جست‌وجوی استاد و پژوهشگر

• Cardiovascular Imaging
• Interventional Radiology
• Cardiac CT/MR Imaging
• +۵ مورد دیگر

Hamid Mojibian, MD, is a Professor of Radiology and Biomedical Imaging at Yale School of Medicine and serves as Director of Cardiac CT/MR Imaging. He also holds the position of Associate Professor of Internal Medicine (Cardiology) and is Interim Chair of the Department of Radiology at Bridgeport Hospital. His work bridges radiology, cardiology, and interventional vascular therapy, with a focus on advanced cardiac imaging and minimally invasive procedures. Research Interests: Cardiovascular Imaging Interventional Radiology Cardiac CT/MR Imaging Artificial Intelligence in Radiology Pulmonary Embolism Management Vascular and Endovascular Therapy Dr. Mojibian's recent research explores the integration of artificial intelligence in diagnosing and managing cardiovascular conditions, particularly pulmonary embolism and abdominal aortic aneurysm. His publications highlight the use of machine learning for risk stratification, diagnostic validation, and improving clinical workflows in vascular imaging. Scientific Awards: RSNA Roentgen Resident/Fellow Research Award RSNA Roentgen Resident/Fellow Research Award Most Outstanding Surgical and Transitional Intern of the Year Dr. Mojibian is actively involved in clinical research and mentoring. He leads the DEFIANCE clinical trial, investigating the ClotTriever system versus anticoagulation in deep vein thrombosis. He collaborates extensively with cardiologists, radiologists, and vascular surgeons, emphasizing interdisciplinary teamwork in patient care and research. He also contributes to programs such as the Advanced Cardiovascular CT/MR Program, Preventive Cardiovascular Health Program, and Vascular & Interventional Radiology at Yale. His laboratory and clinical work are centered at Yale Medicine’s Department of Radiology & Biomedical Imaging, where he advances innovation in non-invasive vascular imaging and interventional techniques.

• Interventional Spine Treatments
• Musculoskeletal Pain Management
• Clinical Documentation Improvement
• +۳ مورد دیگر

Dr. Rishi Bakshi is a Clinical Associate Professor in the Department of Physical Medicine & Rehabilitation at the University of Michigan. His clinical expertise focuses on interventional spine and musculoskeletal pain management, with academic contributions centered on clinical documentation improvement and healthcare quality. He also serves as Ambulatory Care Clinical Chief for the University of Michigan Medical Group. University of Michigan, Clinical Associate Professor (2025) Medical Director, Clinical Documentation and Improvement (2013-present) Ambulatory Care Clinical Chief, University of Michigan Medical Group His research interests bridge clinical practice and healthcare administration, particularly in optimizing documentation quality for risk adjustment and revenue integrity. He has developed standardized tools for comorbidity capture and severity measurement, contributing to health services research and medical informatics. Scholarly work includes validation studies of the Michigan Body Map, a digital tool for pain localization, and safety analyses of interventional procedures in complex patient populations. Recent publications emphasize clinical documentation improvement, interventional pain management, and digital health tools. Key trends include standardization of medical coding, validation of patient self-assessment technologies, and quality improvement in musculoskeletal care. Dr. Bakshi completed his medical degree at Des Moines University, residency in Physical Medicine and Rehabilitation (2008), and Pain Management fellowship (2009), both at Michigan Medicine. He has held leadership roles since 2013, focusing on documentation quality, patient safety, and healthcare analytics. His work integrates clinical practice, health system management, and scholarly research, with ongoing projects in pain procedure safety, digital documentation dashboards, and trainee-driven quality initiatives.

Michael A. Grassoمشاهده پروفایل

دانشیار بالینی

• Clinical Informatics
• Biomedical Informatics
• Consumer Health Informatics
• +۸ مورد دیگر

