Browse School of Medicine | Stanford Profiles

Imran Thobani

Postdoctoral Scholar, Ophthalmology

Contact Info
Mail Code: 9505
ithobani@stanford.edu

BioAssoc Prof Daniel Ting is a senior consultant vitreo-retinal surgeon working in the Singapore National Eye Center (SNEC), an Associate Professor with Duke-NUS Medical School and an Adjunct Clinical Associate Professor and an Innovation Mentor at Stanford University. He is also the Director of Singapore Health Service (SingHealth) AI Office, SNEC Chief Data and Digital Officer, and the Head of AI and Digital Innovation in Singapore Eye Research Institute (SERI). In 2017, Dr Ting was US-ASEAN Fulbright Scholar visiting the Johns Hopkins University Fulbright Scholar to share his expertise in AI and big data in medicine. In addition to that, his research focus span across not only on the technical aspect on machine learning, deep learning, large language models, explainable AI, privacy preserving technologies, but also safe, responsible and ethical clinical AI applications. He is also involved in several international consensus reporting guidelines such as STARD-AI, QUADAS-AI and DECIDE-AI.

To date, Daniel has published >250 publications on peer reviewed, book chapters, educational articles and conference abstracts. Of those, 45 were published in high impact journals (IF >10) such as JAMA, NEJM, Lancet, Nature Medicine, Nature Biomedical Engineering, Lancet Digital Health, Progress in Retinal and Eye Research, Diabetes Care, Nature Digital Medicine, Ophthalmology and etc. As of Aug 2024 (Google Scholar), his current H index: 61, i-10 index: 172 with total citations of >20,000. Dr Ting has received a total of 100M research grants, in which 20 M as a principal investigator, and 80M as co-investigators on AI and digital innovation related projects in health.

Dr Ting serves in several leadership positions at the different AI and eye societies, including the American Academy of Ophthalmology AI and Retina Ophthalmology Technology Assessment committees, and he also chairs the AI and Digital Innovation Standing Committee for the Asia-Pacific Academy of Ophthalmology and Asia-Pacific Vitreo-Retinal Society. He also serves in numerous advisory and editorial boards in the top-tiered digital and medical journals, including Lancet Digital Health, Frontiers in Medicine, Frontiers in Digital Health and Asia-Pacific Journal of Ophthalmology; Section Editor in British Journal of Ophthalmology and Editorial Board Member in Ophthalmology, Ophthalmology Retina, Ophthalmology Science, British Journal of Ophthalmology, Asia-Pacific Journal of Ophthalmology and Retina.

For the accomplishment, Dr Ting was recognized by many top-tiered international AI and ophthalmology societies in winning many prestigious scientific awards, including Tatler Asia Gen T Award (2021), Singapore National Clinician Scientist Award (2021), Asia-Pacific Academy Ophthalmology (APAO) Nakajima Award (2021), Asia-Pacific Vitreo-Retinal Society (APVRS) Ian Constable Award (2021), MICCAI OMIA Prestigious Achievement Award (2020), ARVO Bert Glaser Award for Innovative Research in Retina (2020), USA Macula Society Evangelos Gragoudas Award (2019), APAO Young Ophthalmologist’s Award (2018) and APTOS Young Innovator Award (2017).

In 2022, 2023 and 2024, he is included in the World’s Top 100 Ophthalmology Power list by the Ophthalmologists; and the World’s Top 2% Scientists by the Stanford University world ranking. In 2021, 2022 and 2023, he was consistently ranked top 3 in the deep learning domain over the past decades (2010 – 2023) by the ExpertScape. In 2024, he won the Singapore National Academy of Medicine Young Scientist Award, and also ranked the Top 100 AI Thought Leaders Worldwide with Fei Fei Li, Yan LeCun, Jensen Huang and many others by H20.ai.

Contact Info
dting45@stanford.edu

Andreas Tolias

Professor of Ophthalmology

BioAndreas Tolias is a faculty member at Stanford University, where he co-leads the Enigma Project. His research lies at the interface of neuroscience and AI, combining large-scale neuroscience experiments with machine learning to uncover the principles of natural intelligence. By focusing on perceptual inference and decision-making, his lab integrates systems and computational neuroscience with AI to decipher the network-level principles of intelligence. Dr. Tolias’s work aims to reverse-engineer these principles to create AI systems that are smarter, more robust, trustworthy, and efficient, while providing a powerful platform to test brain algorithms under complex natural tasks. He earned his B.A. and M.A. in Natural Sciences from the University of Cambridge, a Ph.D. in Systems and Computational Neuroscience from MIT, and completed postdoctoral training in Neuroscience and Machine Learning at the Max Planck Institute for Biological Cybernetics in Tübingen.