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Haohui LU

alt text  Researcher
Casual Academic,
The University of Sydney, Australia
E-mail: haohui.lu[at]sydney.edu.au

About me

Haohui Lu holds a PhD in Data Science from the University of Sydney, where he specialised in chronic disease prediction using machine learning. His academic journey includes a Master's in Project Management and a Bachelor's in Commerce, majoring in Operational Management and Decision Sciences. Haohui Lu has an impressive publication record, with over 20 peer-reviewed papers in the realm of health data analytics. His expertise extends beyond academia, with significant industry experience, including a role as a Data Analyst. As an academic at the University of Sydney, he has demonstrated excellence in teaching a variety of data science and project management courses.

My PhD has been recently conferred! I am now actively seeking postdoctoral or research opportunities in the field of machine learning, particularly interested in Natural Language Processing (NLP), Knowledge Graphs (KGs), and broader applications of machine learning in healthcare.

Research Interests

  • AI in healthcare: Healthcare Natural Language Processing with Knowledge Graphs and Large Language Models

  • Machine Learning on Graphs: Graph embedding, Graph neural network

Education

Doctor of Philosophy, The University of Sydney, 07.2020 - 12.2023

  • 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

Research experience

  1. Research Assistant, The University of Sydney, 10.2022 - Present

    • Multidisciplinary experts in policy, law, data science, public health, and political science collaborate to enhance health safeguards in International Investment Agreements (IIAs) both in Australia and globally. My job responsibilities include preprocessing Bilateral Investment Treaties (BITs) data, util- ising the latest Natural Language Processing (NLP) techniques to analyse BITs similarities, genealogy, and network analysis. I also identify key countries’ roles during specific periods, create user-friendly dashboards, and draft manuscripts for publication.

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

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

    • Chronic disease prediction using graph machine learning: Using Australian insurance data and graph- based machine learning, this research predicts chronic diseases and comorbidities, improving accuracy and identifying high-risk patients. This enables early interventions and reduced healthcare costs.

Academic experience

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

    • Tutoring and marking: PMGT 5873: Project Economics and Procurement, PMGT6867 Quantitative Methods: Project Management, QBUS2310 Management Science, QBUS2810 Statistical Modelling for business, QBUS6820 Business Risk Management/Prescriptive Analytics: From Data To Decision and QBUS6840 Predictive Analytics

    • Course design: PMGT5866 Quantitative Methods in Project Management

Recent publications

  1. S Uddin, A Kan, H Lu, "An NLP-based novel approach for assessing national influence in clause dissemination across bilateral investment treaties", Plos One, (2024): 19(3), e0298380. [link]

  2. 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]

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

  4. 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]

  5. 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]

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

  7. H Lu, S Uddin, "KNN-Based Patient Network and Ensemble Machine Learning for Disease Prediction" International Conference on Health Information Science, 2023. [link]

  8. 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]

  9. 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]

  10. H Lu, S Uddin, "Embedding-based link predictions to explore latent comorbidity of chronic diseases" Health Information Science and Systems 11(2), 2023. [link]

  11. H Lu, S Uddin, "Explainable Stacking-Based Model for Predicting Hospital Readmission for Diabetic Patients" Information 13(9), 436. [link]

  12. 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]

  13. 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]

  14. 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]

  15. 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]

  16. 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]

  17. H Lu, S Uddin, F Hajati, MA Moni, M Khushi, "Predictive risk modelling in mental health issues using machine learning on graphs", ACSW 2022: Australasian Computer Science Week 2022, (2022) [link]

  18. 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]

  19. 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]

  20. 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]

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

  22. 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]

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

  • Advanced Engineering Informatics

  • BMC Health Services Research

  • BMC Medical Informatics and Decision Making

  • BMC Public Health

  • Cardiovascular Diabetology

  • Cluster Computing

  • Complexity

  • Computers in Biology and Medicine

  • Computing

  • Health and Technology

  • Health Data Science

  • iScience

  • Journal of Computational Social Science

  • Journal of Medical Systems

  • Machine Learning with Applications

  • Scientific Reports

  • Signal, Image and Video Processing

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. Business Analyst, Dynamic Payment Pty Ltd, 08.2020 - 07.2016

    • Identifying fraudulent activity and taking the necessary action for escalation and analysing unstructured and structured data to build behaviour models and predictive models.

    • Contributes essential administrative support to coordinate a technical team. Revised business performance metrics in collaboration with IT, which increased transparency on sales key factors.

    • The duties of a project administrator include all aspects of facilitating a project: scheduling meeting times and locations, taking meeting minutes, enhanced business data visualisation for the weekly presentations and arranging training for staff.


Update on: 31/03/2024.