Michael A. Grasso, MD, PhD, is a Clinical Associate Professor in the Department of Medicine and Emergency Medicine at the University of Maryland School of Medicine. He practices Emergency Medicine and serves as Director of the Clinical Informatics Group and Program Director for Clinical Informatics graduate programs at the University of Maryland Baltimore. His education includes: BS in Microbiology, University of Maryland (1983) MS in Computer Science, American University (1986) PhD in Computer Science, University of Maryland Baltimore County (1997) MD, George Washington University (2005) Internal Medicine Residency, University of Maryland (2008) Clinical Informatics Fellowship, University of Maryland (2013) Dr. Grasso's research focuses on knowledge representation and reasoning for large-scale clinical data systems. Key areas include: Big data analytics for chronic disease prediction (diabetes, rheumatoid arthritis, opioid misuse) Patient safety and quality improvement in emergency medicine Impact of online consumer health information on clinical decision-making His 13 recent publications (2011-2022) reveal strong trends in Veterans Health Administration data analysis, opioid prescribing patterns, and machine learning applications. Work emphasizes clinical decision support, predictive modeling for mortality/disease, and ICD-10 coding impacts in emergency settings. Scientific honors include: Fellow of the American College of Physicians (FACP) Fellow of the American Medical Informatics Association (FAMIA) Three medical/computing honor societies As Program Director for Clinical Informatics graduate programs, he mentors students in this emerging field. He has secured over $2,000,000 in grants from NIH, FDA, NIST, NASA, and DoD for: Knowledge representation with big clinical repositories Emergency department quality/safety initiatives Consumer health information impact studies He leads the University of Maryland Clinical Informatics Group, leveraging Veterans Health Administration data (35M patients) and University of Maryland Medical System's EPIC repository (14 hospitals) to develop semantic-enhanced machine learning for disease prediction and treatment efficacy.

Hamada Hamid Altalibمشاهده پروفایل

استاد

• Epilepsy
• Neuropsychiatry
• Traumatic Brain Injury
• +۳ مورد دیگر

Hamada Hamid Altalib, DO, MPH, is Professor of Neurology and Psychiatry at Yale School of Medicine, with secondary appointments in Biomedical Informatics & Data Science. He serves as Chief of Neurology at VA Connecticut Healthcare System and Director of Yale Epilepsy Outcomes Research Program. Professor of Neurology & Psychiatry, Yale University Director, Yale Epilepsy Outcomes Research Program Chief of Neurology, VA Connecticut Healthcare System National Director, VA Epilepsy Centers of Excellence His research spans four pillars: Epilepsy Outcomes (clinical trials, seizure action plans, quality improvement), Neuropsychiatry (depression-anxiety-epilepsy interactions, suicide risk in epilepsy, functional seizures), Neurology Health Informatics (electronic health record analysis, social network mapping of care coordination), and Global Mental Health (cultural/religious influences on neuropsychiatric care in Muslim communities). Recent publications highlight his work on antiepileptic drug side effects, epilepsy-suicide links, functional seizure therapy outcomes, and AI-driven healthcare coordination mapping. His lab develops machine learning tools for PNES identification and leads VA national dashboard projects for neurological care. Fellow, American Epilepsy Society (2017) $7.88 million NIH grant for AI mental health research (2024) Board certified in Clinical Informatics (2023), Epilepsy, Neurology, and Psychiatry

• Transfusion Medicine
• Hematology
• Biostatistics
• +۴ مورد دیگر

Na Li is an Adjunct Assistant Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. Her interdisciplinary research bridges computational methods with clinical hematology through extensive collaborations with transfusion medicine experts. Key contributions in biostatistical modeling for immune thrombocytopenia diagnosis Innovations in healthcare logistics for blood product management Pioneering AI-human collaboration frameworks for electronic health record analysis Her scholarly work spans machine learning applications in disease detection, operational research for blood inventory optimization, and clinical decision support systems development. Recent publications demonstrate integration of large language models with medical expertise for improved diagnostic accuracy. Notable research themes include: Pandemic resource allocation fairness Perioperative anticoagulant management Platelet transfusion analytics Blood product demand forecasting Privacy-preserving federated learning Her work has been cited extensively across transfusion medicine , critical care , and healthcare technology domains, with implementations in major clinical trials and policy documents.

