جست‌وجوی استاد و پژوهشگر

Larissa Schlegel-Papeمشاهده پروفایل

پژوهشگر

• Poultry Science
• Animal Welfare
• Stress Assessment in Chickens
• +۴ مورد دیگر

Larissa Schlegel-Pape serves as a Scientific Associate at the Department for Ornamental and Pedigree Poultry within the Farm Animal Clinic of Freie Universität Berlin's Faculty of Veterinary Medicine. Her work focuses on developing innovative methods for assessing chicken welfare through the creation of the "Stressed Chicken Scale," which aims to systematically identify stress indicators in poultry. Her research interests center on animal welfare science, specifically stress assessment in chickens using both behavioral observation and computer vision technology. She investigates how body posture, movement patterns, and other visual indicators can reliably signal discomfort or stress in poultry, with the goal of creating practical assessment tools for veterinarians and poultry farmers. Her work bridges veterinary medicine, ethology, and technological innovation, contributing to refinement research (one of the 3Rs principles) in animal husbandry. Analysis of her publications reveals a strong focus on developing and validating the Stressed Chicken Scale across multiple contexts. Her work spans methodological development, practical implementation studies, and technological integration with computer vision systems. The research demonstrates progression from conceptual framework to validation studies and practical application, with increasing sophistication in assessment techniques and broader implications for animal welfare standards in poultry farming. Schlegel-Pape actively collaborates with the Federal Institute for Risk Assessment (BfR) and participates in interdisciplinary projects involving artificial intelligence applications in agriculture. She presents her findings regularly at major German veterinary conferences including the DVG (Deutsche Veterinärmedizinische Gesellschaft) events, DACh Epidemiology conferences, and specialized poultry medicine gatherings. Her work contributes significantly to advancing animal welfare assessment methodologies and promoting refinement in poultry husbandry practices.

Jim Smithمشاهده پروفایل

استاد

• Interactive Artificial Intelligence
• Statistical Disclosure Control
• Privacy Leakage from AI Models
• +۷ مورد دیگر

Jim Smith is a Professor in Interactive Artificial Intelligence at the University of the West of England (UWE), Bristol, affiliated with the School of Computing and Creative Technologies and the Department of Computer Science and Creative Technologies. He serves as Director of the Computer Science Research Centre and leads the AI@UWE theme. His research is supported by UKRI, Innovate UK, and partnerships with organizations including Health Data Research UK, Office for National Statistics, NHS Scotland, and DSTL. University: University of the West of England School: School of Computing and Creative Technologies Department: Department of Computer Science and Creative Technologies Role: Professor in Interactive Artificial Intelligence Leadership: Director, Computer Science Research Centre Research Interests : Jim Smith's work focuses on Interactive Artificial Intelligence, particularly at the intersection of AI and privacy preservation when using sensitive data for public good. His research includes statistical disclosure control, privacy leakage from AI models, evolutionary computation, machine learning, and systems that learn through human interaction or self-adaptation. He explores how AI can automate privacy checks in research outputs and assess vulnerabilities in trained models. Recent Publications : His recent work spans AI privacy in trusted research environments (e.g., SACRO, SDC-Reboot), dialogue act classification, human-robot interaction, and visualization of deep learning models. Themes include privacy-preserving AI, automated disclosure control, interactive machine learning, and neuromorphic computing. Machine Learning & Privacy Evolutionary Computation Interactive AI Systems Human-Computer Interaction Statistical Disclosure Control Federated Learning Security Scientific Awards : No specific awards are mentioned in the provided texts. Advising and Grants : He currently supervises PhD students on topics including spatio-temporal air quality modeling, federated learning privacy, and threat detection in mobile networks. He leads Innovate UK and UKRI-funded projects such as SACRO and SDC-Reboot, focusing on AI-driven solutions for data confidentiality in public sector research. Interactive Machine Learning for Claim Settlement (Innovate UK) SDC-Reboot (DARE UK/Health Data Research UK) Threat Identification in Mobile Networks (Ribbon Communications) Labs and Teams : He leads the AI@UWE initiative and the Computer Science Research Centre at UWE. His work involves collaboration through DARE UK and open-source development via the AI-SDC GitHub organization, which hosts tools from SACRO and GRAIMATTER projects.

