جست‌وجوی استاد و پژوهشگر

Jennifer Salernoمشاهده پروفایل

استادیار

• Chronic Disease Epidemiology
• Aging Populations
• Causal Models
• +۱۲ مورد دیگر

Jennifer Salerno is a Part-Time Assistant Professor in the Department of Health Research Methods, Evidence, and Impact at McMaster University . She also serves as a Research Associate in the Department of Family Medicine at McMaster, focusing on the TAPER clinical trial (Team Approach to Polypharmacy Evaluation and Reduction) to address polypharmacy in older adults. She actively participates in the Canadian Longitudinal Study on Aging (CLSA) and mentors clinical faculty and medical residents through the InQuiry curriculum. PhD in Epidemiology, University of Toronto MSc in Community Health and Epidemiology, Queen’s University Honours BSc in Biology and Pharmacology Co-op Program, McMaster University Her research spans chronic disease epidemiology in aging populations, causal modeling, clinical trials, comparative effectiveness, health services research, and research ethics . She integrates Big Data and ethical considerations into studies on polypharmacy , multimorbidity , and transitional care for stroke patients. Her recent publications emphasize telemedicine , health technology , and global health ethics . Dr. Salerno has held leadership roles, including Chair of the American College of Epidemiology Ethics and Policy Committee and Executive Council Member of the International Epidemiological Association . Her work frequently involves health technology assessments, systematic reviews, and meta-analyses conducted during her tenure as a clinical epidemiologist in the Government of Ontario (MOHLTC and Health Quality Ontario).

Lu Tianمشاهده پروفایل

استاد

• Survival Analysis
• Semiparametric Modeling
• Restricted Mean Survival Time
• +۴ مورد دیگر

Lu Tian is a Professor of Biomedical Data Science in the School of Medicine at Stanford University, with a courtesy appointment as Professor of Statistics. He has been at Stanford since 2008, progressing from Assistant Professor to his current full Professor position in the Department of Biomedical Data Science. Sc.D. in Biostatistics, Harvard University (1998-2002) M.S. in Mathematics, Nankai University, Tianjin, P.R. China (1995-1998) B.S. in Mathematics, Nankai University, Tianjin, P.R. China (1991-1995) Professor Tian's research spans several critical areas in biostatistics and data science. His work focuses on developing innovative statistical methodologies with direct clinical applications. He has made significant contributions to survival analysis, particularly in restricted mean survival time methods, which offer clinically interpretable alternatives to traditional hazard ratios in clinical trials. His research in meta-analysis addresses limitations of existing methods, especially for studies with sparse data or few studies. In personalized medicine, he develops statistical frameworks for identifying patient subgroups that benefit most from specific treatments. His recent publications demonstrate a clear trend toward developing statistically rigorous methods with direct clinical interpretability. Rather than focusing solely on theoretical advances, his work emphasizes practical applications in oncology and cardiology clinical trials, where he develops methods that provide clear, actionable insights for clinicians. His research bridges the gap between complex statistical theory and real-world clinical decision-making, with particular emphasis on survival analysis techniques that clinicians can readily interpret and apply. Wangkechang Scholarship, Nankai University (1991-1998) Howard Hughes Fellowship (1999-2002) Robert B. Reed Award for Excellence in Biostatistics, Harvard (2000) Distinction in Teaching Award, Harvard School of Public Health (2000-2002) Professor Tian has served as Associate Editor for several prominent journals including Biometrics, Statistics in Medicine, and Biostatistics and Epidemiology, and as Book Editor for Advanced Medical Statistics. He was a Board Director of the International Chinese Statistical Association (2015-2018). His methodological contributions have been widely adopted in clinical research, particularly his work on restricted mean survival time which has changed how treatment effects are reported in oncology trials. His development of exact inference methods for meta-analysis has provided more reliable approaches for combining evidence from small studies.