Daniel Leungمشاهده پروفایل

استاد

• Mucosal Immunology
• MAIT Cells
• Cholera
• +۷ مورد دیگر

Daniel Leung is a Professor of Internal Medicine and Adjunct Professor of Microbiology and Immunology at the University of Utah . He holds a B.Sc. and M.Sc. from the University of British Columbia and an M.D. from Wake Forest University School of Medicine . His research focuses on Mucosal-Associated Invariant T (MAIT) cells in infections like cholera , sepsis , and diarrheal diseases , with emphasis on their immune regulatory roles and vaccine implications. Education: B.Sc., University of British Columbia M.Sc., University of British Columbia M.D., Wake Forest University School of Medicine Leung's work examines how MAIT cells contribute to B cell help , antibody production , and mucosal immunity . His team investigates these cells in human tonsil germinal centers and mouse models , particularly in cholera vaccine development using MAIT-activating ligands . Recent publications highlight his efforts in serosurveillance for cholera and SARS-CoV-2 , integrating statistical and machine learning approaches to estimate disease incidence from cross-sectional data. He also develops electronic clinical decision support tools (eCDST) for diarrhea management in low- and high-resource settings , aiming to reduce antibiotic misuse and improve patient outcomes. Key collaborations include the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) , GHESKIO (Haiti) , and institutions like Johns Hopkins and University of Florida . His research is funded by NIH (R01AI130378, R01AI135114, R01AI135115) and the Bill & Melinda Gates Foundation (OPP1198876).

• Machine Learning
• Medical Imaging
• Computational Modelling
• +۳ مورد دیگر

Martin Lillholm is a Professor at the Department of Computer Science , University of Copenhagen, specializing in Image Analysis, Computational Modelling, and Geometry . His research focuses on leveraging machine learning and deep learning for medical imaging , particularly in breast cancer risk stratification and COVID-19 adverse outcome prediction . He has co-authored numerous high-impact journal articles in Radiology , Scientific Reports , and other venues, often collaborating with clinical researchers. Research Trends & Fields Lillholm’s work bridges computer science and healthcare , with recent publications emphasizing: AI-driven mammography screening for early breast cancer detection Texture analysis in medical images to enhance risk prediction Domain adaptation for robust cross-vendor imaging systems Health registry analysis using machine learning for recurrent cancer identification Pandemic risk modeling for COVID-19 outcomes Collaborations & Impact He collaborates across disciplines, including with Department of Clinical Medicine researchers and international partners . His studies have been widely cited, with significant visibility via Mendeley , X , and news outlets . While no formal awards are listed, his research has driven clinical protocol innovations and policy discussions.

Emily Pfaffمشاهده پروفایل

دانشیار

• Clinical Informatics
• Endocrinology
• Medicine
• +۳ مورد دیگر

Emily Pfaff is an Associate Professor of Medicine and Lead Faculty in the Master of Applied Data Science program at the University of North Carolina (UNC) School of Medicine. She holds a PhD and MS from UNC and a BS from Wesleyan University. Her primary affiliation is the Division of Endocrinology and Metabolism within the Department of Medicine. Dr. Pfaff's research focuses on Clinical Informatics, with emphasis on leveraging electronic health record (EHR) data to study post-acute sequelae of SARS-CoV-2 (Long COVID) and other chronic conditions. She is a key contributor to the NIH-funded RECOVER initiative and the National COVID Cohort Collaborative (N3C), leading efforts in data governance, computational phenotyping, and EHR-based cohort studies. Her work integrates machine learning, epidemiology, and clinical research to address public health challenges. Dr. Pfaff has published extensively on Long COVID epidemiology, viral reinfection dynamics, and EHR data standardization. She co-developed computational tools like CLARK and FHIR-Ontop-OMOP to enhance clinical data interoperability. Her educational roles include teaching Advanced Databases for Data Science and mentoring trainees in translational informatics.

• Public Health
• Veterans' Health
• Pain Management
• +۷ مورد دیگر

Harini Bathulapalli is a researcher affiliated with the Department of Biostatistics at the Yale School of Public Health (YSPH). Her work focuses on veterans’ health, chronic disease epidemiology, pain management, and health informatics. She leads studies examining the interplay between mental and physical health conditions in military populations, opioid prescribing patterns, and musculoskeletal disorders. Her research integrates biostatistical methods with real-world clinical data to improve healthcare quality. Notable projects include analyzing pain care quality in Veterans Health Administration (VHA) settings, evaluating complementary therapies for musculoskeletal pain, and developing predictive models for fall risk in veterans. Bathulapalli collaborates with experts in epidemiology, health policy, and clinical medicine. Her work addresses disparities in care access and outcomes for veterans, particularly those with co-occurring mental and physical health conditions. She contributes to interdisciplinary initiatives exploring how diagnostic coding (e.g., ICD-10) impacts healthcare delivery. Publications highlight her expertise in longitudinal cohort studies, machine learning applications, and health equity. She has published over 30 peer-reviewed articles in journals like *Journal of Pain*, *Pain Medicine*, and *Journal of Alternative and Complementary Medicine*.

