جست‌وجوی استاد و پژوهشگر

• Ophthalmology
• Corneal Endothelial Health
• Cataract Surgery Outcomes
• +۳ مورد دیگر

Clinical Associate Professor Keith Ong is affiliated with the University of Sydney’s Northern Clinical School, specifically within the Department of Clinical Ophthalmology & Eye Health. His clinical specialty is Ophthalmology, with a focus on corneal endothelial health, glaucoma surgery outcomes, and cataract surgical techniques. His research addresses critical areas such as postoperative ocular toxicity, intraocular lens calculations, and infection control in eye surgeries. Dr. Ong’s publications (2011–2023) consistently explore topics like corneal endothelial changes post-trabeculectomy, refractive error management post-cataract surgery, and antibiotic prophylaxis strategies. Recent work emphasizes optimizing surgical techniques to reduce complications, such as BAK-induced corneal toxicity and IOL power calculation accuracy in LASIK patients. His work demonstrates a focus on improving surgical safety and precision, with studies evaluating alternative antibiotic delivery methods and laser therapy impacts. No awards or grants are explicitly noted in the provided text, but his contributions to clinical practice and surgical protocols are evident through his publication record.

• Ophthalmology
• Corneal Surgery
• Laser Vision Correction
• +۳ مورد دیگر

Dr. John Males is a Clinical Senior Lecturer at the University of Sydney and Director of Ophthalmology Training at Sydney Eye Hospital. His clinical and research expertise focuses on corneal surgery, cataract surgery, and laser vision correction. He pioneered Descemet's Stripping Automated Endothelial Keratoplasty (DSAEK) in Australia and is conducting clinical trials on Collagen Cross-Linking for keratoconus. His research spans corneal transplantation techniques, refractive error management, and ocular surface regeneration. Dr. Males holds roles as a consultant at major hospitals and serves as an examiner for the Royal Australian & New Zealand College of Ophthalmologists. Research interests include corneal biomechanics, surgical innovation for keratoconus, and outcomes analysis of cross-linking therapies. He leads studies on corneal densitometry, dry eye correlations, and long-term efficacy of surgical interventions. His work bridges clinical practice with translational research in regenerative medicine and ocular surface engineering. Publications span over 20 years, emphasizing corneal transplantation advancements, keratoconus management, and surgical complication resolution. His contributions to global consensus guidelines for endothelial keratoplasty highlight leadership in standardizing surgical practices.

• Maternal Fetal Medicine
• Ultrasound
• Preeclampsia
• +۳ مورد دیگر

Giuseppe Rizzo is a Full Professor at the Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome. His academic career focuses on Maternal Fetal Medicine, with expertise in ultrasound applications, fetal growth restriction, preeclampsia, and congenital anomalies. Research Interests : Delivery optimization, prematurity prediction, ultrasound diagnostics, and placental anomalies. Recent Publications : Over 15 high-impact studies in 2024-2025 on topics like umbilical cord abnormalities, cerebroplacental ratios, and gestational diabetes complications. Clinical Expertise : Prenatal counseling, Doppler ultrasound, and labor management protocols. Collaborations : Active in multicenter trials across Italy and Europe, with a focus on fetal neurosonology and high-fidelity obstetric simulation.

Heidi Stöcklمشاهده پروفایل

استاد

• Public Health Evaluation
• Intimate Partner Violence
• HIV/AIDS Research
• +۴ مورد دیگر

