جست‌وجوی استاد و پژوهشگر

Osvaldo De Almeidaمشاهده پروفایل

استاد

• Geriatric Psychiatry
• Depression
• Bipolar Disorder
• +۸ مورد دیگر

Osvaldo De Almeida is a Professor of Geriatric Psychiatry at the University of Western Australia (UWA) and holds an adjunct position in the UWA Medical School's Psychiatry department. He completed his medical training at the University of São Paulo and earned a PhD from the Institute of Psychiatry in London. His research focuses on modifiable risk factors for cognitive decline, neuropsychology, and neuroimaging of mental disorders in later life. Key awards include the Jansen Award for Schizophrenia Research (1994). He has led numerous NHMRC-funded projects totaling over A$5 million, investigating cognitive impairment, depression, and lifestyle interventions in aging populations. Education: MD from University of São Paulo (1986), PhD in Psychiatry from the Institute of Psychiatry, London (1993). Research Interests: Geriatric Psychiatry Depression and Bipolar Disorder Schizophrenia with Late Onset Cognitive Decline Mechanisms Neuroimaging Applications Recent Articles Highlight: Cardiac biomarkers and cardiovascular disease risk Psychological interventions for subthreshold depression Impact of diabetes medications on mental health Grants and Projects: Over 75 funded projects since 2001 A$1.8M for Ageing Well research (2006–2011) Kimberley Indigenous Health Studies Labs/Teams: Collaborates with the Centre for Alzheimer’s Disease Research and Care, Perth Elderly Cohort Study.

• Epidemiology
• Public Health
• Clinical sciences
• +۷ مورد دیگر

Professor David Batty is a leading epidemiologist at University College London (UCL), holding the title of Professor of Epidemiology in the Department of Epidemiology & Public Health. He also serves as a Visiting/Honorary Professor at the Universities of Glasgow, Edinburgh, and Oregon State. His research focuses on understanding how psychological, biological, and behavioral factors across the life course influence mortality risks, with an emphasis on prevention strategies. He employs diverse methodologies including cohort studies, Mendelian randomization, and big data analysis, and is a key investigator on major studies such as the English Longitudinal Study of Ageing (ELSA) and the National Child Development Study (NCDS/1958 birth cohort). Education includes a Doctor of Science from the University of Edinburgh (2014), a PhD from the University of Bristol (1999), and prior degrees from London School of Hygiene & Tropical Medicine and Bristol. His work spans cardiovascular disease, dementia, obesity, homelessness mortality, and adverse childhood experiences. He collaborates globally, having worked in Brazil, Denmark, and Australia. Research interests include the interplay of social determinants and health outcomes, cognitive epidemiology, and the use of large-scale datasets to address public health challenges. His recent studies highlight risks associated with contact sports participation, climate change impacts on mortality, and the long-term effects of childhood adversity. Key Research Areas: Cardiovascular epidemiology, cognitive aging, health inequalities, Mendelian randomization Current Roles: Principal Investigator on ELSA, Program Lead for Cognitive Epidemiology (Edinburgh) Grants & Funding: Wellcome Trust Career Development Fellowships, MRC collaborations He advises doctoral students and actively contributes to policy-relevant research addressing global health challenges such as obesity, mental health, and climate change impacts.

Menglu Liangمشاهده پروفایل

استادیار بالینی

• Biostatistics
• Epidemiology
• Cardiovascular Disease Detection
• +۳ مورد دیگر

Dr. Menglu (Maggie) Liang is a Clinical Assistant Professor in the Department of Epidemiology and Biostatistics at the University of Maryland. She holds a PhD in Biostatistics from Penn State University, a Master’s in Biostatistics from the University of Minnesota, and a Master’s in Epidemiology from Johns Hopkins University. Her research focuses on biostatistical methods applied to cardiovascular disease early detection, dynamic survival analysis, and Bayesian network meta-analysis. Education: PhD in Biostatistics, Penn State University (2023) MS in Biostatistics, University of Minnesota (2019) ScM in Epidemiology, Johns Hopkins University (2016) Research Interests: Dr. Liang’s work integrates biostatistical techniques with epidemiological studies to address public health challenges. Key areas include cardiovascular disease biomarkers, environmental health impacts (e.g., extreme heat, wildfires), and methodological advancements in meta-analysis and survival modeling. Recent Research Trends: Her publications emphasize interdisciplinary approaches, combining clinical data with statistical rigor to assess lifestyle interventions, environmental exposure risks, and molecular-level health mechanisms. Recent studies explore step-count metrics in metabolic syndrome populations, heat-related occupational illnesses, and genomic correlates of brain aging. Advising & Grants: While specific grant details are not listed, her academic role suggests involvement in collaborative research projects. No lab affiliations are explicitly mentioned in the provided materials.

