جست‌وجوی استاد و پژوهشگر

Pola Goldberg Oppenheimerمشاهده پروفایل

استاد

• Bio-Nanotechnology
• Micro-Engineering
• Nanomaterials Engineering
• +۳ مورد دیگر

Professor Pola Goldberg Oppenheimer is a leading academic in Micro-Engineering and Bio-Nanotechnology at the School of Chemical Engineering, University of Birmingham , and holds a prestigious Royal Academy of Engineering Research Fellowship . She leads an interdisciplinary team collaborating with clinical and industrial partners (e.g., Queen Elizabeth Hospital, BAE Systems) to develop nanomaterials for advanced healthcare applications such as rapid diagnostics for traumatic brain injury (TBI). Her research bridges nano-to-macro engineering, focusing on electrohydrodynamic lithography and smart nanostructured devices . Education: PhD (2012, University of Cambridge), MPhil (Ben-Gurion University), dual BSc (Chemistry & Chemical Engineering). Awards include the RAEng Fellowship, Beilby Medal, and WomenTech100 recognition. She has secured over £18M in funding, with >70 publications in top journals like Advanced Materials and Nature Biomedical Engineering . Her research group (ANMSA) pioneers innovations in nanocomposite materials , point-of-care sensors , and biomedical device fabrication . Notable contributions include developing optofluidic gold-architectured substrates for TBI detection and hierarchical nanostructuring techniques. She actively engages in policy advising (RAEng, NHS collaborations) and public outreach, including BBC features and STEM mentorship. Awards section highlights: 2021 Royal Society of Chemistry Beilby Medal 2020 WomenTech100 Award 2016 RAEng Fellowship 2013 Birmingham Fellowship Grants and collaborations: Over £18M funding portfolio includes partnerships with Dstl, P&G, and BAE Systems. She leads the Healthcare Technologies Institute (HTI), fostering cross-disciplinary innovation in healthcare technologies.

• Mathematical Statistics
• Biostatistics
• Medical Research
• +۳ مورد دیگر

Lu Ting is a Professor at the Department of Mathematics within the Courant Institute of Mathematical Sciences, New York University. Their research integrates mathematical statistics and biostatistics with applications in oncology, environmental health, and precision medicine. Key focuses include molecular pathway analysis in glioma, biomarker development for drug resistance, and statistical methods in translational research. Research interests span mathematical modeling of cancer therapies, environmental exposure impacts on human health, and optimization algorithms. Notable projects include longitudinal studies on World Trade Center (WTC) exposure effects and sphericity testing in high-dimensional covariance matrices. Publications from 2015–2024 reflect interdisciplinary work combining mathematical rigor with biomedical challenges, particularly in cancer treatment optimization and environmental health surveillance. No scientific awards or grants are explicitly documented in the provided text.

Louisa Lamمشاهده پروفایل

دانشیار

• Cardiovascular disease diagnosis and management
• Emergency nursing
• Coronary care
• +۱۷ مورد دیگر

Louisa Lam is an Associate Professor in Nursing at the Australian Catholic University , Faculty of Health Sciences, School of Nursing, Midwifery and Paramedicine (Victoria). She holds adjunct Associate Professor positions at Monash University and Federation University Australia , reflecting her extensive collaborative research and academic leadership. PhD in Medicine (Public Health), Australian Catholic University Master of Public Health, Monash University Postgraduate Diploma in Advanced Clinical Nursing (Coronary Care) Graduate Certificate in Higher Education Her research focuses on cardiovascular disease management, emergency and intensive care, clinical trials, and nursing workforce wellbeing . She is particularly known for her work on burnout, resilience, workplace aggression, and shared decision-making in mental health . Her expertise in systematic reviews and meta-analyses strengthens evidence-based practice in nursing and public health. The 15 most recent publications reflect a strong trend toward mental health, workforce wellbeing, education interventions, and public health during and after the pandemic . Key areas include psychological distress among healthcare workers, resilience training, nursing education, and organizational strategies for patient safety. Her work often involves cross-country collaborations, especially with researchers in China and Southeast Asia. Scientific Awards: Top cited article 2019-2020, Journal of Psychiatric & Mental Health Nursing Dean's Recognition Award 2020, Federation University 2009 Research Week Prize, The Alfred Centre and Monash University Advising and Grants: Louisa Lam has full HDR supervisory accreditation and has mentored numerous PhD and Master’s students. She serves as a grant reviewer for the Ministry of Health, Singapore, and is actively involved in research funding evaluation. Her editorial role as an editor for Collegian further demonstrates her scholarly influence. Labs and Teams: She collaborates with multi-institutional and international research teams, particularly in mental health, emergency care, and public health. She is affiliated with the Australasian Nursing and Midwifery Clinical Trials Network and the NSW Cardiovascular Research Network, contributing to large-scale research initiatives.