• Biomechanics
• Human Kinetics
• Biomedical Engineering
• +۳ مورد دیگر

Dr. Ryan B Graham is an Associate Professor in the School of Human Kinetics at the University of Ottawa , with cross-appointments to the Ottawa-Carleton Institute for Biomedical Engineering . He holds Adjunct Assistant Professor positions at Queen's University and University of Waterloo , reflecting his interdisciplinary collaborations in biomechanics and biomedical engineering. His research focuses on spine movement analysis , motion capture validation , and sensor technology applications in clinical and military contexts. Recent work includes developing markerless motion capture systems, assessing movement reliability, and modeling spine dynamics for injury prevention. Editorial Contributions: Associate Editor for Biomechanics and Control of Human Movement at Frontiers in Sports and Active Living Dr. Graham's academic network includes collaborations with institutions in Canada, the UK, and the Netherlands, with a focus on advancing biomechanical methodologies and their clinical translation.

Yan Maمشاهده پروفایل

استاد

• Missing Data Imputation
• Machine Learning
• Meta-Analysis
• +۱۲ مورد دیگر

Yan Ma serves as Professor and Chair of Biostatistics at the University of Pittsburgh, with additional appointments in Orthopaedic Surgery and Clinical and Translational Science. Previously, he was Professor and Vice Chair at George Washington University Milken Institute of Public Health (2014-2022) and Assistant Professor at Hospital for Special Surgery/Weill Cornell Medical College (2008-2014). His educational background includes: PhD in Statistics, University of Rochester (2008) MA in Statistics, University of Rochester (2004) MS in Mathematics, Syracuse University (2003) BS in Statistics, Beijing Normal University (2001) Ma's research centers on advanced statistical methodologies including missing data imputation, machine learning, meta-analysis, causal inference, and longitudinal methods, applied across orthopedics, anesthesiology, health disparities, and emergency medicine through team science and translational research frameworks. His publication trajectory demonstrates sustained innovation from methodological foundations (2008-2012) to contemporary applications in health disparities and machine learning (2016-2022), consistently addressing complex biomedical challenges through high-impact journals like JAMA and Health Services Research. His scientific recognition includes: ASA's Statistics in Epidemiology Young Investigator Award (2010) Interorganizational Team Science Award (2012) ORISE FDA Research Fellowship (2017) APHA Achievement in Academia Award Ma has secured R01 funding from NIH/AHRQ for missing data methods in health disparities research while serving as Associate Editor for ASA journals and reviewer for NIH/PCORI/VA panels, demonstrating leadership in statistical methodology development and interdisciplinary collaboration. His team-science approach bridges statistical innovation with clinical implementation across orthopedics and anesthesiology, driving evidence-based practice through methodological rigor and cross-disciplinary partnerships.

Cecilia R. Aragonمشاهده پروفایل

استاد

• Human-Centered Data Science
• Human-Centered Artificial Intelligence
• Human-Computer Interaction
• +۵ مورد دیگر