• Medical Informatics
• Information Retrieval
• Health Search Systems
• +۳ مورد دیگر

Bevan Koopman is an academic affiliated with Queensland University of Technology (QUT), holding a Lecturer position within the School of Computer Science's Department of Computer Science. He completed his PhD in 2014, focusing on semantic search applications in health informatics. His research emphasizes medical information retrieval, task-oriented search systems, and clinical query generation. Key contributions include developing the Relevation! open-source relevance assessment tool and advancing semantic inference models for healthcare applications. Koopman collaborates with the QUT ielab, contributing to projects like consumer health search systems and systematic review methodologies. His work bridges computer science and healthcare, addressing challenges in clinical data integration, medical terminology utilization, and evidence-based medicine systems. PhD in Semantic Search (2014, QUT) Co-developer of the Relevation! system Active contributor to TREC and CLEF health IR tracks Research interests span medical informatics, health search systems, and natural language processing in clinical contexts. His work has been published in venues like Journal of the Association for Information Science and Technology and ECIR conferences.

Aron Henrikssonمشاهده پروفایل

عضو هیئت علمی

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Aron Henriksson is a Senior Lecturer and Associate Professor at the Department of Computer and Systems Sciences, Stockholm University. He co-leads the Natural Language Processing Research Group and contributes to the Learning Analytics and AI for Education Group , focusing on large language models, privacy, explainability, and domain adaptation across healthcare and education. His research integrates AI and NLP into critical domains, including Developing SweClinEval - the first Swedish clinical NLP benchmark Privacy-preserving techniques for LLMs using pseudonymization Multimodal prediction models for healthcare outcomes (e.g., COVID-19 mortality) Educational applications of retrieval-augmented generation Henriksson teaches courses in Big Data, AI management, NLP, and information retrieval. His work bridges technical innovation with practical implementation across EU-funded projects like Extreme Food Risk Analytics (EFRA) and clinical AI initiatives, emphasizing ethical AI deployment and data utility preservation.

John T. Farrarمشاهده پروفایل

استاد

• Pain Research
• Epidemiology
• Biostatistics
• +۳ مورد دیگر

John T. Farrar is a Professor of Epidemiology in the Department of Biostatistics and Epidemiology at the University of Pennsylvania's Perelman School of Medicine. He serves as Senior Scholar at the Center for Clinical Epidemiology and Biostatistics, Co-Director of the Biostatistics Analysis Center, and Co-Director of the Master of Science in Clinical Epidemiology Program. Additionally, he is a Clinical Associate in the Department of Neurology and member of the University of Pennsylvania Cancer Center. His educational background includes: Sc.B. in Biology with Computer Science minor from Brown University (1976) M.D. from University of Rochester School of Medicine (1981) M.S.C.E. from University of Pennsylvania (2000) Ph.D. in Biostatistics and Epidemiology from University of Pennsylvania School of Medicine (2004) Dr. Farrar's research focuses on pain mechanisms and management , with particular emphasis on: Development of new pain medications and assessment methodologies Brain imaging studies in chronic pain patients Complementary therapies like acupuncture for osteoarthritis and cancer-related fatigue Cancer treatment-associated pain syndromes Widespread pain phenotypes in urologic pelvic pain disorders Gender disparities in pain diagnosis and treatment His recent publications (2024-2025) demonstrate leadership in clinical trial design through the IMMPACT initiative, with significant contributions to pain measurement standards and validation of diagnostic tools. Key themes include methodological innovations for pain assessment, neurology applications in tremor disorders, and end-of-life care economics in neurodegenerative diseases, reflecting his interdisciplinary approach spanning oncology, urology, and neurology. Scientific awards were not specified in the source material. As Co-Director of the Master of Science program, Dr. Farrar mentors clinical epidemiology students while leading NIH-funded research through the MAPP Research Network and Age-Related Eye Disease Study. His collaborative work involves: Multi-institutional pain clinical trials Development of patient-centered pain assessment tools Analysis of healthcare utilization patterns in chronic pain populations Investigation of biological and psychosocial pain mechanisms He maintains active clinical practice on the Symptom and Palliative Care Team, developing multidisciplinary pain management protocols for cancer patients while directing the Biostatistical and Epidemiology Consulting Center's analytical operations.

