Mornin FENG-mid-shot

Domian Lead, Biostatistics, Modeling, AI and Data Analytics (B.MAD) Domain Director,
AI for Public Health (AI4PH) Program
Associate Professor

Tel (65) 6516 4984

FENG Mengling ‘Mornin’

Mengling “Mornin” Feng is an Associate Professor at the National University of Singapore (NUS), where he directs the AI for Public Health (AI4PH) Program and leads the Biostatistics, Modelling, AI, and Data Analytics (B.MAD) domain. Specializing in healthcare AI, Dr. Feng’s research centers on innovative solutions in medical imaging analysis, treatment recommendation systems, and clinical text processing. He actively participates in international collaborations and serves as Chair of the Singapore Chapter of the Observational Health Data Sciences and Informatics (OHDSI) consortium, promoting standardized healthcare data practices.

Dr. Feng earned his Ph.D. from Nanyang Technological University and completed postdoctoral training at Harvard-MIT. His significant contributions have earned him accolades, including the Excellent Youth Award at the World Health Forum, the Trailblazer Award by Singapore’s Ministry of Digital Development and the NUS Innovation Venture Creation (IVC) – Provost’s Innovation Chair Professor Award.

Since joining NUS in 2017, Dr. Feng has secured over SGD 10 million in research and translation funding as Lead Principal Investigator, Theme Principal Investigator, and Co-Principal Investigator. He has published nearly 200 papers in top-tier conferences and journals, attracting over 15,000 citations. Dr Feng is among the top 2% most cited scientists in 2025. Additionally, Dr. Feng has been the lead organizer of the annual Singapore Healthcare AI Datathon for the past seven years, drawing thousands of interdisciplinary researchers from the region.

Affiliation

  • NUS Saw Swee Hock School of Public Health
  • NUS Yong Loo Ling School of Medicine
  • NUS Institute of Data Science
  • NUS Biomedical Engineering Department, CDE

Research Areas

  • Agentic AI for health
  • Medical foundation model
  • Generative AI models for medical and health time-series understanding
  • Reinforcement learning for treatment recommendation and reasoning
  • Large-model-as-a-judge for large scale AI model evaluation

Teaching Areas

  • AI for health
  • Health data analytics
  • Evidence discoveries with real-world data

Academic/Professional Qualifications

  • Senior Post-doc (2014), Harvard-MIT Health Science Technology Division, MIT, US
  • PhD (2009), Nanyang Technological University, Singapore
  • Bachelor (2003), Nanyang Technological University, Singapore

Awards/Honours

  • NUS Innovation Venture Award, Provost Chair Professor Award – 2025
  • 4th World Health Forum, Excellent Youth Award – 2024
  • One Asia Start-up Award, Digital/AI Category (my spinoff FahtomX) – 2024
  • Trailblaser Award by Ministry of Digital Development and Information (my spinoff FahtomX) – 2024
  • Global Breast Cancer Conference, Good Poster Presentation Award- 2023
  • Graduate Student Research Award (my PhD student) – 2023
  • Corporate Slingshot Challenge Winner (my spinoff FathomX) – 2022
  • CHISEL Healthcare Innomatch – 2022
  • Slingshot Competition JTC Award (my spinoff FahtomX) – 2021
  • Editor of the year, Health Data Science (A Science Partner Journal) – 2021
  • Singapore Data Science Consortium Dissertation Research Fellowship (my PhD student) – 2021
  • Businesswire Medtech Innovator Award – 2020
  • Second runner-up Physionet Challenge – 2020
  • Finalist for MedTech Innovator Asia Pacific – 2020
  • Graduate Student Research Award (my PhD student) – 2020
  • Gold and Silver medals for Kaggle Medical Imaging AI competitions – 2019
  • 1st Runner-up ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Competition, 2018
  • Top 5 (Team “Eagle Eye”) in The Digital Mammography DREAM Challenge – 2017

Career History

  • Senior Post-doc, MIT, US (2012-2015)
  • Head of Biomedical and Healthcare Analytics Lab, Institute for Infocomm Research, Singapore (2014~2017)
  • Affiliate Scientist, Harvard-MIT Health Science Technology Division, MIT (2014~present)
  • Scientist, Institute for Infocomm Research, Singapore (2009~2012)

Administrative Leadership

  • Domian Lead, Biostatistics, Modeling, AI and Data Analytics (B.MAD) Domain
  • Director, AI for Public Health (AI4PH) Program
  • Associate Director of Research, Institute of Data Science (IDS)
  • Senior Assistant Director, AIO, NUHS