Cecilia R. Aragon is a Professor in the Department of Human Centered Design & Engineering at the University of Washington, where she also serves as an Adjunct Professor in Computer Science & Engineering, Electrical and Computer Engineering, and the Information School. She is additionally a Senior Data Science Fellow at the eScience Institute. Aragon directs the Human-Centered Data Science Lab and has made significant contributions at the intersection of human-computer interaction and data science. Her research interests focus on human-centered data science, human-centered artificial intelligence, human-centered machine learning, human-computer interaction (HCI), computer-supported cooperative work (CSCW), visual analytics, aviation and astronautics sociotechnical systems, and emotion in informal text communication. Aragon's work bridges technical and social aspects of data science, particularly examining how humans interact with and gain insight from large datasets through both quantitative and qualitative methods. Aragon's recent publications demonstrate a strong focus on understanding online communities, sentiment analysis, distributed mentoring systems, and the ethical implications of AI. Her work spans multiple disciplines including social computing, data visualization, and astrophysics data analysis, showing her interdisciplinary approach to human-centered data science. Presidential Early Career Award for Scientists and Engineers (PECASE) 2008 Fulbright Fellowship 2017-18 HCDE Faculty Innovator in Research Award, University of Washington, 2015 Distinguished Alumni Award, Computer Science, University of California, Berkeley, 2013 Top 25 Women of the Year, Hispanic Business Magazine, 2009 Aragon has secured over $28 million in research funding from organizations including the National Science Foundation, National Institute of Standards and Technology, Department of Energy, Gordon and Betty Moore Foundation, Alfred P. Sloan Foundation, Washington Research Foundation, and industry partners like Microsoft and Intel. Her educational background includes a Ph.D. in Computer Science from UC Berkeley (2004), an M.S. in Computer Science from UC Berkeley, and a B.S. with Honors in Mathematics from Caltech. She leads the Human-Centered Data Science Lab and is affiliated with the eScience Institute, the Nearby Supernova Factory, and various research groups focused on data-intensive scientific collaborations. Her work on collaborative visual analytics systems like Sunfall has had significant impact in both academic and applied settings.

• Medical Imaging
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Dr. Neal Bangerter is a Visiting Professor in the Department of Bioengineering at Imperial College London's Faculty of Engineering. He specializes in medical imaging (MRI), artificial intelligence, machine learning, and signal processing. Dr. Bangerter holds adjunct appointments at INSEAD, the University of Utah, and Brigham Young University. His research focuses on ultra-high field MRI, AI applications in healthcare, and data-driven bioscience technologies. He leads the London Collaborative Ultra-High Field Scanner (LOCUS) project and advises companies on AI and innovation strategies. Education: B.S. in Physics (UC Berkeley), M.S. and Ph.D. in Electrical Engineering (Stanford University). Career highlights include roles at McKinsey & Company, Microsoft, and Reactrix, as well as founding BYU's Medical Imaging Research Center. He has pioneered cross-faculty initiatives like the Crocker Innovation Fellowship Program. Research interests include novel MRI pulse sequences, AI in medical imaging, and large-scale health data analysis. His work spans collaborations with Stanford, Oxford, Cambridge, and Siemens Healthcare. He teaches executive education at INSEAD, focusing on bridging technical concepts with business strategies. Key awards include the David Evans Chair at Brigham Young University. His contributions to the UK Biobank Neuroimaging study and development of MRI techniques like RAFO-4 highlight his impact on advancing imaging technologies and AI applications in healthcare.

Craig B. Birgfeldمشاهده پروفایل

استاد

• Craniofacial Surgery
• Plastic Surgery
• Pediatric Surgery
• +۳ مورد دیگر

Craig B. Birgfeld, M.D. , is a professor in the Department of Surgery at the University of Washington School of Medicine and serves as Director of the UW Fellowship in Craniofacial Surgery. He practices at the Burn and Plastic Surgery Clinics at Harborview Medical Center, specializing in congenital craniofacial abnormalities, trauma reconstruction, and pediatric plastic surgery. Educational Background: Virginia Commonwealth University School of Medicine (Medical Education, 2000) University of Washington - Fellowship in Craniofacial Surgery Hospital of the University of Pennsylvania - Residency in Plastic Surgery Hospital of the University of Pennsylvania - Internship in General Surgery Dr. Birgfeld's research focuses on Craniofacial Microsomia and outcomes analysis in surgical interventions for congenital skull and facial abnormalities. His clinical work emphasizes trauma reconstruction, cleft lip/palate surgery, and craniofacial plastic surgery. He actively teaches Plastic Surgery residents and Craniofacial fellows, aiming to cultivate top-tier surgeons in the field. Recent publications highlight advancements in craniosynostosis treatment, facial symmetry measurement, and outcomes in craniofacial surgical procedures. His work spans collaborations with multidisciplinary teams in neurosurgery, pediatrics, and genetic research.