Joanna Dipnallمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Deep Learning
• Biostatistics
• +۷ مورد دیگر

Joanna Dipnall is a Senior Lecturer at Monash University's Monash Biostatistics Unit and a Senior Research Fellow specializing in applied statistics, machine learning, and health data analytics. She holds an Honours in Econometrics from Monash University and a PhD from Deakin University's School of Medicine, where she developed the Risk Index for Depression (RID) using machine learning and traditional statistical techniques. Her research focuses on integrating AI with health data, particularly trauma outcomes, mental health, and registry-based analyses. Affiliations: Monash Biostatistics Unit (School of Public Health and Preventive Medicine), Honourary Postdoctoral Research Fellow at Barwon Health since 2017. Teaching: Co-Coordinator of the Monash Masters of Health Data Analytics course, teaching 'Introduction to Health Data Analytics'. Collaborations: Extensive work with Monash Faculty of IT, NHMRC-funded projects, and global collaborations in trauma outcomes and opioid surveillance. Her research interests span machine/deep learning applications in health, trauma data analytics, and mental health epidemiology. Key projects include the PRAISE study (predicting fracture outcomes via AI) and opioid poisoning surveillance using novel technologies. She has supervised numerous postgraduate students and contributed to over 60 peer-reviewed publications. Grants & Projects: Principal investigator in multiple NHMRC and industry-funded projects, including the $2.5M PRAISE study (2021–2025) and opioid surveillance research (2025–2027). She also co-leads the ARDC SeRP Research Users Community of Practice. Labs/Teams: Active in Monash's Clinical Registries and Biostatistics Unit, collaborating with interdisciplinary teams across health, IT, and economics.

Chong Chun Wieمشاهده پروفایل

دانشیار

• Microbial Ecology
• Gut Microbiome Science
• Digital Health
• +۵ مورد دیگر

Chong Chun Wie is an Associate Professor and Deputy Head of School (Research) at the School of Pharmacy, Monash University Malaysia. He holds a BSc (HONS) in Biochemistry and a PhD in Microbial Ecology. His research focuses on microbial ecology, gut microbiome science, and digital health, addressing global challenges like diabetes prevention, antimicrobial resistance (AMR), and neurodegenerative disorders. He leads the Monash Microbiome Research Centre and co-leads the 1000MYMicrobiome Project. Dr. Chong has held roles including Program Director for the MSc Molecular Medicine program at International Medical University (IMU), securing accreditation for open distance learning, and establishing a double degree program with Queen Mary’s University of London. He is part of the Faculty of Pharmacy and Pharmaceutical Sciences at Monash University Australia, ranked top 5 globally in QS rankings. His interdisciplinary work bridges microbiology, public health, and technology. Research interests include the gut-brain axis in neurological disorders, diet-microbiome-host metabolism interactions, and antibiotic effects on gut microbiota. Recent projects involve studying dietary impacts on gastrointestinal diseases and obesity via systems biology, and applying machine learning to analyze microbial interactions. He has also explored the microbiome of farm animals, preterm neonates, and links between gut health and diseases like Parkinson’s, diabetes, and oral cancer. Chong has secured grants for studies on probiotics, dietary fibers, and gut health interventions. His awards include the ITEX 2022 Gold Medal for contributions to digital health tools. He serves on editorial boards of journals like Therapeutic Advances in Gastroenterology and PLoS ONE . Key labs/teams: Monash Microbiome Research Centre, Future Centre Initiative at Monash University Malaysia. His work supports UN SDGs related to health, sustainable cities, and responsible consumption.