Shawn Whiteheadمشاهده پروفایل

استاد

• Neurodegenerative Diseases
• Stroke Pathophysiology
• Vascular Cognitive Impairment
• +۳ مورد دیگر

Dr. Shawn Whitehead is a Professor and Director of the Western Institute for Neuroscience at Western University's Schulich School of Medicine & Dentistry, Department of Anatomy and Cell Biology. He holds a Ph.D. and B.Sc. (Honours) from The University of Western Ontario. His research focuses on neurodegenerative diseases, stroke pathophysiology, and vascular cognitive impairment, with an emphasis on lipid biochemistry and neuroinflammation mechanisms. Key methodologies include rodent models of co-morbid conditions and MALDI-MS imaging for lipid analysis. Research interests include understanding the interplay between Alzheimer's disease and cerebrovascular disorders, lipid raft dynamics in neurons, and the role of gangliosides in neurodegeneration. He explores how vascular risk factors like hypertension and diabetes exacerbate cognitive decline and amyloid-beta pathology. His work integrates molecular, behavioral, and imaging approaches to address translational challenges in neurodegenerative diseases. Publications highlight breakthroughs in lipid biomarker discovery, stroke-induced cardiac dysfunction, and the development of novel animal models. Collaborative projects include investigations into neurovascular unit dysregulation and the brain-heart connection. Current efforts aim to identify therapeutic targets for co-morbid conditions through lipidomic profiling and extracellular vesicle analysis.

Elizabeth S Chenمشاهده پروفایل

دانشیار

• Biomedical Informatics
• Clinical Informatics
• Natural Language Processing
• +۷ مورد دیگر

Elizabeth S. Chen, PhD, FACMI serves as Interim Director of the Brown Center for Biomedical Informatics (BCBI), Associate Professor of Medical Science, and Associate Professor of Health Services, Policy & Practice at Brown University. She leads the Clinical Informatics Innovation and Implementation (CI3) Laboratory and directs the Advance RI-CTR Biomedical Informatics, Bioinformatics, and Cyberinfrastructure Enhancement (BIBCE) Core. Her work focuses on leveraging electronic health data and health information technology for biomedical discovery and healthcare delivery. Education: PhD in Biomedical Informatics, Columbia University MPhil in Biomedical Informatics, Columbia University MA in Biomedical Informatics, Columbia University BS in Computer Science, Tufts University Dr. Chen's research centers on clinical documentation, clinical decision support, health information needs, standards and interoperability, natural language processing (NLP), and data mining and machine learning. Her CI3 Laboratory innovates in EHR use for primary and secondary purposes, implements digital solutions within clinical systems, and inspires the next generation of clinical informaticians. Current projects focus on mental health informatics and pediatric informatics, using data and computational approaches to improve patient care. Analysis of Dr. Chen's recent publications reveals a strong focus on NLP applications in clinical settings, mental health informatics, and EHR data utilization. Her work spans multiple domains including suicide risk assessment, pediatric autism spectrum disorder comorbidities, and clinical note analysis, demonstrating consistent application of machine learning and data mining techniques to solve real-world clinical problems. Scientific Awards: FACMI (Fellow of the American College of Medical Informatics) Dr. Chen actively mentors high school, undergraduate, graduate, and medical students as well as junior faculty. She co-directs the Scholarly Concentration in Biomedical Informatics for medical students and co-instructs biomedical informatics and data science courses. Her grant activities include leadership of the BIBCE Core, which makes EHR and other data accessible for clinical and translational research in Rhode Island. As leader of the CI3 Laboratory, Dr. Chen oversees research focused on innovating EHR use, implementing digital solutions, and training future clinical informaticians. The lab's three-fold mission directly impacts how electronic health data is leveraged for both patient care and research purposes across multiple healthcare domains.

Suzan Arslanturkمشاهده پروفایل

دانشیار

• Medical Informatics
• Predictive Analytics
• Machine Learning
• +۴ مورد دیگر

Dr. Suzan Arslanturk is Associate Professor in Computer Science and Industrial & Systems Engineering at Wayne State University's College of Engineering. She directs the Machine Learning and Health Informatics Laboratory, focusing on predictive analytics for healthcare applications. Research domains include: Cancer subtyping through multi-omics data integration Biomarker discovery for prostate cancer using cross-cancer learning Drug repurposing for cancers with DNA-repair deficiencies Neonatal brain anomaly detection via MRI analysis Operational healthcare optimization during medical surges Leads the development of deep learning frameworks for medical imaging segmentation and clinical text analysis. Publications demonstrate innovations in multimodal data fusion, domain adaptation, and unsupervised abnormality detection. Advises PhD candidates in computational healthcare research and directs academic programs in data mining and intelligent systems.