Bradley V. Vaughnمشاهده پروفایل

استاد

• Sleep disorders
• Epilepsy
• Nocturnal events
• +۳ مورد دیگر

Dr. Bradley V. Vaughn is a Professor of Neurology at the University of North Carolina School of Medicine. His clinical and research focus spans sleep disorders, epilepsy, and neurological conditions affecting sleep physiology. Currently based in Chapel Hill, NC, he specializes in understanding the complex interactions between neurological diseases and sleep patterns. MD from Hershey Medical School Internship/Residency at Hershey Medical Center Chief Residency and Fellowship at UNC Research interests center on sleep medicine and neurological comorbidities , particularly exploring: Interactions between epilepsy and sleep cycles Polysomnography technical advancements Sleep disturbances in neurogenetic disorders Management of nocturnal neurological events His publications since 2004 demonstrate consistent contributions to understanding sleep architecture in neurological contexts and advancing precision medicine applications . Recent work (2022-2025) explores genetic correlations and cancer-related sleep disturbances. No specific awards or student advisement information appears in the provided materials.

• Pneumonia
• Clinical Decision-Making
• Medical Informatics
• +۳ مورد دیگر

Dr. Barbara E. Jones serves as an Associate Professor in the Department of Internal Medicine at the University of Utah School of Medicine, with dual appointments in Pulmonary and Critical Care Medicine. Her clinical practice spans diverse healthcare settings within the Veterans Affairs system and academic medical centers, focusing on evidence-based adaptation of care to varied patient populations. Her educational background includes: M.D. from University of Washington School of Medicine B.A. in Philosophy from Dartmouth College Master of Science in Clinical Investigation (M.S.C.I) from University of Utah Postdoctoral Fellowship in Pulmonary and Critical Care Medicine at University of Utah Residency in Internal Medicine at University of Utah Dr. Jones' research centers on decision-making processes in pneumonia diagnosis and treatment, employing a tripartite informatics approach combining population analytics, cognitive behavior analysis, and clinical decision support systems. Her work specifically targets reducing diagnostic uncertainty and treatment variation across healthcare systems, with emphasis on equitable care delivery for diverse patient populations. Current projects investigate diagnostic discordance in community-acquired pneumonia, electronic surveillance for hospital-acquired infections, and machine learning applications for diagnostic error detection. Analysis of her 15 most recent publications reveals consistent focus on pneumonia management systems, with emerging emphasis on pandemic impacts on diagnostic practices and AI-driven quality improvement. Her work predominantly utilizes large VA healthcare datasets spanning 100+ medical centers, featuring mixed-methods approaches that integrate quantitative analytics with qualitative clinician experience assessment. Dr. Jones actively contributes to clinical guideline development and medical education through editorial work in major journals including Chest and Annals of Internal Medicine , where she frequently addresses controversies in pneumonia diagnosis and antibiotic stewardship. Her research program operates at the intersection of the University of Utah Health system and the Veterans Affairs national healthcare network, leveraging electronic clinical decision support implementations across diverse hospital settings including rural and critical access facilities. Current initiatives focus on real-time feedback systems for diagnostic performance improvement and automated surveillance for healthcare-associated infections.

