Haohui Lu

Haohui Lu

Research Fellow
Molly Wardaguga Institute, Faculty of Health
Charles Darwin University, Australia

E-mail:

Currently recruiting PhD candidates in AI for Health — please send your CV and transcript to my email.

Haohui Lu is a Research Fellow at the Molly Wardaguga Institute, Charles Darwin University. His research focuses on the development and application of artificial intelligence methods, particularly graph machine learning and large language models, to improve health systems and outcomes. He holds a PhD in Data Science from the University of Sydney, where he specialised in AI for healthcare. His academic background also includes a Master's in Project Management and a Bachelor's in Commerce. Haohui is committed to advancing equitable, data-driven approaches in public health and supporting Indigenous data sovereignty through applied machine learning research.

  • Generative AI: Foundation models and large language models
  • AI in Healthcare: Machine learning applications in health
  • Machine Learning on Graphs: Graph embedding, graph neural networks, knowledge graphs

Education

Honours & Awards

Experience

Research

Academic

Industry

Recent Publications Full list on Google Scholar

2026
  1. H Lu, Z Shao, J Gao, S Uddin, "Bias Mitigation in Large Language Models for Tabular Data Classification", Machine Learning, 115(4), 79, 2026. [link]
  2. S Uddin, H Lu, F Hajati, "Fairness-preserving framework for machine learning: data bias quantification, model evaluation, and robustness across multiple datasets", AI and Ethics, 6(1), 25, 2026. [link]
  3. X Li, L Tao, H Lu, M Dong, J Gao, C Xu, "WATS: Wavelet-Aware Temperature Scaling for Reliable Graph Neural Networks", Proceedings of the Fourteenth International Conference on Learning Representations (ICLR), 2026. [link]
2025
  1. H Lu, Y Lin, R Wu, P Sun, Y Gao, Z Li, "An Explainable Graph Learning Framework for Severe Maternal Morbidity Prediction", Australasian Joint Conference on Artificial Intelligence, 335–346, 2025. [link]
  2. S Uddin, Y Huang, S Fang, H Lu, "Assessing Algorithmic Fairness in Socioeconomic Predictions Using Australian Census Data", Australasian Joint Conference on Artificial Intelligence, 163–176, 2025.
  3. U Naseem, J Rashid, H Lu, D Ng, Z Hussain, A Hussain, "Medical Domain Knowledge Collaborative Graph Learning for Healthcare Event Prediction", Expert Systems, 42(12), e70151, 2025. [link]
  4. H Lu, Thow, A.M., Patay, D. et al. "Identifying the factors influencing the development of bilateral investment treaties with health safeguards: a Machine Learning-based link prediction approach", J Comput Soc Sc, 8, 8, 2025. [link]
  5. H Lu, Y Lin, Z, M L Yiu, Y & S, "Toward fair medical advice: Addressing and mitigating bias in large language model-based healthcare applications", Artificial Intelligence in Medicine, 168:103216, 2025. [link]
  6. Z Shao, H Xi, H Lu, Z, M G. H. Bell & J Gao, "A spatial–temporal Large Language Model with Denoising Diffusion Implicit for predictions in centralized multimodal transport systems", Transportation Research Part C: Emerging Technologies, 179:105249, 2025. [link]
2024
  1. H Lu, U Naseen, "Can Large Language Models Enhance Predictions of Disease Progression? Investigating Through Disease Network Link Prediction", Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 17703–17715, 2024. [link]
  2. H Lu, S Uddin, "A parameterised model for link prediction using node centrality and similarity measure based on graph embedding", Neurocomputing, 127820, 2024. [link]
  3. H Lu, S Uddin, "Unsupervised machine learning for disease prediction: a comparative performance analysis using multiple datasets", Health and Technology, 14(1), 141–154, 2024. [link]
  4. S Uddin, H Lu, A Rahman, J Gao, "A novel approach for assessing fairness in deployed machine learning algorithms", Scientific Reports, 14(1), 17753, 2024. [link]
  5. S Uddin, H Lu, W Alschner, D Patay, N Frank, FS Gomes, AM Thow, "An NLP-based novel approach for assessing national influence in clause dissemination across bilateral investment treaties", PLOS ONE, 19(3), e0298380, 2024. [link]