Øyvind Molbergمشاهده پروفایل

استاد

• Rheumatology
• Autoimmune Diseases
• Systemic Sclerosis
• +۵ مورد دیگر

Øyvind Molberg is a Professor at the Department of Rheumatology and Infectious Diseases, Faculty of Medicine, University of Oslo. His clinical and research work is closely affiliated with Oslo University Hospital (OUS Rikshospitalet), where he maintains a visiting address at Sognsvannsveien 20, 0372 Oslo. Molberg's research focuses on autoimmune diseases with particular emphasis on systemic sclerosis, systemic lupus erythematosus, vasculitis, and related conditions. His research interests span multiple dimensions of rheumatic diseases. Molberg investigates genetic factors underlying autoimmune conditions, particularly HLA associations and genetic architecture of inflammatory myopathies. He has significant expertise in interstitial lung disease associated with systemic sclerosis, developing and validating diagnostic methods using HRCT scans. His work also explores gastrointestinal manifestations in rheumatic diseases, including studies on faecal microbiota transplantation for systemic sclerosis patients. Molberg contributes substantially to epidemiological research on autoimmune diseases, examining incidence, prevalence, and mortality patterns in Norwegian populations. Molberg's recent publications reveal evolving research trends toward understanding the genetic basis of autoimmune diseases and developing improved diagnostic criteria. His work increasingly integrates multi-omics approaches to identify biomarkers and therapeutic targets. The research demonstrates strong clinical translation, with studies on vascular ultrasound for giant cell arteritis monitoring and validation of diagnostic criteria for IgG4-related disease. His contributions to understanding disease progression in systemic sclerosis and lupus highlight his focus on improving patient outcomes through better diagnostics and monitoring strategies. Molberg actively collaborates with numerous researchers across Norway and internationally, as evidenced by his extensive co-authorship on multi-center studies. His work frequently appears in high-impact rheumatology journals including Arthritis & Rheumatology, The Lancet Rheumatology, and RMD Open. While specific grant information isn't detailed in the provided text, his leadership in clinical trials like the ReSScue study on faecal microbiota transplantation suggests substantial research funding support.

Jens Lauritsenمشاهده پروفایل

استاد

• Public Health
• Injury Prevention
• Trauma Research
• +۱۲ مورد دیگر

Jens Lauritsen is a Professor at the Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, and is affiliated with Odense University Hospital (OUH) and the Research Unit of Orthopaedic Surgery in Odense. His interdisciplinary work bridges clinical orthopaedics, public health, and injury prevention. His research interests focus on trauma epidemiology, injury prevention, public health policy, and medical informatics. Key areas include concussion trends, pediatric and traffic-related injuries, suicide prevention, and the clinical use of driving simulators for post-accident assessment. He employs nationwide register data and cohort studies to inform evidence-based interventions. The recent publications reflect a strong trend in population-level injury research using Danish national registries, with emphasis on bicycle and pedestrian safety, mental health after self-harm, and technological innovation in orthopaedic rehabilitation. His work combines quantitative epidemiology with practical clinical implementation. Assistant Editor, The Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine (2011) He has supervised several PhD and master’s students and leads or participates in multiple active research projects, including those on suicide prevention, driving simulators, and injury classification. His work is supported by institutional and national research funding. He collaborates extensively with public health agencies, municipal authorities, and clinical teams. Lauritsen is deeply involved in public engagement, contributing to over 695 media appearances on injury prevention, traffic safety, and public health policy. He leads initiatives to modernize clinical documentation and improve preventive strategies based on hospital data.

Sree Appuمشاهده پروفایل

دانشیار مدعو

• Urologic Oncology
• Prostate Cancer Treatment
• Robotic Surgery
• +۲ مورد دیگر

Sree Appu is an Adjunct Associate Professor in Urology at Monash University, affiliated with Alfred Hospital. He completed his medical training at the University of Melbourne (1991) and specialized in uro-oncology through fellowships at the University of Toronto and accredited International Society of UroOncology centers. His expertise includes robotic prostatectomy, minimally invasive prostate cancer treatments, and urologic oncology research. Dr. Appu pioneered robotic surgery at Southern Urology Group and served as Clinical Head of Monash Medical Centre and Monash Cancer Centre for seven years. He is certified in brachytherapy, HiFU, and robotic surgery at Cabrini and Epworth Hospital networks. His research focuses on prostate cancer diagnosis/treatment and contributes to UN Sustainable Development Goals. Notable awards include the Urological Society Travelling Fellowship in Oncology.