• Critical Care Neurology
• Electroencephalography (EEG)
• Neuroimaging
• +۸ مورد دیگر

Dr. Jennifer Ahjin Kim is an Assistant Professor in the Department of Neurology at Yale School of Medicine. She specializes in neurocritical care, focusing on quantitative analysis of electroencephalography (EEG) and neuroimaging for early diagnosis and treatment optimization in patients with severe neurologic injuries. Her clinical interests include traumatic brain injury, subarachnoid hemorrhage, stroke, and post-traumatic epilepsy. PhD in Neuroscience, Brown University (2012) MD from Brown University (2012) Neurology Residency, Massachusetts General Hospital/Brigham & Women's Hospital (2016) Neurocritical Care Fellowship, Massachusetts General Hospital/Brigham & Women's Hospital (2019) Dr. Kim’s research integrates multimodal data (EEG, MRI, CT) with machine learning to predict secondary complications after brain injuries. She actively contributes to clinical trials like BOOST3 and ASPIRE, aiming to improve outcomes for patients with traumatic brain injury, stroke, and hemorrhage. Her recent work emphasizes automated detection of epileptiform discharges, predictive modeling for delayed cerebral ischemia, and application of NLP to CT reports. Collaborations include frequent partnerships with Guido Falcone, Lawrence Hirsch, and Emily Gilmore. Dr. Kim leads the Kim Laboratory, which focuses on bedside monitoring technologies and secondary prevention strategies.

Elena A. Eroshevaمشاهده پروفایل

استاد

• Hierarchical Modeling
• Longitudinal Data Analysis
• Latent Variable Modeling
• +۴ مورد دیگر

Elena A. Erosheva is a Professor in the Department of Statistics and Social Work at the University of Washington , where she also serves as the Associate Director of the Center for Statistics and the Social Sciences (CSSS). Her research focuses on hierarchical modeling , longitudinal data analysis , and latent variable models , with applications in social sciences, disability research, and health statistics. She has taught courses like Multivariate Data Analysis, Sample Survey Techniques, and Statistical Modeling with Latent Variables. Education : PhD in Statistics, University of Washington (2002) Current Research : Measuring gender-based homophily in collaborations, modeling life course transitions, partial mastery cognitive diagnosis models, and bias detection in grant review Scientific Awards : 2019 NIH Center for Scientific Review First Prize for Most Creative Idea for Detection of Bias in Peer Review 2013 Mitchell Prize, International Society for Bayesian Analysis 2015 ASA Student Paper Award (with Maryclare Griffin) Her students and postdoctoral researchers include Sheridan Grant, Y. Samuel Wang, and Jonathan Gruhl. She uses R software extensively in her teaching and research. Contact: erosheva@uw.edu | Personal Website

Rosemary Xorlanyo Doe-Asinyoمشاهده پروفایل

مدرس

• Child Occupational Therapy
• Neurodevelopmental Disabilities
• Motor Performance Assessment
• +۳ مورد دیگر

Rosemary Xorlanyo Doe-Asinyo serves as a Lecturer (Education) in Occupational Therapy within the Department of Health Sciences at Brunel University's College of Health, Medicine and Life Sciences, bringing over six years of experience in teaching and clinical placement coordination. A pioneer in Ghana's occupational therapy education, she completed foundational training as part of West Africa's first BSc occupational therapy cohort and gained UK clinical experience through a placement with North Umbria Healthcare NHS Foundation Trust. Her academic credentials include: MSc in Occupational Therapy from the University of Cape Town, South Africa Ms. Doe-Asinyo's research centers on neurodevelopmental disabilities in school-aged children, with emphasis on Developmental Coordination Disorder (DCD), motor performance, physical fitness metrics, and the therapeutic role of active play. She specializes in developing culturally responsive assessment tools and interventions for diverse pediatric populations, while advocating for expanded occupational therapy education frameworks and intra-professional fieldwork collaboration across practice settings. Her 2021 publications in Heliyon and BMC Pediatrics establish the PERF-FIT test battery as a validated instrument for measuring motor skill-related physical fitness in children, demonstrating strong ecological validity and reliability metrics. These works reflect her commitment to evidence-based pediatric assessment methodologies that bridge clinical practice and research. Professional leadership defines her career through roles as inaugural president of the Occupational Therapy Association of Ghana (OTAG), delegate for the World Federation of Occupational Therapists (WFOT), and current OTAG board member. Concurrently, she maintains active clinical practice as a private occupational therapist specializing in children and young people with neurodevelopmental disabilities and their families.