Harlan Campbellمشاهده پروفایل

استاد مدعو

• Bayesian Statistics
• Meta-Analysis
• Causal Inference
• +۳ مورد دیگر

Harlan Campbell is an Adjunct Professor in the Department of Statistics at the University of British Columbia (UBC), affiliated with the Faculty of Science. His research focuses on Bayesian statistics, causal inference, meta-analysis, and statistical methodology, with applications in medical and public health contexts. Campbell collaborates internationally, such as leading the Zika virus individual participant data consortium to study pregnancy outcomes. He has advised at least one student: Nathaniel Wu Dyrkton. His work integrates evidence synthesis, clinical trial analysis, and methodological innovation to address gaps in data reporting and analysis across disciplines. Education details are not explicitly provided, but his career reflects advanced training in statistical theory and applications. Research interests include developing robust statistical techniques for observational studies, addressing measurement error in meta-analyses, and improving methodologies for indirect treatment comparisons. He has contributed to frameworks for estimating infection fatality rates during the COVID-19 pandemic and advancing Bayesian approaches to evidence synthesis. Publications span topics like structural uncertainty in evidence synthesis, doubly robust estimators for clinical trials, and causal inference in longitudinal studies. His work emphasizes rigorous statistical methods to enhance reliability in medical and epidemiological research. Grants and collaborations are implied through his involvement in global consortia and methodological reviews but are not explicitly detailed in the provided text. Campbell maintains an active Google Scholar profile with extensive citation activity.

Shahram Heydariمشاهده پروفایل

مدرس

• Transportation Safety
• Travel Behavior
• Traffic-Related Air Pollution
• +۴ مورد دیگر

Dr. Shahram Heydari is a Lecturer (Assistant Professor) at the University of Southampton's Department of Civil, Maritime and Environmental Engineering, affiliated with the Transportation Research Group. His research focuses on transportation safety, air pollution, and travel behavior at the intersection of public health and urban planning. He holds a PhD in Transportation Engineering from the University of Waterloo and completed postdoctoral training at Imperial College London. Dr. Heydari is an editorial board member of Analytic Methods in Accident Research and a member of CIRRELT. He has supervised multiple PhD students and received notable awards including the Alexander Graham Bell Scholarship and NSERC Fellowship. His research integrates advanced statistical methods like Bayesian nonparametrics to address real-world transportation challenges. Key areas include roadway safety, air quality modeling, and promoting sustainable transport modes. He collaborates across disciplines, including public health and environmental science, to inform policy and reduce health disparities linked to transportation systems. Education: PhD in Transportation Engineering, University of Waterloo, Canada Civil Engineering Degree, University of Rome La Sapienza, Italy Postdoctoral Fellowship, Imperial College London Awards: Alexander Graham Bell Doctoral Scholarship NSERC Postdoctoral Fellowship Teaching: CENV6168: Transportation Management and Safety CENV6124: Transport Data Analysis and Techniques FEEG6025: Data Analytics in Civil Systems External Roles: Editor, Analytic Methods in Accident Research (2019–present) Member, CIRRELT (2018–present)

• Neurology
• Stroke Research
• Medical Imaging
• +۳ مورد دیگر

Dr Xuya Huang is an Associate Clinical Fellow at the School of Psychology & Neuroscience, University of Glasgow. Their primary affiliation is at the Southern General Hospital in Glasgow, specializing in neurology and stroke research. Dr Huang's work focuses on clinical trials, thrombolytic therapies, and neuroimaging in acute stroke management, contributing to major studies like the RESTART trial. They have co-authored numerous articles on topics including CT perfusion analysis, antiplatelet therapy effects, and stem cell transplantation outcomes in stroke patients. Their research bridges clinical practice and advanced imaging techniques, aiming to improve stroke prognosis and treatment efficacy. Dr Huang collaborates extensively with international teams on trials evaluating new therapeutic strategies for cerebrovascular diseases. Key research areas include: stroke pathophysiology, thrombolytic agent comparisons (e.g., tenecteplase vs alteplase), and optimizing imaging protocols for acute stroke diagnosis. Their publications frequently address treatment selection criteria, recanalization outcomes, and biomarker validation in ischemic and hemorrhagic stroke subtypes. PhD: Not explicitly stated in provided text Current Role: Clinical Fellow with active involvement in randomized controlled trials (RESTART, ATTEST) Major contributions include advancing understanding of CT perfusion thresholds' impact on tissue viability predictions and refining antiplatelet therapy guidelines for post-stroke patients. Their work integrates clinical data with neuroimaging analytics to improve patient stratification and treatment personalization.