Maryse Lapierre-Landryمشاهده پروفایل

عضو هیئت علمی

• Biomedical Engineering
• Medical Imaging
• Machine Learning
• +۳ مورد دیگر

Maryse Lapierre-Landry, PhD, is a Research Assistant Professor in the Department of Biomedical Engineering at Case Western Reserve University's Case School of Engineering, with an affiliation to the School of Medicine. Her research focuses on advancing image processing techniques, including machine learning, for applications in biomedical optical imaging. Key areas include automated analysis of 3D microscopy and optical coherence tomography (OCT) data for applications such as corneal nerve evaluation, embryonic heart development, and vascular segmentation. Dr. Lapierre-Landry bridges the gap between novel optical instruments and biomedical scientists by developing algorithms for complex datasets. Her work addresses challenges in segmenting blood vessels in embryonic hearts, analyzing corneal nerve patterns, and optimizing tissue segmentation without vascular contrast agents. Publications span topics like deep learning for 3D histology, nuclei detection in microscopy, and 4D OCT imaging of beating hearts. Her research emphasizes practical solutions for biomedical imaging challenges in ophthalmology, cardiology, and developmental biology. Dr. Lapierre-Landry has contributed to over 20 peer-reviewed articles since 2013, with recent work focusing on corneal nerve activity, vagus nerve segmentation via micro-CT, and multiplexed virtual histology. Her interdisciplinary approach integrates engineering, computer science, and biology to advance medical diagnostics and research tools.

• Cardiovascular Nursing
• Cardiac Rehabilitation
• Atrial Fibrillation
• +۷ مورد دیگر

Ian Jones is a Professor of Cardiovascular Nursing at Liverpool John Moores University, affiliated with the School of Nursing and Allied Health. He has been in this role since 2016 and remains an active researcher and academic leader in cardiovascular nursing and rehabilitation. His research interests are centered on improving outcomes for patients with cardiovascular disease, particularly through cardiac rehabilitation, patient self-efficacy, health-related quality of life, and innovative screening methods such as AI-based phenotyping and wearable technologies. He also investigates integrated care models for stroke and cardiorenal metabolic syndrome, as well as the impact of digital health interventions. His recent publications demonstrate a strong trend in mixed-methods research, systematic reviews, and clinical trials focused on patient-centered outcomes, preventive cardiology, and digital innovation in healthcare. He contributes to major scientific statements from organizations such as the European Society of Cardiology. Cardiac Rehabilitation and Quality of Life Atrial Fibrillation Screening and Management Digital Health and Wearable Technologies Integrated and Multidisciplinary Care Nursing Education and Research Training He has co-authored numerous high-impact publications and contributed to book chapters and consensus guidelines. While no specific awards are listed, his involvement in influential scientific statements indicates recognition within the field. He mentors students and early-career researchers, as evidenced by his contributions to PhD theses and research placement development. His work often involves collaboration with multidisciplinary teams across institutions and countries. He is involved in several research initiatives, including studies on supermarket-based AF screening (SHOPS-AF), web-based rehabilitation (ACTIVATE trial), and the Liverpool-Huawei Stroke Study, indicating active leadership in translational cardiovascular research.

Justin Siegelمشاهده پروفایل

استاد

• Protein Engineering
• Enzymology
• Computational Biology
• +۳ مورد دیگر

Justin Siegel is a Professor at the University of California, Davis, affiliated with the Genome and Biomedical Sciences Facility. His research focuses on computational and experimental protein engineering, particularly designing novel enzymes for therapeutic and industrial applications. He pioneered the de novo design of enzymes performing non-natural reactions like the Diels-Alder reaction and collaborated with Foldit gamers to enhance enzyme activity. Recent work includes gluten-degrading enzymes, CO2 fixation pathways, and biofuel production. He holds a PhD from the University of Washington (2011) and a BS from UC Davis (2005). Research Highlights: Developed Rosetta computational tools for macromolecular design Engineered anthrax therapeutics and gluten-digesting enzymes Pioneered noncanonical cofactor-based biocatalytic systems Investigates sustainable protein production and cultured meat economics Awards: Sloan Research Fellowship (2014), UC Davis Faculty Appointment (2012). His work spans 80+ publications and multiple patent families, with ongoing projects in metabolic engineering and drug discovery.