Rosanna Coleمشاهده پروفایل

مدرس ارشد

• Sustainable Supply Chain Management
• Socially Responsible Sourcing
• Blockchain Technology
• +۷ مورد دیگر

Rosanna Cole is a Senior Lecturer in Sustainable Supply Chain Management at the Department of Business Analytics and Operations, Surrey Business School, University of Surrey. She holds roles as Deputy Head of the Department of Business Analytics and Operations, Sustainability Fellow at the Institute for Sustainability, and Deputy Head of Business Transformation (2019-2020). Her research focuses on sustainable supply chain management, socially responsible purchasing, modern slavery eradication, and blockchain applications in supply chains. Previously, Cole worked at Roehampton Business School (2008-2016) and Deloitte LLP. She is a Senior Fellow of the HEA, Sustainability Fellow, and completed the Advanced Management Programme (2024). Her awards include Surrey Business School’s EC Researcher of the Year (2019). Research interests include supply chain intermediary roles, blockchain adoption, labor exploitation mitigation, and sustainable procurement practices. She collaborates with institutions like the University of Manchester, Lancaster University, and Cranfield University. Her work addresses global challenges such as refugee integration into supply chains and supply chain turbulence linked to geopolitical issues. Teaching includes modules on sustainable operations, supply chain governance, and ethics. She has advised PhD students at institutions like Deusto Business School and the University of Gloucestershire. Key projects include ROADS UK (asphalt sustainability), Syrian refugee workforce integration, and blockchain in healthcare device reverse logistics. Her articles span risk matrix improvements, labor exploitation governance, and temporal production dynamics.

Anne McAlisterمشاهده پروفایل

استادیار

• Engineering Education
• Engineering Identity
• Sociotechnical Thinking
• +۳ مورد دیگر

Anne McAlister is an Assistant Professor in the First Year Engineering Center at the University of Virginia. Her research focuses on engineering identity, teamwork, and sociotechnical thinking to broaden participation in engineering education. She holds a Ph.D. in Education and M.S. in Systems Engineering from the University of Virginia, and a B.S. in Chemical Engineering from The Ohio State University. Dr. McAlister’s work addresses barriers to success in engineering education and strengthens sociotechnical thinking among first-year students. Her research interests center on understanding and challenging systemic obstacles in engineering education, particularly regarding identity formation and inclusive pedagogies. Key areas include engineering culture’s impact on mental health, interdisciplinary science and engineering integration in K-12 classrooms, and graduate student role identity development. Recent publications explore elements of engineering culture affecting student mental health, inclusive classroom practices in NGSS-aligned curricula, and the multifaceted roles of engineering doctoral students. She teaches ENGR 1010: Engineering Foundations 1, emphasizing foundational skills for first-year engineering students. Dr. McAlister’s work has been published in leading journals like the Journal of Engineering Education and Science Education . Her research methodologies include mixed methods and grounded theory approaches to study identity development and educational equity.

Xin Gaoمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Statistical Genetics
• +۵ مورد دیگر

Xin Gao is a Professor in the Department of Mathematics and Statistics at York University, Toronto. His research focuses on Artificial Intelligence , Machine Learning , and Statistical Genetics , with applications in biomedical data analysis and planetary science. He leads the Artificial Intelligence and Machine Learning Lab , which has developed impactful tools like an online Type 2 Diabetes risk predictor using logistic regression and Mars rock composition analyzers for NASA. His methodological work includes penalized composite likelihood and multi-task feature learning , implemented in R packages FusionLearn and lassoGEE . Scientific Awards NSERC Discovery Acceleration Award ($120,000, 2018-2020) Key Software Contributions FusionLearn : Correlated multi-task feature learning lassoGEE : High-dimensional clustered/longitudinal data analysis Notable Collaborations Vector Institute (AI scholarship mentoring) Fields Institute (committee roles) International genomic data integration projects