Research Community

Dr. Wilson Goh is the Chief Data Scientist at the Centre of AI in Medicine (C-AIM) at Nanyang Technological University and a Senior Lecturer (Associate Professor) at Imperial College London. Wilson currently leads the Data Science Research Programme and serves as Co-Director of the Centre for Biomedical Informatics (CBI).

Dr. Goh’s research focuses on leveraging complex multimodal clinical datasets to develop and deploy AI solutions within hospital environments, with particular emphasis on mental health and proteomics. His recent work has expanded into implementation science, where he explores data governance issues and investigates how clinicians trust and interact with AI systems. Through his research, Dr. Goh aims to bridge the gap between basic science, AI innovation and integration into healthcare.

Mental Health, AI Development, Implementation, Governance

Prof Guan is a President's Chair in Computer Science and Engineering and Deputy Dean of the College of Computing & Data Science at Nanyang Technological University (NTU), Singapore. He is the Director of the Centre for Brain-Computing Research (CBCR). He has been the Director of the Artificial Intelligence Research Institute of NTU and the Co-Director for S-Lab for Advanced Intelligence from 2020-2025. He initiated and served as the Co-Director of the Rehabilitation Research Institute of Singapore (RRIS), 2015-2018 & 2024-2025.

Prior to joining NTU in 2016, he was with the Institute for Infocomm Research, A*STAR, where he established the Brain-Computer Interface research and founded the Department of Neural & Biomedical Technology.

He published 420 refereed journal and conference papers and holds 26 granted patents and patent applications. He licensed 17 patents/technologies to six companies in Singapore and USA. He delivered more than 100 keynote speeches and invited talks (including the keynote at the opening ceremony of the 7th International BCI Meeting, Asilomar, USA, May 2018, and the plenary talk at IEEE International Conference on Rehabilitation Robotics (ICORR), July 2015). He is a recipient of numerous awards owing to his contributions to Brain-Computer Interface research, which include the international Annual BCI Research Award (First Prize), the King Salman Award for Disability Research, the Nanyang Research Award, the IES Prestigious Engineering Achievement Award, the Achiever of the Year (Research) Award, the Finalist of the President Technology Award.

Brain-Computer Interfaces (BCI), Machine Learning, Neural Signal & Image Processing, and Artificial Intelligence

Assistant Professor Kelvin Li Zhenghao is a Consultant Ophthalmologist and the Clinical Lead for Innovation and Digitalisation at Tan Tock Seng Hospital. He serves as an Assistant Professor at the Lee Kong Chian School of Medicine, Nanyang Technological University. Dr. Li occupies a unique niche at the intersection of clinical medicine and engineering, holding a Master of Technology in Intelligent Systems from NUS-ISS, where he was awarded the Best Project Award and named to the CEO’s Honour List for two consecutive years. His research portfolio focuses on the rigorous benchmarking of Artificial Intelligence and the development of resilient health systems. During his tenure as a Visiting Scholar at the Stanford Byers Eye Institute (2024–2025), he led research into the clinical applicability of Generative AI, publishing on the performance of Vision-Language Models (VLMs) and Large Language Models (LLMs) for ophthalmic diagnostics.

He is the Principal Investigator for multiple grants, developing accessible diagnostic tools such as smartphone-based deep learning systems and virtual reality perimetry. In addition to his work on decentralized care models, he actively publishes on complex clinical cases related to neuro-ophthalmology and general ophthalmology.

His contributions to research and education have been recognized with the Asia-Pacific Academy of Ophthalmology Achievement Award (2019), the Young Investigator Award (Bronze) at the Singapore Healthcare and Biomedical Congress (2019), and the National Healthcare Group Teaching Award for Junior Doctors (2021). He is a Fellow of the Royal College of Ophthalmologists (UK) and the Academy of Medicine, Singapore. A leader in healthcare transformation, he serves as the HEAL Adoption Lead at the Centre for Healthcare Innovation (CHI) and is a member of the Clinician Innovator Committee at Tan Tock Seng Hospital.

Research Interests:

Ophthamology, Implementation

Latest Publication:

Dr Chunyan Miao is a President’s Chair Professor and Chair of the College of Computing & Data Science (CCDS) at Nanyang Technological University, Singapore. Under her leadership, CCDS has contributed greatly to NTU’s top world rankings in CS, CE and AI. CCDS has been ranked as the top university for AI Research Citation Impact by The Nikkei Review. Forbes has listed CCDS’s Data Science and Artificial Intelligence (DSAI) programme in ‘The 10 Best AI and Data Science Undergraduate Courses for 202.