Heidi Stöckl is a Professor of Public Health Evaluation at Ludwig Maximilian University of Munich (LMU) and holds an Honorary Professorship at the London School of Hygiene and Tropical Medicine (LSHTM). She leads the Public Health Evaluation group at LMU's Institute for Medical Information Processing, Biometry and Epidemiology (IBE), and serves as a Special Graduate Faculty member at the University of Guelph, Canada. Her research focuses on gender-based violence, HIV/AIDS, and health systems strengthening in low- and middle-income countries. Stöckl has led the WHO Collaborating Center on Research on Gender-Based Violence and Health and directed the Gender Violence & Health Centre at LSHTM. She has extensive experience in global health research, including modeling violence prevalence and investigating violence's impact on HIV transmission and maternal health. Education: 2006-2009: DPhil in Evidence-based Social Intervention, University of Oxford (Nuffield College) 2005: MSc in Sociology, University of Oxford 2000-2005: Diploma in Political Science, Free University of Berlin and LMU Munich Research Interests: Prioritizes intimate partner violence (IPV), sexual violence, and their intersections with HIV/AIDS, mental health, and socioeconomic factors. Her work addresses structural determinants of violence, including economic inequality and gender norms, with a focus on sub-Saharan Africa and Southeast Asia. Methodologically, she combines epidemiological approaches with qualitative studies to inform policy and intervention design. Key Contributions: Has published over 100 peer-reviewed articles, including seminal work on global IPV prevalence, child sexual violence meta-analyses, and the health impacts of trafficking. Her team's Burden of Proof study quantified violence's health effects, influencing global health metrics. She also leads initiatives on pandemic-related violence and health system resilience.

• Medical Imaging
• Deep Learning
• Computer Vision
• +۳ مورد دیگر

Madeleine Wyburd is an Associate Member of the Department of Computer Science at the University of Oxford. Her research focuses on advancing medical imaging technologies through deep learning and computer vision, particularly in the domain of fetal ultrasound analysis. She specializes in developing algorithms for 3D ultrasound reconstruction, anatomically plausible segmentation, and automated assessment of fetal brain development. Her work bridges computer science and clinical medicine, emphasizing applications in obstetrics and prenatal care. Key contributions include techniques like RapidVol for real-time 3D ultrasound volume reconstruction and TEDS-Net, a topology-preservation network for medical image segmentation. She also explores test-time adaptation methods to improve subcortical segmentation accuracy in fetal brain imaging. Wyburd's research integrates interdisciplinary methodologies from biomedical engineering and algorithm design. Recent projects analyze cortical plate development in second-trimester fetuses and compare 3D ultrasound with MRI volumetric measurements to enhance clinical diagnostic reliability. Her work aims to improve prenatal care through AI-driven tools that standardize fetal biometry assessment and reduce human error in clinical workflows. She collaborates with global institutions, as evidenced by her participation in the 34th World Congress on Ultrasound in Obstetrics and Gynecology. Her studies often address practical challenges like sparse-sampling in intrapartum ultrasound and normative brain maturation tracking up to 2 years post-birth.

• Medical Biometry
• Epidemiology
• Cognitive Dysfunction
• +۴ مورد دیگر

Insa Feinkohl is a Professor at the Chair of Medical Biometry and Epidemiology within the Faculty of Health at the University of Witten/Herdecke . Her research focuses on risk factors for cognitive dysfunction and mental health in older adults, particularly post-surgery, with emphasis on metabolic and cognitive risk factors. Bachelor of Science (BSc) in Psychology (1 st class honors) – University of Dundee (2006-2009) Master of Science (MSc) in Psychology of Individual Differences (with distinction) – University of Edinburgh (2009-2010) PhD in Community Health Sciences – University of Edinburgh (2010-2014) Post Doc in Knowledge Construction Group – Leibniz Institute for Knowledge Media, Tübingen (2014-2015) Postdoc in Molecular Epidemiology Group – Max Delbrück Center, Berlin (2015-2022) Habilitation in Molecular Epidemiology – Charité Universitätsmedizin Berlin (2021) Her research integrates medical biometry and epidemiology to study postoperative cognitive dysfunction (POCD), delirium, and aging-related cognitive decline. Key areas include biomarker validation (e.g., leptin, interleukins), brain connectivity (dopaminergic networks, thalamus), and metabolic risk factors (diabetes, obesity). She contributed to the BioCog project , an EU-funded initiative for personalized risk prediction of postoperative cognitive impairment. Her recent publications highlight trends in perioperative neuroscience, including brain mineralization, cytokine associations with neurocognitive disorders, and structural/functional imaging in delirium. Articles also explore metabolic syndrome, cognitive reserve, and delirium prediction models using machine learning. Insa Feinkohl is affiliated with major academic societies, including the German Society for Epidemiology , German Society for Medical Informatics, Biometry and Epidemiology , and the German University Association .