Claire Stapletonمشاهده پروفایل

مدرس ارشد

• Musculoskeletal Physiotherapy
• Haemodynamics
• Cervical Artery Dysfunction
• +۶ مورد دیگر

Claire Stapleton is a Senior Lecturer in Musculoskeletal Physiotherapy at the School of Allied Health Professions and Pharmacy, Keele University , where she serves as MSc Physiotherapy Programme Director and School Equality, Diversity and Inclusion Lead . With a clinical and academic background in physiotherapy, her work bridges musculoskeletal diagnostics, vascular safety in manual therapy, and digital healthcare innovation. Doctorate in vascular compression syndromes at the shoulder (Liverpool John Moores University, 2009) Postgraduate Health Research Certificate (Leeds University, 2005) First-Class BSc (Hons) in Physiotherapy (Coventry University, 2000) BSc (Hons) in Sports Science (University of Birmingham, 1996) Her research focuses on haemodynamics , cervical artery dysfunction , and digital literacy in health professions , with grants from the Chartered Society of Physiotherapy and NIHR Health Technology Assessment . Recent work explores clinical prediction rules for back pain and exercise interventions for whiplash , reflecting her expertise in musculoskeletal diagnostics and injury prevention. Awards include the Keele Excellence Award and Keele Teaching Innovation Award . Chartered Society of Physiotherapy Robert Williams International Award Keele Excellence Award Keele Teaching Innovation Award Claire advocates for digital healthcare integration and technology-enhanced learning , evidenced by her animated educational tools and student video-creation initiatives. She previously led a post-doctoral NIHR project in biomechanical gait analysis and managed a private sports injury clinic during her PhD, supporting Olympic athletes and TASS-funded students.

• Inflammatory Bowel Disease (IBD)
• Medication Adherence
• Transitions of Care in Adolescents with Chronic Illness
• +۳ مورد دیگر

Dr. Donna Bacchus is a Clinical Assistant Professor at the University of Texas at Arlington (UTA), affiliated with the College of Nursing and Health Innovation. She holds a PhD in Nursing from UTA (2013), an MSN from the University of Texas Health Science Center Houston (1993), and a BSN from the University of Ottawa (1980). With over 40 years of experience in clinical nursing, education, and healthcare leadership, her research focuses on IBD patient care, medication adherence, and transitions to adulthood in chronic illness. She actively teaches in UTA’s graduate nursing programs and contributes to online education strategies. Dr. Bacchus has received awards including the Graduate Dean’s Fellowship (2013) and multiple teaching honors. Her service roles include committee leadership in nursing accreditation, student grievances, and Crohn’s and Colitis Foundation (CCF) initiatives. She has presented nationally on IBD care, transitions of care, and biosimilar medications. Her work bridges clinical practice with academic innovation in chronic disease management.

Gianluca Zazaمشاهده پروفایل

استادیار

• mHealth
• Remote Patient Monitoring
• Cardiovascular Risk Assessment
• +۴ مورد دیگر

Gianluca Zaza is an Assistant Professor (non-tenure track) in the Computer Science Department at the University of Bari Aldo Moro, where he conducts research in healthcare technology and artificial intelligence applications. His work focuses on developing contact-less monitoring systems for vital signs and cardiovascular risk assessment using advanced computational techniques. Dr. Zaza's research interests span mHealth, remote patient monitoring, cardiovascular risk assessment, fuzzy inference systems, photoplethysmography, computer vision, and healthcare technology. His work demonstrates a strong focus on applying computational intelligence to solve real-world healthcare problems, particularly in the context of non-invasive monitoring solutions. His recent publications reveal a clear trend toward developing contact-less monitoring systems for vital signs, with particular emphasis on blood oxygen saturation and cardiovascular risk assessment. These works leverage neuro-fuzzy systems, remote photoplethysmography, and video imaging techniques to create practical healthcare solutions that have gained significant relevance during the pandemic era. Member of GNCS-INDAM (Gruppo Nazionale per il Calcolo Scientifico) of Istituto Nazionale di Alta Matematica Research supported by INdAM GNCS within the project 'Computational Intelligence methods for Digital Health' Associated with CITEL - Centro Interdipartimentale di Telemedicina Dr. Zaza's research has practical applications in telemedicine and remote patient monitoring, with potential to reduce the need for physical contact during health assessments, which became particularly valuable during the COVID-19 pandemic. His work bridges the gap between computer science, biomedical engineering, and clinical practice through innovative technological solutions.