Guy Barrington Marksمشاهده پروفایل

استاد

• Respiratory Diseases
• Epidemiology
• Air Pollution
• +۴ مورد دیگر

Professor Guy Barrington Marks is a Scientia Professor of Respiratory Medicine at the University of New South Wales (UNSW) and Head of Respiratory and Environmental Epidemiology at the Woolcock Institute of Medical Research. He holds concurrent appointments as a Respiratory Physician at Liverpool Hospital, Director of the Australian Centre for Airways Disease Monitoring (ACAM), and Honorary Professor at the University of Sydney. As an NHMRC Senior Principal Research Fellow, he leads major initiatives in lung health research. His research focuses on: Epidemiology of respiratory diseases including tuberculosis, asthma, and COPD Health impacts of air pollution and environmental exposures Capacity-building for lung health research in low-resource settings Innovative diagnostic and management strategies for multidrug-resistant TB Recent publications emphasize global environmental health threats (wildfire PM2.5), TB control innovations (levofloxacin prophylaxis, active case finding), and machine learning applications in public health. His work consistently bridges clinical medicine, epidemiology, and health policy. Awards recognize his exceptional contributions: Research Medal from Thoracic Society of Australia and New Zealand (2010) NHMRC Top Ranked Research Fellow (2013) Fellowship in Australian Academy of Health and Medical Sciences International recognition from Vietnam National TB Program (2017) He mentors global research consortia including the Centre for Air Pollution, Energy and Health Research and leads >$5M in NHMRC grants. Current roles include President of the International Union Against Tuberculosis and Lung Diseases and Editor-in-Chief of the International Journal of Tuberculosis and Lung Disease .

• Retinal Imaging
• Biomedical Image Analysis
• Artificial Intelligence in Medicine
• +۱۲ مورد دیگر

Manuel Trucco is a Professor in the Department of Computing, with a research focus on biomedical image analysis, particularly in retinal imaging and its applications in systemic diseases. His work integrates artificial intelligence, deep learning, and image processing to develop tools for assessing retinal vasculature and detecting biomarkers for conditions such as diabetes, cardiovascular disease, and dementia. His research interests lie at the intersection of computer science and clinical medicine. He specializes in retinal vessel segmentation , tortuosity measurement , fractal dimension analysis , and automated assessment platforms like VAMPIRE. His studies leverage large datasets including the UK Biobank and Lothian Birth Cohort to correlate retinal microvasculature changes with systemic diseases. He applies both handcrafted features and deep learning models to enhance classification accuracy in retinal image analysis. The trends in his recent publications reflect a strong emphasis on AI-driven diagnostics , automated retinal image analysis , and non-invasive biomarker discovery . His work spans technical algorithm development and clinical validation, targeting applications in diabetic retinopathy, coronary artery disease, Alzheimer's, and glaucoma. The integration of multi-resolution modeling, particle swarm optimization, and ultra-wide field imaging demonstrates innovation in both methodology and scalability. VAMPIRE: Vessel Assessment and Measurement Platform for Images of the Retina Research supported by extensive project funding from 2007–2024 Active contributor to large-scale cohort studies (UK Biobank, Lothian Birth Cohort) Supervised 10 research students or early-career researchers h-index of 37 with over 5,800 citations Manuel Trucco leads a research group focused on retinal image analysis and biomedical computing , contributing to the development of software platforms and AI models for clinical use. His team works on both fundamental image processing techniques and their translation into medical practice, particularly in primary and secondary care settings.