Nophar Geifmanمشاهده پروفایل

استاد

• Health Informatics
• Artificial Intelligence in Healthcare
• Biomedical Data Analytics
• +۴ مورد دیگر

Professor Nophar Geifman is a leading academic in Health and Biomedical Informatics at the University of Surrey's School of Health Sciences, affiliated with the Digital Health Expert Group. His research focuses on leveraging AI, big data analytics, and omics technologies to improve patient outcomes in healthcare. He specializes in translational research, particularly in proteomics, genomics, and machine learning applications for disease prediction and treatment optimization. Key research areas include analyzing large datasets like the UK Biobank to study treatment outcomes in endocrine disorders, autoimmune conditions (e.g., juvenile arthritis, lupus), and renal diseases. He also explores diet-health correlations, viral pathogenesis (e.g., RSV), and methodological challenges in omics research. His work emphasizes real-world impact, such as developing clinical decision tools and improving biomarker reliability. Publications span peer-reviewed journals in rheumatology, nephrology, epidemiology, and data science. He actively addresses challenges in clinical trial data integration, reproducibility of biomarker studies, and the ethical use of large-scale health data. His interdisciplinary approach bridges computational methods with clinical practice, aiming to drive precision medicine and patient-centric care.

Dana Crawfordمشاهده پروفایل

استاد

• Genetic Epidemiology
• Electronic Health Records (EHR) Integration
• Pleiotropy Analysis
• +۳ مورد دیگر

Dana Crawford is a Professor in the Department of Population and Quantitative Health Sciences and Associate Director for Community and Population Research & Outreach at the Cleveland Institute for Computational Biology (CICB). She holds a secondary appointment in the Department of Genetics and Genome Sciences at Case Western Reserve University (CWRU). Her research focuses on leveraging large-scale epidemiological and clinical data from electronic health records (EHRs) to study genetic variants associated with human diseases, particularly investigating pleiotropy (single genes influencing multiple traits) and environmental modifiers of genetic associations, including pharmacogenomics. Education: PhD in Genetics and Molecular Biology from Emory University (2000), postdoctoral training at the CDC (2000–2002) and University of Washington (2002–2006). Prior to CWRU, she served as tenure-track faculty at Vanderbilt University (2006–2014). Research interests include applying genomic data to understand genotype-phenotype associations in diverse populations, particularly addressing disparities in precision medicine. She leads NIH-funded consortia such as the eMERGE Network and PAGE study, advancing infrastructure for multi-ethnic genomic research. Her work emphasizes translating genetic discoveries into clinical practice through EHR integration. Notable achievements include developing phenome-wide association studies (PheWAS), co-chair roles in major consortia, and leadership in the American Society of Human Genetics. She co-directs a post-baccalaureate program preparing students for biomedical PhD/MD-PhD paths. Awards: AAAS Fellow (2020), ACMI Fellow (2023), Kavli Fellow, and ASHG Cotterman Award.

Spyridoula Vazouمشاهده پروفایل

دانشیار

• Exercise Psychology
• Physical Activity and Cognition
• Youth Physical Activity Motivation
• +۳ مورد دیگر

Spyridoula Vazou is an Associate Professor in the Department of Kinesiology at Michigan State University's College of Education. Her work focuses on exercise psychology and pedagogy, emphasizing physically active environments to enhance learning and cognitive function in youth. She holds a Ph.D. from the University of Birmingham, UK. Her research explores strategies to motivate physical activity through qualitative interventions, integrating movement with academics. Key areas include school wellness programs like SWITCH, classroom-based physical activity, and the cognitive benefits of rhythmic activities like traditional dance. Publications reflect interdisciplinary approaches, combining neuroscience, education, and public health. Vazou's work addresses both acute and chronic effects of physical activity on executive functions, with a focus on lifespan development and intervention efficacy. She has contributed to policy frameworks for school-based physical activity programs and evaluated implementation strategies for community health initiatives.

• Forecasting
• Logistics and Supply Chain Management
• Artificial Neural Networks
• +۵ مورد دیگر

Sven F. Crone is a Lecturer (Assistant Professor) in Management Science at Lancaster University Management School (LUMS), where he serves as deputy director of the Lancaster Centre for Forecasting. He holds a PhD (Dr. rer. pol.) from the University of Hamburg, Germany, and a Habilitation from Catholic University of Eichstätt-Ingolstadt. His research focuses on forecasting methodologies, neural networks, and data mining applied to supply chain management, inventory optimization, and business decision support. He has published extensively in journals like EJOR, JORS, and Neurocomputing, and is a member of professional associations including IEEE and the International Institute of Forecasters. Dr. Crone teaches courses in forecasting, data mining, and demand planning, and has extensive industry collaboration experience with firms like Beiersdorf, Sanofi-Aventis, and Celanese. He has secured grants from corporate partners and research institutions, including the KISC Forecasting grant (2009) and SAS & International Institute of Forecasters grant (2006). His work has earned prestigious awards, including the 2008 Professor Herbert Jacob Award and 2009 German Operational Research Society award. Research interests include neural network applications in forecasting, computational intelligence, and model selection. He actively supervises PhD students in forecasting, with a focus on time series prediction and algorithmic advancements. His external roles include leadership in IEEE technical committees and visiting fellowships at institutions like the University of Oviedo.