• Health Services Research
• Decision Science
• Cancer Screening
• +۵ مورد دیگر

Anna N. A. Tosteson serves as Professor of The Dartmouth Institute, Professor of Medicine, and Professor of Community and Family Medicine at Dartmouth's Geisel School of Medicine, holding the James J. Carroll 1948 Professorship in Oncology. She directs the Comparative Effectiveness Research Program at The Dartmouth Institute for Health Policy and Clinical Practice, serves as Associate Director for Population Sciences at Dartmouth Cancer Center, and is Principal Investigator for SYNERGY (Dartmouth's Clinical and Translational Science Institute). Her educational background includes an ScD in Biostatistics (Health Decision Sciences) and MS in Biostatistics from Harvard University, plus a BS in Statistics & Biometry from Cornell University. She teaches PH 119/121 and QBS 140 on Decision and Cost-Effectiveness Analysis. As a decision scientist and health services researcher, Tosteson investigates healthcare innovation diffusion impacts on health outcomes, costs, and quality through diverse data sources including registries, EHRs, clinical studies, and claims data. Her research focuses on cancer screening, musculoskeletal disease, and women's health with emphasis on comparative effectiveness. Recent publications reveal strong trends in telehealth accessibility disparities, risk-based cancer screening protocols, rural-urban care access gaps, and diagnostic pathway optimization - particularly examining digital divides in mammography follow-up and cross-state cancer care travel patterns. Her active NIH-funded research includes Promoting follow-up of abnormal cancer screening tests using population-based systems (NCI: U01CA225451) and Risk-based breast cancer screening and surveillance in community practice (NCI: P01CA154292). She mentors through SYNERGY's CTSA program and leads population sciences initiatives at Dartmouth Cancer Center.

Sophia Banoمشاهده پروفایل

استادیار

• Computer Vision
• Surgical Robotics
• Medical Imaging
• +۳ مورد دیگر

Sophia Bano is an Assistant Professor in Robotics and Artificial Intelligence at the Department of Computer Science, University College London (UCL), since November 2022. She is affiliated with the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), Surgical Robot Vision group, UCL Robotics Institute, and the Centre for Artificial Intelligence. Previously, she was a Senior Research Fellow at WEISS, contributing to the GIFT-Surg project. Her research focuses on AI-driven techniques for context awareness, navigation, and surgical robotics in minimally invasive procedures, including endoscopic workflow analysis, 3D reconstruction, and surgical vision systems. Education: BEng in Mechatronics Engineering (NUST, Pakistan), MSc in Electrical Engineering (NUST), MSc in Computer Vision and Robotics (Erasmus Mundus VIBOT), PhD from Queen Mary University of London and Technical University of Catalonia (Erasmus Mundus Fellowship). Post-doctoral work at University of Dundee (EPSRC ACE-LP project) and Imperial College London (ERC STING project). Research interests include computer vision for surgery, medical imaging, surgical robotics, and AI in healthcare. She leads the UKRI EPSRC-funded 'AI-enabled Decision Support in Pituitary Surgery' project and contributed to the GIFT-Surg and CARES projects. Publications span 3D reconstruction in fetoscopy, surgical workflow recognition, and AI models for medical imaging. Awards include the IJCARS-MICCAI Best Paper Award (2020) and the Best Innovation Award at the 2022 Surgical Robot Challenge. She organizes conferences like EndoVis and serves as a reviewer for journals like IEEE Transactions on Medical Imaging and MICCAI.