  6. S Uddin, H Lu, "Confirming the statistically significant superiority of tree-based machine learning algorithms over their counterparts for tabular data", PLOS ONE, 19(4), e0301541, 2024. [link]
  7. S Uddin, S Yan, H Lu, "Machine learning and deep learning in project analytics: methods, applications and research trends", Production Planning & Control, 1–20, 2024. [link]
  8. S Uddin, H Lu, "Dataset meta-level and statistical features affect machine learning performance", Scientific Reports, 14(1), 1670, 2024. [link]
  9. S Uddin, A Khan, H Lu, F Zhou, S Karim, F Hajati, MA Moni, "Road networks and socio-demographic factors to explore COVID-19 infection during its different waves", Scientific Reports, 14(1), 1551, 2024. [link]
2023
  1. H Lu, S Uddin, "KNN-Based Patient Network and Ensemble Machine Learning for Disease Prediction", International Conference on Health Information Science, 2023. [link]
  2. H Lu, S Uddin, "Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends", Healthcare, 11(7), 1031, 2023. [link]
  3. H Lu, S Uddin, "Embedding-based link predictions to explore latent comorbidity of chronic diseases", Health Information Science and Systems, 11(2), 2023. [link]
  4. H Lu, S Uddin, "Explainable Stacking-Based Model for Predicting Hospital Readmission for Diabetic Patients", Information, 13(9), 436, 2023. [link]
  5. S Uddin, A Kan, H Lu, "Impact of COVID-19 on Journal Impact Factor", Journal of Informetrics, 17(4), 101458, 2023. [link]
  6. S Uddin, S Ong, H Lu, P Matous, "Integrating machine learning and network analytics to model project cost, time and quality performance", Production Planning & Control, 11(7), 1031, 2023. [link]
2022
  1. H Lu, S Uddin, "Predictive risk modelling in mental health issues using machine learning on graphs", ACSW 2022: Australasian Computer Science Week, 2022. [link]
  2. H Lu, S Uddin, "A disease network-based recommender system framework for predictive risk modelling of chronic diseases and their comorbidities", Applied Intelligence, 1–11, 2022. [link]
  3. H Lu, S Uddin, F Hajati, MA Moni, M Khushi, "A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus", Applied Intelligence, 1–12, 2022. [link]
  4. S Uddin, S Ong, H Lu, "Machine learning in project analytics: a data-driven framework and case study", Scientific Reports, 12(1), 1–13, 2022. [link]
  5. S Uddin, S Wang, H Lu, A Khan, F Hajati, M Khushi, "Comorbidity and multimorbidity prediction of major chronic diseases using machine learning and network analytics", Expert Systems with Applications, 117761(205), 2022. [link]
  6. S Wang, H Lu, A Khan, F Hajati, M Khushi, S Uddin, "A machine learning software tool for multiclass classification", Software Impacts, 100383(13), 2022. [link]
  7. S Uddin, H Lu, A Khan, S Karim, F Zhou, "Comparing the Impact of Road Networks on COVID-19 Severity between Delta and Omicron Variants: A Study Based on Greater Sydney (Australia) Suburbs", International Journal of Environmental Research and Public Health, 19(11), 9551, 2022. [link]
  8. S Uddin, I Haque, H Lu, M Moni, E Gide, "Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction", Scientific Reports, 1–12, 2022. [link]
  9. S Uddin, S Wang, A Khan, H Lu, "Comorbidity progression patterns of major chronic diseases: The impact of age, gender and time-window", Chronic Illness, 2022. [link]
  10. S Uddin, A Khan, H Lu, F Zhou, S Karim, "Suburban Road Networks to Explore COVID-19 Vulnerability and Severity", International Journal of Environmental Research and Public Health, 19(4), 2039, 2022. [link]
2021
  1. H Lu, S Uddin, "A weighted patient network-based framework for predicting chronic diseases using graph neural networks", Scientific Reports, 1–12, 2021. [link]

Full list of publications on Google Scholar.

Academic Service

Reviewer for Q1 journals including IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports, Artificial Intelligence in Medicine, Computers in Biology and Medicine, and Health Data Science.

Conferences & Presentations