-based osteoporosis detection via eConsult, and dementia identification systems using Explainable AI (XAI). He has pioneered work on poetry chronology (e.g., Hafez’s works) and led initiatives in primary care recommender systems. Rahgozar has over 20 years of industrial experience in decision science and analytics, with expertise in banking, healthcare, and supply chain optimization. As a Mitacs Principal Investigator and startup partner, he co-developed chatbot-based recommendation systems and advanced AI tools for e-health applications. His current work emphasizes improving syncope management and frailty prediction in geriatrics using NLP and LLMs. He collaborates across disciplines, bridging computational methods with clinical and literary domains, and actively contributes to healthcare innovation through partnerships with hospitals and academic institutions.

• Cardiology and Cardiovascular Disease
• Medical Materials
• Heart Failure
• +۳ مورد دیگر

Cecilia Linde is a Professor at the Department of Medicine, Solna, Karolinska Institutet. She holds a Docent degree from Karolinska Institutet (1997) and has been a faculty member since at least 2020, advancing to Senior Professor in 2025. Her research focuses on cardiovascular disease, particularly heart failure, cardiac resynchronization therapy (CRT), and clinical management of arrhythmias like atrial fibrillation. She is affiliated with the Endothelial dysfunction in atherosclerotic cardiovascular disease research group led by John Pernow. Education: Docent, Karolinska Institutet, 1997 Research Interests: Cardiac devices (CRT, pacemakers) Heart failure mechanisms and therapy Atrial fibrillation management Clinical trial design Cardiovascular biomarkers Publications Trends: Her work emphasizes translational cardiology, with recent focus on CRT efficacy across patient subgroups, device-guided AF management, and machine learning applications in predicting CRT outcomes. She collaborates globally on large-scale trials like REVERSE and NOAH-AFNET 6. Awards: No specific prizes mentioned, though her extensive editorial contributions to top journals like European Heart Journal and Circulation reflect peer recognition. Advising & Grants: While no grant details are provided, her involvement in multi-center clinical trials indicates significant research funding. She has not listed formal advisees but co-authors numerous international collaborators. Labs/Teams: Active in the John Pernow research group studying endothelial dysfunction and atherosclerosis. Collaborates with institutions globally on cardiovascular device trials and guideline development.

Elijah Behrمشاهده پروفایل

استاد

• Cardiovascular Medicine
• Genetics
• Genomics
• +۷ مورد دیگر

Elijah Behr is a Professor of Cardiovascular Medicine, Consultant Cardiologist, and Electrophysiologist specializing in sudden cardiac death, genetics, and genomics. His research focuses on arrhythmia syndromes, inherited cardiac conditions, and genetic testing integration into clinical practice. Behr is actively involved in evaluating novel diagnostic methods like pharmacological provocation testing and synthetic electrocardiogram analysis for conditions such as Brugada syndrome. His work spans clinical trials comparing implantable defibrillator therapies (subcutaneous vs transvenous), exercise tolerance in hypertrophic cardiomyopathy, and molecular mechanisms underlying cardiomyopathies. He has contributed to consensus guidelines on genetic testing for cardiomyopathies and device therapy optimization. Behr’s studies also address population-specific genetic variants (e.g., SCN5A in Thailand) and outcomes in rare cardiac diseases identified via large-scale genomic projects like the 100,000 Genomes Project. Key research themes include arrhythmia risk stratification, sex-based differences in device complications, and the role of genetics in sudden cardiac death. His multidisciplinary approach combines clinical cardiology with advanced genomic techniques to improve diagnostic accuracy and patient care in inherited heart conditions. Behr’s academic contributions are further reflected in his leadership in international trials like TETRIS and PRAETORIAN-XL, which investigate arrhythmia management strategies. He advocates for comprehensive molecular autopsy protocols to identify genetic causes of sudden death and improve familial screening.