Amy Sheppardمشاهده پروفایل

مدرس ارشد

• Digital Eye Strain
• Ocular Accommodation
• Presbyopia
• +۳ مورد دیگر

Dr. Amy Sheppard is a Senior Lecturer at Aston Optometry School within Aston University's College of Health and Life Sciences. She serves as Deputy Head (Education) and maintains UK optometry registration. Her academic appointments include roles as External Examiner for the University of Central Lancashire's MSci Optometry programme and iPRO research adviser for the College of Optometrists. She also chairs Vision Care for Homeless People. Education: 2018: Master's in Education with Distinction (Aston University) 2014: Senior Fellow of Higher Education Academy 2010: PhD in Optometry (Aston University) 2003: BSc (Hons) Optometry, First Class Honours (Aston University) Research Focus: Dr. Sheppard's work centers on anterior eye physiology with specialization in Digital Eye Strain, ocular accommodation mechanisms, and presbyopia management. Her investigations employ clinical optometry approaches to address contemporary vision challenges, particularly screen-related visual fatigue and age-related focusing disorders. Recent work explores mobile health applications for vision assessment and AI integration in ophthalmic care. Publication Trends: Her recent articles demonstrate strong focus on digital eye health epidemiology, innovative myopia control interventions, presbyopia diagnostics, and technological applications in optometry. Collaborative work frequently addresses clinical guidelines development and vision-related public health challenges across different demographics. Awards & Honors: Senior Fellow of Higher Education Academy (2014) Fellow of UK Higher Education Academy (2013) Master's in Education with Distinction (2018) Academic Leadership: She leads modules in Clinical Practice (undergraduate) and Ageing Eye (postgraduate), supervises research dissertations, and directs educational initiatives. She accepts PhD students and contributes to curriculum development across optometry programs. Affiliations: Member of Optometry & Vision Science Research Group (OVSRG) and Aston Research Centre for Health in Ageing. Maintains active collaborations with national optometry organizations and international research teams.

Thomas J. Mariniمشاهده پروفایل

استادیار

• Medical Imaging
• Neuroradiology
• Artificial Intelligence in Radiology
• +۵ مورد دیگر

Thomas J. Marini, M.D. , is an Assistant Professor in the Department of Imaging Sciences at the University of Rochester Medical Center. He specializes in diagnostic radiology and neuroradiology, with a focus on expanding imaging access through teleultrasound and AI integration. Education M.D., University of Rochester, 2017 Internship, Transitional Year, St. Josephs Hospital and Health Center, 2017–2018 Residency, Diagnostic Radiology, University of Rochester Medical Center, 2018–2022 Fellowship, Neuroradiology, University of Rochester Medical Center, 2022–2023 Research Interests Dr. Marini’s research spans medical imaging , artificial intelligence , and telemedicine , particularly in low-resource settings. His recent work explores AI-driven ultrasound segmentation, pulmonary vasculature imaging, and telediagnostic systems for obstetrics, thyroid, and breast imaging. His publications highlight AI integration in ultrasound, automated fetal biometry , and imaging accessibility improvements in rural Peru. This aligns with his focus on democratizing diagnostic tools through technology. Scientific Awards Best Fellow Teacher Award, 2023 Cum Laude Award (American Society of Pediatrics), 2022 University of Rochester Imaging Sciences Excellence Award, 2022 Roentgen Research Award, 2022 Holman Research Pathway Certificate, 2022 Phi Beta Kappa Honor Society Induction, 2013