Professional/Consulting Activities

  • Area Chair of Conferences such as AAAI & KDD
  • Reviewer of international journals such as Nature Communication, Nature Machine Intelligence, Nature Scientific Data, NPJ Digital Medicine, LANCET eBioMedicine, LANCET Digital Health, Radiology: Artificial Intelligence, Journal of Medical Internet Research,
  • International Senior Editor such as LANCET Digital Health Health Data Science
  • Chapter Chair, OHDSI, Singapore
  • Scientific Mentor, Korea Clinical Datathon
  • Organizing Chair, Singapore Healthcare AI and EXPO
  • Judge, ANZICS Critical Care Datathon 2018
  • Scientific mentor, Beijing PLA-MIT Datathon 2018
  • Co-organizor, Tokyo Healthcare Analytics Symposium and Datathon 2018
  • Organizing Chair, NUS-MIT Healthcare Analytics Symposium and Datathon 2017
  • International Scientific Advisory Committee, 16th International Symposium on Intracranial Pressure and Neuromonitoring 2016

Major Publications

  • Liu, Yihao, Xu Cao, Tingting Chen, Yankai Jiang, Junjie You, Minghua Wu, Xiaosong Wang, Mengling Feng, Yaochu Jin, and Jintai Chen. “From screens to scenes: A survey of embodied AI in healthcare.” Information Fusion 119 (2025): 103033.
  • He, Kai, Jiaxing Xu, Qika Lin, Wenqing Wang, Zeyu Gao, Jialun Wu, Yucheng Huang, and Mengling Feng. “External Retrievals or Internal Priors? From RAG to Epitome-Augmented Generation by Fuzzy Selection.” IEEE Transactions on Fuzzy Systems (2025).
  • Gao, Zhan, Qika Lin, Huaxuan Wen, Bin Pu, Mengling Feng (corresponding author), and Kenli Li. “Incorporating Large Vision Model Distillation and Fuzzy Perception for Improving Disease Diagnosis.” IEEE Transactions on Fuzzy Systems (2025).
  • Lin, Qika, Kai He, Yifan Zhu, Fangzhi Xu, Erik Cambria, and Mengling Feng. “Cross-modal Knowledge Diffusion-based Generation for Difference-aware Medical VQA.” IEEE Transactions on Image Processing (2025).
  • Mengling Feng, et al. “A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics.” Information Fusion (2025): 102963.
  • Perera, Dilruk, Siqi Liu, Kay Choong See, and Mengling Feng. “Smart Imitator: Learning from Imperfect Clinical Decisions.” Journal of the American Medical Informatics Association (2025): ocae320.
  • Lin, Qika, Yifan Zhu, Xin Mei, Ling Huang, Jingying Ma, Kai He, Zhen Peng, Erik Cambria, and Mengling Feng. “Has multimodal learning delivered universal intelligence in healthcare? A comprehensive survey.” Information Fusion (2024): 102795.
  • Mengling Feng, et al. “Reinforcement Learning to Optimize Ventilator Settings for Patients on Invasive Mechanical Ventilation: Retrospective Study.” Journal of Medical Internet Research 26 (2024): e44494.
  • Huang, Ling, Su Ruan, Yucheng Xing, and Mengling Feng. “A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods.” Medical Image Analysis (2024): 103223.
  • Yang, Hongfei, Jiangeng Chang, Wenbo He, Caitlin Fern Wee, John Soong Tshon Yit, and Mengling Feng. “Frailty Modeling using Machine Learning Methodologies: A Systematic Review with Discussions on Outstanding Questions.” IEEE Journal of Biomedical and Health Informatics (2024).
  • Mengling Feng et al “GEM: Empowering MLLM for Grounded ECG Understanding with Time-series and Images”, NeurIPS 2025
  • Kai He, Yucheng Huang, Wenqing Wang, Delong Ran, Dongming Sheng, Junxuan Huang, Qika Lin, Jiaxing Xu, Wenqiang Liu, Mengling Feng “Crab: A Novel Configurable Role-Playing LLM with Assessing Benchmark” ACL 2025.
  • Lin, Qika, Tianzhe Zhao, Kai He, Zhen Peng, Fangzhi Xu, Ling Huang, Jingying Ma, and Mengling Feng. “Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models.” ACL 2025.
  • Ma, Jingying, Qika Lin, Ziyu Jia, and Mengling Feng. “ST-USleepNet: A spatial-temporal coupling prominence network for multi-channel sleep staging.” IJCAI 2025
  • Zhang, Jihai, Xiang Lan, Xiaoye Qu, Yu Cheng, Mengling Feng, and Bryan Hooi. “Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning.” In European Conference on Computer Vision (ECCV), pp. 35-52. Springer, Cham, 2025.
  • Mengling Feng, et al. “Avoiding Feature Suppression in Contrastive Learning: Learning What Has Not Been Learned Before.” ECCV 2024.

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