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CDU Research Profile

Haohui LU

alt text  Research Fellow
Molly Wardaguga Institute
Faculty of Health
Charles Darwin University, Australia
E-mail: haohui.lu[at]cdu.edu.au

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

About me

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

Doctor of Philosophy, The University of Sydney, 07.2020 - 12.2023, Conferral date: 18 March 2024

  • Thesis title: Chronic Disease Prediction using Graph Machine Learning

Graduate Certificate in Data Science, The University of Sydney, 11.2019

  • Distinction average

  • Main Courses: Data science, Statistics, Database Management Systems, Algorithms.

Master of Project Management, The University of Sydney, 11.2012

  • Distinction average

  • Main Courses: Project economic and scheduling control.

Bachelor of Commerce, The University of Sydney, 11.2011

  • Main Courses: Operations management, Business Analytics

Honours and Awards

  • Feedback for Teaching Student Survey Award 2024 - The University of Sydney
  • Paulette Isabel Jones Career Award 2023 - The University of Sydney
  • Postgraduate Research Support Scheme 2022 - The University of Sydney

Research experience

  1. Research Fellow, Charles Darwin University, 03.2025 -

    • Conduct research on AI, machine learning, and data science in public health, contributing to publications, grant writing, and project reports.

    • Lead data collection, management, and analysis while collaborating with interdisciplinary teams and mentoring research students.

    • Support Indigenous Data Sovereignty initiatives and work within a culturally safe research framework with First Nations communities.

  2. Research Assistant, The University of Sydney, 07.2024 - 03.2025

    • Managed the end-to-end development and training of AI models, driving operational efficiency and quality through comprehensive analysis of project data and business scenarios.

    • Led a faculty-funded project on child undernutrition, optimizing processes and enhancing collaboration with cross-functional international teams, including policymakers in Indonesia.

    • Integrated Large Language Models (LLMs) into web-based GUIs, improving data interaction and visualization.

  3. Research Assistant, The University of Sydney, 10.2022 - 07.2024

    • Supported multidisciplinary experts in National Health and Medical Research Council (NHMRC) Ideas Grant project

    • Spearheaded data preprocessing and analysis for Bilateral Investment Treaties (BITs) using Natural Language Processing (NLP), enhancing treaty analysis with a custom-built LLM featuring interactive querying capabilities.

    • Developed and maintained dashboards, utilizing advanced data visualization tools to ensure clear communication of complex data insights.

    • A tool is developed by me for this article with demo video.

  4. Doctoral Researcher, The University of Sydney, 07.2020 - 12.2023

    • Conducted research on chronic disease prediction using graph machine learning, achieving improved accuracy in identifying high-risk patients.

    • Managed large datasets using SQL and Python, employing machine learning techniques to derive actionable insights.

    • Created interactive dashboards with Tableau, PowerBI, and Python, facilitating data-driven decision-making.

Academic experience

  1. Casual Academic, The University of Sydney, 07.2020 - Present

    • Taught a variety of subjects at the University of Sydney, including advance machine learning in business, predictive analytics, management science, business analytics, operations management, scheduling, and quantitative methods in project management.

Recent Publications

2025

  1. 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]

  2. 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]

  3. 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, (2024) 17703–17715 [link]

  2. H Lu, S Uddin, "A parameterised model for link prediction using node centrality and similarity measure based on graph embedding", Neurocomputing, (2024) 127820. [link]

  3. H Lu, S Uddin, "Unsupervised machine learning for disease prediction: a comparative performance analysis using multiple datasets", Health and Technology, (2024): 14 (1), 141 - 154. [link]

  4. S Uddin, H Lu, A Rahman, J Gao, "A novel approach for assessing fairness in deployed machine learning algorithms", Scientific Reports, (2024), 14 (1), 17753. [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, (2024): 19(3), e0298380. [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, (2024): 19(4), e0301541. [link]

  7. S Uddin, S Yan, H Lu, "Machine learning and deep learning in project analytics: methods, applications and research trends", Production Planning & Control, (2024): 1-20. [link]

  8. S Uddin, H Lu, "Dataset meta-level and statistical features affect machine learning performance", Scientific Reports, (2024): 14 (1), 1670. [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, (2024): 14 (1), 1551. [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. [link]

  5. S Uddin, A Kan, H Lu, "Impact of COVID-19 on Journal Impact Factor", Journal of Informetrics, (2023): 17(4), 101458. [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, (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, (2022): 1-11. [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, (2022): 1-12. [link]

  4. S Uddin, S Ong, H Lu, "Machine learning in project analytics: a data-driven framework and case study", Scientific Reports 12.1 (2022): 1-13. [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, (2022): 117761 (205). [link]

  6. S Wang, H Lu, A Khan, F Hajati, M Khushi, S Uddin, "A machine learning software tool for multiclass classification", Software Impacts, (2022): 100383 (13). [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, (2022): 19 (11), 9551. [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, (2022): 1-12. [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, (2022): 19 (4), 2039. [link]

2021

  1. H Lu, S Uddin, "A weighted patient network-based framework for predicting chronic diseases using graph neural networks", Scientific Reports, (2021): 1-12. [link]

Full list of publications in Google Scholar.

Conferences and paper presentations

Invited to serve as a session chair @ Sunbelt 2022. Session 93-1. Migration and social networks in the COVID-19 context. Full details here.

My poster is accepted in Digital Health Week 2022.

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 etc.

Professional experience

  1. Peer Support Advisor/Senior Peer Supper Advisor, 06.2021 - 06.2023

    • Assist students with a range of enquiries, ranging from what support services the university offers to what social activities a student can join

  2. Personal Banker, ANZ bank, 06.2016 - 04.2020

    • Provide a full range of professional sales advice to help customers achieve their financial needs and goals. Explain lending products’ fees, interest, and current campaigns to the customer, ensuring that the products meet customers’ needs.

    • Tier 2 Personal Advice, Personal lending, and small business accreditation.

  3. Project Administrator/Business Analyst, Dynamic Payment Pty Ltd, 08.2012 - 07.2016

    • Led initiatives to detect and mitigate fraudulent activities, resulting in enhanced security and business growth during peak periods.

    • Analyzed sales data to develop predictive models and interactive dashboards, significantly improving business transparency and decision-making processes.

    • Provided essential administrative support to technical teams, including scheduling, minute-taking, and training coordination, contributing to project success.


Update on: July 2025.