• Genetic Epidemiology
• Cardiovascular Health
• Public Health
• +۳ مورد دیگر

Nicole Armstrong is an Assistant Professor in the Department of Epidemiology at the University of Alabama at Birmingham (UAB) School of Public Health. She holds additional appointments as an Associate Scientist at UAB's Center for AIDS Research and Center for Outcomes and Effectiveness Research and Education (COERE). Her research focuses on genetic epidemiology, cardiovascular health, and health disparities, particularly in African American populations. She earned her Ph.D. in Multi/Interdisciplinary Studies from East Carolina University (2019), an M.S. in Biological and Biomedical Sciences (2015), and a B.S. in Biological and Biomedical Sciences from Miami University (2011). Her postdoctoral training was in Epidemiology at UAB (2019–2022). Dr. Armstrong’s work integrates genomic, epigenetic, and metabolomic approaches to study chronic diseases like diabetes, cardiovascular conditions, and kidney disease. Her studies emphasize understanding how genetic variants, lifestyle factors, and environmental exposures interact to influence health outcomes, with a focus on underserved communities. Notably, she has explored the utility of polygenic risk scores in predicting disease susceptibility and tailoring interventions. Her publications highlight advancements in identifying genetic contributors to hypertension treatment resistance, stroke recurrence, and diabetes risk. She collaborates across disciplines to address disparities in disease burden across racial and ethnic groups. Though no formal awards are listed, her research has been supported by grants from institutions like the National Institutes of Health. Dr. Armstrong advises students and mentors junior researchers through her roles in UAB’s academic and research centers. She actively participates in community-based initiatives to improve health equity and is affiliated with multiple clinical trials and observational studies, including the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort.

Jingfeng Jiangمشاهده پروفایل

استاد

• Biomechanics
• Medical Imaging
• Machine Learning
• +۳ مورد دیگر

Jingfeng Jiang is a Professor of Biomedical Engineering and Graduate Program Director at Michigan Technological University. His research focuses on translational biomedical engineering, particularly in biomechanics, medical imaging, and machine learning applications. He holds a PhD in Civil Engineering (Computational Mechanics) from the University of Kansas, along with MS degrees in Computer Science and Structural Engineering, and a BS in Civil Engineering from Zhejiang University, China. Research Interests : Dr. Jiang’s work bridges biomechanics, medical imaging, and computational intelligence. He develops tools for transforming biomedical imaging data into clinically relevant parameters, such as soft tissue elasticity and blood flow characteristics. His current projects include real-time elastography systems, AI-driven medical imaging analysis, and precision medicine tools for cardiovascular and oncological applications. He collaborates closely with clinical and industrial partners, notably contributing to Siemens’ real-time elastography system. Education : PhD, Civil Engineering (Computational Mechanics), University of Kansas MS, Computer Science, University of Kansas MS, Structural Engineering (Structural Dynamics), Zhejiang University, China BS, Civil Engineering, Zhejiang University, China Grants and Collaborations : Dr. Jiang’s work involves clinical and industrial collaborations, including contributions to commercial medical imaging systems. His lab focuses on advancing ultrasound technologies, computational fluid dynamics modeling, and AI-driven diagnostics for cardiovascular diseases and cancers. Labs and Future Work : His research lab aims to improve precision medicine through tool development in medical imaging and biomechanics. Current open positions include doctoral students and post-doctoral researchers to support projects in aneurysm rupture prediction, liver fibrosis characterization, and AI-driven diagnostic systems.

Saeed Amalمشاهده پروفایل

استاد پژوهشی

• Artificial Intelligence
• Healthcare Applications
• Deep Learning
• +۷ مورد دیگر