• Experimental Finance
• Behavioral Finance
• Risk Perception
• +۳ مورد دیگر

Felix Holzmeister is a Professor at the University of Innsbruck’s Department of Economics , Austria, whose research agenda spans experimental and behavioral finance, meta-science, risk perception, and the reproducibility of empirical research. His work is characterised by large-scale collaborative projects—often involving dozens of co-authors—that combine laboratory experiments, field interventions, and meta-analytical methods to understand how individuals, particularly finance professionals, form expectations, perceive risk, and make decisions under uncertainty. Research Interests: Experimental and behavioural finance, with special attention to replicability of asset-market findings Risk preferences and risk perception among finance professionals and retail investors Meta-science topics such as non-standard errors, reviewer reliability, and computational reproducibility Policy-oriented behavioural interventions (nudging) in household finance and debt collection Across more than fifteen recent papers, Holzmeister’s research exhibits a clear focus on methodological rigour and transparency . His 2024 Journal of Finance article on non-standard errors—co-authored with over 150 colleagues—has already garnered thousands of downloads and citations, illustrating the broad interest in improving statistical inference in finance. A parallel strand of work uses preregistered experiments to test the robustness of classic asset-market findings, while additional studies explore how cognitive skills and personality traits shape fund-manager performance and how choice architecture influences portfolio allocation. Scientific Impact & Recognition: Top-3 000 SSRN author by total downloads (>24 000) Top-10 500 SSRN author by total citations (>100) Lead or co-author on large multi-institutional studies featured in top finance journals Collaboration & Funding: Holzmeister routinely leads interdisciplinary teams involving institutions such as the Stockholm School of Economics, VU Amsterdam, HEC Paris, the University of Gothenburg, and Copenhagen Business School. Funding acknowledgements in his papers imply support from national science foundations and European research councils, although specific grant numbers are not detailed in the present text. Laboratory & Research Environment: He conducts experiments within the University of Innsbruck’s experimental-economics laboratory infrastructure and is affiliated with cross-university consortia such as the “Non-Standard Errors Project” and the “Researcher Variation in Economics” consortium, which bring together dozens of scholars to tackle methodological challenges in economics and finance.

Lawrence Hayesمشاهده پروفایل

مدرس

• Chronic Disease Management
• Exercise Physiology
• mHealth Technology
• +۵ مورد دیگر

Dr. Lawrence Hayes is a Lecturer in Physiology at Lancaster Medical School, part of Lancaster University's Faculty of Health and Medicine. His research focuses on chronic condition management using mHealth approaches and developing exercise strategies to prevent disease. Key areas include endocrinology, exercise biochemistry, HIIT, and nutrition. He holds a PhD from the University of the West of Scotland (2014) and MSc/BSc qualifications from Manchester Metropolitan University. Dr. Hayes has secured significant research funding, including £500,000 from NIHR for a Long COVID trial and £300,000 in equipment grants. He serves as an external examiner for multiple universities’ sports medicine and exercise science programs. His work appears in journals like The American Journal of Medicine and Frontiers in Aging . Recent studies explore pacing strategies for ME/CFS, Long COVID symptom tracking, and digital interventions in elderly populations. His editorial roles include contributions to Frontiers in Physiology and COVID Journal .

Rhiannon Owenمشاهده پروفایل

استاد

• Biostatistics
• Health Technology Assessment
• Bayesian Methods
• +۶ مورد دیگر

Professor Rhiannon Owen holds a Personal Chair in Health Data Science at Swansea University Medical School, affiliated with Health and Care Research Wales (HCRW) as a Senior Research Leader and NIHR Advanced Fellow. Her research focuses on Bayesian methods in Health Technology Assessment, evidence synthesis, and large-scale health data analysis, supported by major funding bodies including the Medical Research Council (MRC) and Wellcome Trust. She advises NICE committees and provides strategic guidance to pharmaceutical/healthcare sectors. Rhiannon teaches biostatistics and health data analysis across undergraduate, postgraduate, and professional development programs, leveraging her expertise in translating statistical methods into clinical practice. Key research themes include multimorbidity patterns using electronic health records, vaccine safety surveillance, and economic modeling. Her work has produced influential frameworks like the Wales Multimorbidity e-Cohort (WMC) and tools such as MetaInsight for interactive meta-analysis visualization. Award highlights include Fellowship of the Learned Society of Wales and Higher Education Academy recognition. She collaborates internationally on projects involving linked datasets and has secured grants totaling over £5 million. Rhiannon leads Swansea's Health Data Science team, fostering cross-sector partnerships between academia, industry, and policymakers.