Dr Miao is an expert on Human-Centered AI and is well-known for her impactful AI research in health, ageing, education, and digital industry. She has received over 20 Best Paper and AI awards.

Dr Miao is the Founding Director and Lead PI of the Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), the first AI-empowered interdisciplinary ageing research centre in Singapore.

Human Centred Artificial Intelligence (HAI), Human AI Interaction, AI Ethics, HAI for health, ageing, sustainability and education.

 

Associate Professor Joanne Ngeow is an Associate Professor at Nanyang Technological University's Lee Kong Chian School of Medicine. She is also a Senior Consultant in Division of Medical Oncology at the National Cancer Centre Singapore (NCCS). A/Prof Ngeow currently heads the NCCS Cancer Genetics Service with an academic interest in hereditary cancer syndromes and translational clinical cancer genomics. Her current clinical focus and research revolves around understanding cancer predisposition by studying cancers clustering in families, young adults and in families with multiple / rare cancer presentations.

Human Cancer Genetics and Genomic Medicine, Molecular Epidemiology of Cancer, Population Health and Health Services Research

Professor Sung is a renowned researcher in gastroenterology and hepatology. He led a group of experts from 15 Asia-Pacific countries to launch colorectal cancer screening research in 2004, and has laid down clear guidelines and promoted colorectal screenings in the region. 

Professor Sung’s research interests include intestinal bleeding, Helicobacter pylori, peptic ulcer, hepatitis B, colorectal cancer, and other cancers related to the digestive system. Professor Sung and his team proved the relationship between H. Pylori and peptic ulcer diseases. They were first in demonstrating that a course of antibiotics lasting a week can cure H. Pylori infection and successfully treat peptic ulcers and minimize their relapse. At the same time, he and his research team pioneered the use of endoscopic treatment for ulcer bleeding to reduce the need for operative surgery. 

Professor Sung has published over 1000 full scientific articles in leading medical and scientific journals. He was listed as “Highly Cited Researchers”, released by the Clarivate Analytics, for the years 2018, 2019 and 2020. He has edited and authored around 30 books, as well as many chapters in major textbooks.

Dr Dacheng Tao is currently a Distinguished University Professor in the College of Computing & Data Science at Nanyang Technological University. He mainly applies statistics and mathematics to artificial intelligence and data science, and his research is detailed in one monograph and over 200 publications in prestigious journals and proceedings at leading conferences, with best paper awards, best student paper awards, and test-of-time awards. His publications have been cited over 112K times and he has an h-index 160+ in Google Scholar. He received the 2015 and 2020 Australian Eureka Prize, the 2018 IEEE ICDM Research Contributions Award, and the 2021 IEEE Computer Society McCluskey Technical Achievement Award. He is a Fellow of the Australian Academy of Science, AAAS, ACM and IEEE.

Dr Yeo Si Yong is an Assistant Professor of Digital Health at LKCMedicine, where he advances the development and clinical translation of AI for healthcare. He specialises in AI-based biomedical data analysis, with strong expertise in computer vision and the design of robust AI systems for medical imaging, diagnostics, and decision support. His projects include medical innovation pipeline, deep learning models and multimodal analytics for healthcare.

Dr Yeo contributes deep technical expertise in medical image understanding, predictive modelling, and medical AI, to enable research that turns complex signals into actionable insights. He works closely with clinicians and healthcare domain experts to align technology with patient needs and operational realities, prioritising reliability, fairness, and safety in medical settings. His research interests include medical imaging, explainable AI, risk stratification, and multimodality data analysis. The technology developed have help clinicians in disease detection, diagnosis, patient-specific treatment and medical intervention.

Medical imaging, Explainable AI, Risk Stratification, Multimodality Data Analysis, Health Informatics, Medical Data Interpretation, 

Population Health

EFFDNet: A Scribble-Supervised Medical Image Segmentation Method with Enhanced Foreground Feature Discrimination, Proceedings of Medical Image Computing and Computer Assisted Intervention

Dr. Baosheng Yu is currently an Assistant Professor at the Lee Kong Chian School of Medicine (LKCMedicine), Nanyang Technological University (NTU), Singapore. Before joining LKCMedicine, he was a Research Fellow at the University of Sydney, Australia. He obtained a B.E. degree from the University of Science and Technology of China (USTC) in 2014 and a Ph.D. degree from the University of Sydney (USYD) in 2019. His research focuses on applying cutting-edge AI technologies to medical data, including images, text, and signals, to improve the accuracy and efficiency of diagnosis, treatment, and patient care.

Medical Imaging, Medical Image Analysis, Large Language Models for Medicine