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

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

Mi-Ok Kimمشاهده پروفایل

استاد

• Biostatistics
• Causal Inference
• Clinical Trial Design
• +۱۲ مورد دیگر

Dr. Mi-Ok Kim is a Professor in the Department of Epidemiology and Biostatistics at the University of California San Francisco (UCSF) and serves as the Director of the Biostatistics Core at the Helen Diller Family Comprehensive Cancer Center (HDFCCC). She joined UCSF from Cincinnati Children’s Hospital Medical Center, where she led the Biostatistics Unit for the Cancer and Blood Diseases Institute. Her role involves leading strategic development of shared biostatistical resources and providing expert statistical support across basic, clinical, and population sciences research. Dr. Kim earned her MS (2000) and PhD (2003) in Statistics from the University of Illinois at Urbana-Champaign. Her research program centers on methodological advancements in biostatistics, including non- and semi-parametric inference, longitudinal and survival data analysis, and causal inference using structured data from registries, network databases, and electronic health records. She has a strong focus on comparative effectiveness research (CER) and patient-centered outcomes research (PCOR), particularly in the context of hierarchical data structures. Her recent publications reflect a broad interdisciplinary reach, spanning oncology, pediatrics, public health, and machine learning applications in healthcare. Themes include health disparities, clinical trial design, biomarker analysis, and statistical methods for integrating aggregate and individual-level data. She has published in high-impact journals such as JAMA Dermatology , Annals of Applied Statistics , and Pediatrics . Dr. Kim has received multiple honors and awards, including: Fellowship, University of Illinois at Urbana-Champaign (1997) First Prize, Capital One Financial Data Challenge Competition (2001) Norton Prize, Robert Bohrer Workshop for Student Papers in Statistics (2002) Junior Faculty Travel Award, International Conference on Robust Statistics (2004) Travel Award, AACR Cancer Biostatistics Workshop (2008) She has served as Principal Investigator on numerous grants from NIH, NSF, and PCORI, supporting research in areas such as pediatric Crohn’s disease, kidney transplantation, and meta-analysis methods. Her work emphasizes rigorous statistical methodology to reduce bias and improve efficiency in real-world data analysis. She collaborates extensively with multidisciplinary teams across UCSF and beyond, contributing to clinical studies, protocol development, and outcomes research. Dr. Kim leads a research team focused on optimal handling of complex data structures in treatment selection and outcome analysis, with applications in chronic disease management and cancer care. She is also engaged in developing frameworks for post-market monitoring of machine learning-based medical devices, reflecting her forward-looking approach to biostatistical innovation.

• Neurodegenerative Diseases
• Alzheimer's Disease
• Protein Aggregation
• +۵ مورد دیگر

Meenakshisundaram Balasubramaniam serves as an Assistant Professor in the Department of Geriatrics within the College of Medicine at the University of Arkansas for Medical Sciences. His research is centered on neurodegenerative mechanisms with a focus on Alzheimer's disease pathology, protein aggregation dynamics, and autophagy regulation. As a key collaborator in the Shmookler Reis laboratory, he contributes to interdisciplinary investigations spanning computational biology, molecular pharmacology, and translational neuroscience. Dr. Balasubramaniam's research interests encompass: Protein aggregation mechanisms in Alzheimer's and related dementias Computational drug repurposing and target identification Autophagy-lysosomal pathway dysfunction in neurodegeneration Neuroinflammation and cytokine signaling cascades Structural modeling of neurodegenerative disease targets His work integrates advanced computational approaches with experimental validation to identify therapeutic interventions for age-related neurological disorders. Analysis of recent publications reveals strong thematic continuity in targeting protein aggregation pathways across neurodegenerative conditions. His research demonstrates particular innovation in repurposing existing drugs (e.g., ezetimibe, TDZD analogs) to modulate aggregation mechanisms, with consistent application of computational modeling to predict drug-target interactions. The work spans from fundamental molecular mechanisms to preclinical validation in diverse model systems. Scientific recognition includes: Outstanding doctoral student research award from the Engineering and Information Technology program at the University of Arkansas at Little Rock (2016) As Co-Investigator on multiple NIH-funded projects including R01AG084472 and P01AG012411, Dr. Balasubramaniam contributes to major initiatives investigating neuroinflammation, protein aggregates, ApoE4 drug targeting, and autophagy rescue in Alzheimer's pathogenesis. His collaborative approach extends across neuroscience, oncology, and computational biology domains. He operates within the Shmookler Reis laboratory environment, which maintains strong connections between computational modeling groups and experimental neuroscience teams. This integrated approach facilitates rapid translation of in silico predictions to in vitro and in vivo validation, particularly using C. elegans and mammalian cell models for neurodegenerative disease mechanisms.