Saeed Amal is an Assistant Research Professor at Northeastern University's College of Engineering and Department of Bioengineering. He leads the Amal Lab for Precision Medicine, focusing on AI-driven healthcare solutions. His primary affiliation is with Northeastern's Roux Institute and he holds academic leadership roles in cardiovascular and cancer research. Previously, he was a Postdoctoral Fellow at Stanford University School of Medicine and held industry roles as VP of R&D at Cardiacx Medical (cardiology startup) and Data Scientist positions at companies like General Electric and Dynamic Yield. Education: B.Sc. in Computer Science, Technion - Israel Institute of Technology (2005) M.Sc. in Computer Science (Information Retrieval), University of Haifa (2009) Ph.D. in Computer Science (Machine Learning), University of Haifa (2019) Postdoctoral Research, Stanford University (2021–Present) Research Interests: AI applications in healthcare, including deep learning for medical imaging, NLP for clinical text analysis, and multimodal data fusion for cardiovascular and cancer care. His work emphasizes precision medicine, automated disease diagnosis (e.g., prostate and breast cancer), and improving patient outcomes through AI-driven tools. Key Contributions: Developed AI tools for cardiovascular disease prediction using multimodal data fusion, led initiatives in digital pathology for cancer grading, and pioneered the use of generative AI in Gleason pattern segmentation. His lab has published extensively on machine learning models for adverse event prediction in surgery and healthcare decision-making visualization. Awards: Emerging Visionary Award (Northeastern University NAI, 2025) Grants and Collaborations: Partnered with Santovia Path AI and Prima CARE on AI tools for cancer diagnosis. His work is supported by Northeastern's research initiatives and collaborations with institutions like Stanford University School of Medicine. Labs/Teams: Director of The Amal Lab for Precision Medicine, focusing on AI and multimodal data integration for disease care.

Wei Zhuمشاهده پروفایل

استاد

• Biostatistics
• Medical Imaging
• Clinical Research
• +۵ مورد دیگر

Wei Zhu is a Professor and Deputy Chair in the Department of Applied Mathematics and Statistics at Stony Brook University. She holds a Ph.D. in Biostatistics from UCLA (1996), an M.S. in Statistics from UIC (1992), and a B.S. in Mathematics from East China Normal University (1989). Her research focuses on biostatistics, brain image analysis, clinical trial design, and environmental modeling. Key areas include medical imaging algorithms, climate prediction systems, and statistical methodologies for biomedical studies. Her work spans interdisciplinary applications such as predictive analytics for kidney disease outcomes, synthetic data-driven climate forecasting, and cryptocurrency crash prediction frameworks. She has developed machine learning models for medical image segmentation and decision tree frameworks for risk stratification in pandemic contexts. Teaching includes courses like AMS 312 and AMS 572. Zhu’s research portfolio emphasizes translational statistics, with contributions to genomic modeling, financial market dynamics, and public health policy evaluation. Her lab addresses pressing challenges in healthcare analytics, environmental science, and computational biomedicine through rigorous statistical innovation.

• Pharmacoepidemiology
• Comparative Effectiveness Research
• Medical Informatics
• +۳ مورد دیگر

Brian C. Sauer, PhD, MS is a researcher affiliated with the Department of Internal Medicine (primary) and adjunct departments including Biomedical Informatics , Family & Preventive Medicine , and Population Health Sciences . His work focuses on improving observational research methodologies through transparency and reproducibility frameworks. Education: PhD from University of Florida MS from University of Utah BS from University of Florida Dr. Sauer's research spans pharmacoepidemiology , medical informatics , and causal inference , with a particular emphasis on database research transparency and reproducibility. He leads the development of TRUST (Transparent ReUsable database and Statistical Tools) modules funded by the Veterans Health Administration and OMOP , enabling automated, shareable clinical workflows. His recent work examines cardiovascular risks in rheumatoid arthritis patients, drug safety monitoring via electronic health records, and multimorbidity pattern analysis using machine learning. Dr. Sauer collaborates extensively with epidemiologists, biostatisticians, and health services researchers across the Veterans Affairs system.

• Head and Neck Oncology
• Surgical Oncology
• Thyroid and Parathyroid Disorders
• +۳ مورد دیگر

Marcus M. Monroe, MD is an Associate Professor in the Department of Otolaryngology—Head and Neck Surgery at the University of Utah School of Medicine. He serves as a surgeon and investigator at the Huntsman Cancer Institute (HCI), where he holds leadership roles including Division Chief of Head and Neck Surgical Oncology, Leader of the Head and Neck Disease Center, and Physician Lead for the Head and Neck Clinical Trials Program. Education: MD from University of Missouri; Fellowship at MD Anderson Cancer Center; Residency and Internship at Oregon Health and Science University Dr. Monroe specializes in treating advanced head and neck cancers (skin cancers, thyroid/parathyroid tumors, salivary gland tumors, and upper aerodigestive tract malignancies) through a multidisciplinary approach involving medical/radiation oncologists, dermatologists, endocrinologists, pathologists, and radiologists. His research focuses on cancer survivorship , radiation toxicity , prognostic modeling , and multidisciplinary treatment outcomes . His recent publications analyze cardiovascular risks in cancer survivors, AI accuracy in patient education, recurrence patterns of cutaneous carcinoma, and radiation therapy trends. He has contributed to book chapters on Cancer of the Head and Neck and Targeted Therapy in Translational Cancer Research , and participates in public health podcasts on thyroid cancer and HPV-related head and neck diseases.