• Ultrasound
• Radiology
• Doppler Ultrasound
• +۳ مورد دیگر

Edward G. Grant, MD, serves as a Clinical Professor of Radiology (Part-Time) at the University of Southern California (USC), where he was Chair of the Department of Radiology from 2002 to 2019. Prior to USC, he chaired the Imaging Department at Greater Los Angeles VA Healthcare System and held professorial and vice-chair positions at UCLA. His career spans over three decades in academic radiology with a focus on clinical ultrasound innovation and leadership. Dr. Grant is an internationally recognized authority in ultrasound, having pioneered neonatal cranial sonography early in his career. His research encompasses Doppler ultrasound for vascular disease diagnosis, clinical investigation of ultrasound contrast agents, PACS, and advancements in thyroid and liver imaging. He has authored four books and numerous peer-reviewed articles, establishing himself as a leading figure in diagnostic radiology. Analysis of his 15 most recent publications (2021-2025) reveals a strong emphasis on contrast-enhanced ultrasound applications across multiple organ systems, particularly in thyroid nodule characterization (TI-RADS), liver disease quantification, and renal transplant evaluation. His work also addresses emerging areas like AI integration in ultrasound reporting and environmental impacts on fetal development through the MADRES cohort study. His notable awards include: Pasadena Magazine Top Doctor (2011) Dr. Grant has served as an examiner for the American Board of Radiology for 13 consecutive years and remains an active lecturer at national and international conferences. While the provided text does not detail specific grants or student advising, his extensive publication record and leadership roles indicate significant contributions to radiology education and clinical practice advancement.

• Medical Biometry
• Health Services Research
• Mental Health
• +۵ مورد دیگر

Anne Daubmann is a Research Fellow at the Institute of Medical Biometry and Epidemiology within the Faculty of Medicine at the University Medical Center Hamburg-Eppendorf (UKE) . Her work focuses on Medical Biometry and Health Services Research , with significant contributions to Mental Health , Multiple Sclerosis , and Primary Care domains. Her research spans: Digital health interventions for cancer patients Nutritional strategies in MS management Family-oriented mental health policies AI reliability in medical guidelines Resuscitation training methodologies Scientific Awards: Certificate in Medical Biometry (2020) from GMDS and IBS-DR She holds memberships in the International Biometric Society, German Region (IBS-DR) . Her methodological expertise includes statistical analysis in clinical trials and health outcomes evaluation .

Pirro Hysiمشاهده پروفایل

دانشیار

• Genetics
• Genomics
• Ophthalmology
• +۶ مورد دیگر

Dr. Pirro Hysi is a Reader (equivalent to Associate Professor) in Ophthalmology at King's College London, where he serves as School Academic Lead for Postgraduate Research within the School of Life Course & Population Sciences. His work bridges genetics, genomics, and ophthalmology, with a focus on understanding the genetic basis of eye diseases and related conditions. Dr. Hysi's research spans multiple areas of genetic ophthalmology and population genomics. His work primarily focuses on genome-wide association studies (GWAS) to identify genetic variants associated with eye conditions including myopia, glaucoma, keratoconus, and age-related macular degeneration. He also investigates the intersection of genetics with metabolomics, exploring how metabolic pathways influence ocular health and disease progression. His research has expanded into machine learning applications for disease prediction and the genetic basis of systemic conditions that impact eye health. His publication record demonstrates a strong focus on large-scale genetic studies, often leveraging data from the UK Biobank and other international cohorts. Recent work has expanded into applying machine learning approaches to predict disease progression and exploring the genetic basis of conditions beyond ophthalmology, including cardiovascular health, metabolic disorders, and pregnancy-related conditions. Dr. Hysi serves as Principal Investigator or Co-Investigator on multiple research projects funded by organizations including Fight for Sight, the BrightFocus Foundation, NIH, and the Fetal Medicine Foundation. His current projects include investigating metabolic factors in age-related eye diseases, developing vision measurement technology, and identifying glaucoma therapy targets through multi-omic analysis. He is actively involved in several major research consortia, particularly the UK Biobank Eye and Vision Consortium, and has contributed to numerous large-scale genetic studies across multiple medical specialties. His work has been featured in high-impact journals including Nature Communications, JAMA Ophthalmology, and Nature Medicine.