Hosamadin S. Assadiمشاهده پروفایل

پژوهشگر

• Cardiovascular Magnetic Resonance Imaging
• Artificial Intelligence in Medical Diagnostics
• Heart Failure with Preserved Ejection Fraction (HFpEF)
• +۳ مورد دیگر

Dr. Hosamadin S. Assadi is a postgraduate researcher at the University of East Anglia (Norwich Medical School) and an Honorary Scientist at Norfolk & Norwich University Hospitals NHS Foundation Trust. With a medical background and an MRes in Cardiovascular Medicine from the University of Sheffield, he specializes in AI-driven cardiovascular magnetic resonance (CMR) imaging post-processing techniques. Focus areas: 4D flow CMR, scar/fibrosis quantification, and pressure-volume loop analysis Key collaborations: UEA, University of Sheffield, and NHS Trust External roles: Honorary Scientist (Cardiology) since 2022 His research develops AI-based CMR methodologies to improve diagnosis accuracy for cardiovascular diseases, particularly heart failure with preserved ejection fraction (HFpEF) and aortic stenosis. Publications demonstrate expertise in: Machine learning for cardiac segmentation Non-invasive hemodynamic monitoring Multi-center clinical validation studies Sex-specific prognostic models Scientific recognition includes: FMH Research Prize (2024) MRes Distinction (2020) Media engagement highlights: AI applications in cardiac MRI featured in 44+ outlets (2024) Development of rapid heart scan technology Public education about MRI-based pressure detection systems

Charumathi Sabanayagamمشاهده پروفایل

دانشیار

• Epidemiology
• Chronic Kidney Disease (CKD)
• Age-Related Eye Diseases
• +۴ مورد دیگر

Charumathi Sabanayagam is an Associate Professor at Duke-NUS Medical School and a Principal Investigator at the Singapore Eye Research Institute (SERI). She holds adjunct positions at the NUS Saw Swee Hock School of Public Health and serves in the Ophthalmology & Visual Sciences Academic Clinical Programme (EYE ACP). Her work bridges epidemiology, machine learning, and public health, focusing on chronic kidney disease (CKD) and age-related eye diseases. Education : MBBS (University of Madras, 1980-1985) MD (Microbiology, Tamilnadu Dr. M.G.R. Medical University, 1995-1998) M Med (PH) and PhD (Epidemiology and Biostatistics, National University of Singapore, 2005-2010) Her research explores ethnic differences in CKD and eye disease, machine learning for risk prediction, and global collaborations in meta-analysis. Recent work includes AI tools for CKD detection via retinal imaging and virtual clinical research training courses. She has secured multiple grants, including the NMRC HCSA and Duke-NUS KMRA. Selected scientific awards include the SERI Best Mentor Award (2015), SingHealth Publish Award (2020), SERI Long Service Award (2021), and being named a Stanford Top 2% Scientist (2021-2023). She has published over 300 peer-reviewed articles with an H-index of 75. Notable Grants : NMRC Transition Award (2012-2016) Duke-NUS Khoo Mentored Research Award (2015-2018) NMRC Health Service Research Clinician Scientist Award (2022-2025) Duke-NUS Eye-ACP Education Grant (2023-2024) Her media-covered innovations, including AI screening tools for CKD, have been highlighted in The Straits Times and CNA 938 FM . She actively mentors researchers and leads global meta-analysis projects.