Chen Zhan

Research Interests

Bioinformatics and computational biology Machine learning Artificial Intelligence Statistical data science

Dr Chen Zhan

Grant-Funded Researcher (B)

SAIGENCI

College of Health

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.

Dr Chen Zhan is a computational biomedical researcher working at the intersection of data science, artificial intelligence and health research. His expertise spans bioinformatics, genomics, statistical modelling, machine learning and the analysis of complex biomedical and real-world health data. His work focuses on developing rigorous and interpretable computational approaches that support biological discovery, clinical research and evidence-based healthcare.
Chen holds a PhD in Computer and Information Science and a Master’s degree in Software Engineering. His doctoral research developed computational methods for detecting adverse drug event signals from prescription and pharmacovigilance data, establishing a foundation in pharmacoepidemiology, medication safety and large-scale health data analysis. He has since applied his interdisciplinary expertise across computational biology, responsible AI and data-intensive biomedical research.

Chen’s current research focuses on the construction, integration and analysis of human cell atlases using single-cell and multi-omics data. He develops statistical, machine-learning and generative modelling approaches to characterise cellular diversity, identify biological and technical sources of variation, and enable robust comparisons across tissues, datasets and populations.

A broader aim of his research is to make large biomedical reference datasets more reusable for hypothesis testing, disease research and precision medicine. His work includes computational modelling of cell composition and gene expression, atlas integration and benchmarking, cancer and immune-system research, and the development of scalable and interpretable methods for complex biological data.

Building on his earlier work in pharmacoepidemiology and medication safety, Chen is also interested in connecting molecular, cellular and population-level evidence. This research direction supports collaboration across biomedical science, genomics, pharmacy, public health, clinical research and artificial intelligence.

Date Position Institution name
2024 - ongoing Research fellow University of Adelaide
2023 - 2024 Bioinformatican University of Melbourne
2020 - 2023 Research Associate University of South Australia

Date Institution name Country Title
2016 - 2020 University of South Australia Australia PhD

Year Citation
2025 Mangiola, S., Brown, R., Zhan, C., Berthelet, J., Guleria, S., Liyanage, C., . . . Pal, B. (2025). Circulating immune cells exhibit distinct traits linked to metastatic burden in breast cancer. Breast Cancer Research, 27(1), 73-1-73-17.
DOI Scopus5 Europe PMC1
2024 Zhan, C., Joksimovi'c, S., Ladjal, D., Rakotoarivelo, T., Marshall, R., & Pardo, A. (2024). Preserving Both Privacy and Utility in Learning Analytics. IEEE Transactions on Learning Technologies, 17, 1655-1667.
DOI Scopus13 WoS8
2024 Deho, O. B., Liu, L., Li, J., Liu, J., Zhan, C., & Joksimovic, S. (2024). When the Past != The Future: Assessing the Impact of Dataset Drift on the Fairness of Learning Analytics Models. IEEE Transactions on Learning Technologies, 17, 1007-1020.
DOI Scopus9 WoS4
2023 Zhan, C., Blessed Deho, O., Zhang, X., Joksimović, S., & de Laat, M. (2023). Synthetic data generator for student data serving learning analytics: A comparative study. Journal of Learning Letters.
DOI
2023 Deho, O. B., Joksimovic, S., Li, J., Zhan, C., Liu, J., & Liu, L. (2023). Should Learning Analytics Models Include Sensitive Attributes? Explaining the Why. IEEE Transactions on Learning Technologies, 16(4), 560-572.
DOI Scopus22 WoS17
2022 Ladjal, D., Joksimović, S., Rakotoarivelo, T., & Zhan, C. (2022). Technological frameworks on ethical and trustworthy learning analytics. British Journal of Educational Technology, 53(4), 733-736.
DOI Scopus9 WoS7
2022 Deho, O. B., Zhan, C., Li, J., Liu, J., Liu, L., & Le, T. D. (2022). How do the existing fairness metrics and unfairness mitigation algorithms contribute to ethical learning analytics?. British Journal of Educational Technology, 53(4), 822-843.
DOI Scopus70 WoS50
2020 Zhan, C., Roughead, E., Liu, L., Pratt, N., & Li, J. (2020). Detecting high-quality signals of adverse drug-drug interactions from spontaneous reporting data. Journal of Biomedical Informatics, 112(article no. 103603), 103603-1-103603-13.
DOI Scopus15 WoS13 Europe PMC10
2020 Zhan, C., Roughead, E., Liu, L., Pratt, N., & Li, J. (2020). Detecting potential signals of adverse drug events from prescription data. Artificial Intelligence in Medicine, 104(101839), 101839-1-101839-14.
DOI Scopus12 WoS9 Europe PMC9
2018 Reynolds, C., Agrawal, M., Lee, I., Zhan, C., Li, J., Taylor, P., . . . Roos, G. (2018). A sub-national economic complexity analysis of Australia's states and territories. Regional studies, 52(5), 715-726.
DOI Scopus48 WoS42
2018 Zhan, C., Roughead, E., Liu, L., Pratt, N., & Li, J. (2018). A data-driven method to detect adverse drug events from prescription data. Journal of Biomedical Informatics, 85, 10-20.
DOI Scopus9 WoS8 Europe PMC6

Year Citation
2026 Mengyuan, S., Yingnan, G., Ning, L., Dharmesh, B., Michael, M., Juan, H., . . . Stefano, M. (2026). cellNexus: Quality control, annotation, aggregation and analytical layers for the Human Cell Atlas data.
DOI
2026 Berrocal-Rubio, M. A., Gleeson, J., Kato, M., Delobel, D., Kore, H., Beckhouse, A. G., . . . Wells, C. A. (2026). Alternative promoters used during myeloid differentiation and upon activation change the gene products available for innate immune programs.
DOI

Date Role Research Topic Program Degree Type Student Load Student Name
2025 Co-Supervisor Comprehensive Analysis of the Human Immune System in Cancer and Health Using Single-Cell Multi-omics Master of Philosophy (Medical Science) Master Full Time Mr Venkatesh Kamaraj
2023 Co-Supervisor Trust Dynamics and Role Emergence in Complex Problem-Solving: A Study of Human-AI Collaborative Learning Environments in Secondary Education  Doctor of Philosophy Doctorate Part Time Mr Zhengzheng Wang

Date Role Research Topic Program Degree Type Student Load Student Name
2021 - 2023 Co-Supervisor Investigating the fairness of predictive learning analytics models in static and dynamic environments Doctor of Philosophy Doctorate Full Time Mr Oscar Deho

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