Christina Malamateniouمشاهده پروفایل

دانشیار

• Medical Imaging
• MRI
• Perinatal MRI
• +۱۱ مورد دیگر

Dr. Christina Malamateniou is a Reader (Associate Professor) in Radiography at City St George's, University of London, and an Adjunct Professor in Radiography at University College Cork. With over 20 years of experience in Medical Imaging as both an academic and researcher, she previously worked as an MRI radiographer at the University Hospital of Athens. She has taught Medical Imaging and Research Methods across 7 universities in 3 countries and serves as co-director for the postgraduate programme in Radiography at City University. Her educational background includes: PhD in Medical Imaging from Imperial College London MA in Clinical Education from King's College London BSc (Hons) in Diagnostic Radiography from Technological Educational Institute of Athens, Greece Senior Fellow of the Higher Education Academy (2018) Dr. Malamateniou is an expert in MRI research with specialization in brain MRI, perinatal MRI, placental MRI, and high field MRI. Her work bridges clinical practice with technological innovation, particularly in the application of Artificial Intelligence to radiography. She has a strong interest in clinical education, research mentoring, and building research capacity among healthcare practitioners. Her current research involves fetal MRI with King's College London, simulated practice with University of Greenwich, AI implementation with the Society and College of Radiographers, cardiovascular MRI with Oxford University, and MRI in autism with multiple institutions. Her extensive publication record demonstrates a clear trajectory toward integrating AI into radiography practice while maintaining a strong foundation in traditional MRI research. Recent work focuses on ethical AI implementation, inclusive research practices for neurodiverse populations, and addressing workforce challenges in global radiography. Dr. Malamateniou has received significant recognition for her contributions: Clinical MRI Prize of the British Institute of Radiology (2006) Leonard Levy Prize of the British Institute of Radiology (2006) City Extraordinary Women Award (2023) Senior Fellow of the Higher Education Academy As a dedicated mentor, Dr. Malamateniou has supervised 6 PhD students as primary or secondary supervisor, with 2 completions to date and more expected soon. She has secured substantial research funding totaling £2.8 million as either principal or co-investigator. Her leadership extends to numerous professional roles including Chair of the Radiographers' Scientific Programme Committee at the European Congress of Radiology (2016), Research Committee Chair for the European Federation of Radiographer Societies, and leadership of the Formal Radiography Research Mentoring (FoRRM) scheme. Through her work with the Society and College of Radiographers, she has co-authored national guidelines on perinatal MRI, patient and public engagement with research, and artificial intelligence in radiography. She also serves as Chair of the AI working group and has established fetal MRI services at multiple international hospitals.

Mujdat Cetinمشاهده پروفایل

استاد

• Computational Imaging
• Signal Processing
• Machine Learning
• +۵ مورد دیگر

Mujdat Cetin is a Professor of Electrical and Computer Engineering at the University of Rochester, serving as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science. Previously, he held faculty positions at Sabanci University, Istanbul, Turkey, and MIT. He earned his BS from Bogazici University (1993), MS from the University of Salford (1995), and PhD from Boston University (2001). His research focuses on computational imaging, signal processing, and machine learning applications in biomedical imaging, radar systems, and neuroscience. Key areas include uncertainty quantification in imaging, probabilistic methods for image analysis, and brain-computer interfaces. His work bridges electrical engineering, computer science, and biomedical applications. Notable awards include the IEEE Signal Processing Society Best Paper Award and the Turkish Academy of Sciences Distinguished Young Scientist Award. He is an IEEE Fellow and has held editorial roles for journals like IEEE Transactions on Computational Imaging and SIAM Journal on Imaging Sciences. His contributions span conference leadership, including Technical Program Co-chair roles for major signal processing and fusion events. Cetin’s research group has advanced computational sensing, medical imaging algorithms, and EEG-based emotion/motor imagery classification. Ongoing work emphasizes integrating physics-based models with deep learning for robust imaging and signal analysis.