Sabrina McNewمشاهده پروفایل

استادیار

• Disease Ecology
• Behavioral Ecology
• Epigenetics
• +۵ مورد دیگر

Sabrina McNew is an Assistant Professor in the Department of Ecology and Evolutionary Biology at the University of Arizona (2023–present). She holds a Ph.D. in Biology from the University of Utah (2018) and a B.A. in Biology from Pomona College (2009). Her research focuses on disease ecology of birds and their parasites, emphasizing behavioral defenses, transcriptomic/epigenetic responses to stressors, and host-parasite community dynamics. She conducts field experiments and genomic analyses in arid/island ecosystems like the Galápagos and Sky Islands. Research Themes: Behavioral ecology of host defense mechanisms Epigenetic and transcriptomic responses to environmental stressors Host-parasite interactions in changing ecosystems Conservation applications in island and arid environments Key Achievements: Rose Postdoctoral Fellowship (2017) NSF Graduate Research Fellowship (2013–2017) Fulbright Fellow in Ecuador (2009) Laboratory Activities: The McNew Lab emphasizes interdisciplinary approaches, including comparative genomics and creative analytical methods. They collaborate with institutions like the Cornell Lab of Ornithology and focus on conservation-relevant questions. Visit McNew Lab Website for more details.

• Health Informatics
• Health Data Analytics
• Meta-analysis
• +۷ مورد دیگر

Samuel Stewart, PhD, is an Associate Professor and Director of Health Data Nova Scotia in the Department of Community Health and Epidemiology at Dalhousie University’s Faculty of Medicine. He specializes in health informatics, with expertise in secondary health data, meta-analysis, and health data analytics. His research bridges technical and clinical domains, focusing on improving healthcare decision-making through data-driven approaches. Education: PhD in Health Informatics, Dalhousie University (2014) MMath, University of Waterloo (2008) BSc, University of King’s College (2006) Research Interests: Secondary health data utilization Knowledge translation and meta-analysis methodologies Machine learning and natural language processing in healthcare Social network analysis in clinical communities Awards: 2015 Canada Health Infoway Data Impact Challenge Runner-Up ($10k) 2013 Best Paper, ISHIMR International Symposium 2012 Steven Huesing Scholarship (Top Canadian Graduate Student in Health Informatics) Teaching & Service: Course instructor for Biostatistics, Health Informatics, and Data Mining Dalhousie REDCap Server Administrator Member, Health Sciences Research Ethics Board Leadership in interprofessional deprescribing research initiatives Labs & Teams: Leads Health Data Nova Scotia, fostering collaborative research across institutions. Active in the Canadian Network for Observational Drug Effect Studies (CNODES), focusing on pharmacoepidemiology and research impact measurement.

Ying Dingمشاهده پروفایل

دانشیار

• Biostatistics
• Precision Medicine
• Survival Analysis
• +۶ مورد دیگر

Ying Ding is an Associate Professor of Biostatistics at the University of Pittsburgh's Dietrich School of Arts and Sciences, serving as Associate Dean for Graduate Academic Affairs and Faculty Senate President. She holds a Ph.D. from the University of Michigan (2010), an M.A. from Indiana University (2005), and a B.S. from Nanjing University (2003). Her research focuses on semiparametric methods for time-to-event data , precision medicine , multi-omics data integration , and deep learning in causal inference . Collaborative work spans cancer studies, neuropsychiatric disorders (e.g., schizophrenia), aging-related research (e.g., Alzheimer's disease and age-related macular degeneration), and pediatric pulmonary diseases (e.g., asthma). Dr. Ding has secured significant funding, including: R01GM141076 (2022–2026): $800,000 for statistical methods in multivariate survival data NIH/NEI R21EY030488 (2020–2023): $270,000 for AMD progression prediction R03MH108849 (2016–2018): $100,000 for proteomics analysis in schizophrenia Her awards include the 2021 James L. Craig Excellence in Education Award and 2022 Delta Omega Honor Society induction. Her work bridges statistical methodology with clinical applications, emphasizing reproducibility and translational impact. Key educational contributions include teaching courses like Applied Survival Analysis and Biostatistics Seminar. She advises students on projects combining statistical theory with real-world health challenges.