Chen Zhan

Chen Zhan

South Australian Immunogenomics Cancer Institute

Faculty of Health and Medical Sciences

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

Dr Chen Zhan is a data scientist and AI specialist currently working as a Research Fellow in Bioinformatics at SAiGENCI. He has extensive interdisciplinary research experience with significant research outputs intersecting computer/data science, machine learning, and artificial intelligence across various domains, including economics, environmental science, pharmacy, education, and computational biology.

My current work focuses on integrating multi-omics data to construct cell atlases and applying statistical methods, machine/deep learning, and generative AI to infer atlas data and develop pre-trained models identifying biological and experimental factors influencing cell behaviour.

  • Journals
    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.
    DOI
    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 Scopus5
    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 Scopus2
    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 Scopus13
    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 Scopus6
    2022 Deho, O. B., Zhan, C., Li, J., Liu, J., Liu, L., & Duy Le, T. (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 Scopus44
    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, 13 pages.
    DOI Scopus11 Europe PMC5
    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, 14 pages.
    DOI Scopus11 Europe PMC8
    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 Scopus43 WoS33
    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 Europe PMC6

  • Book Chapters
    Year Citation
    2021 Joksimović, S., Marshall, R., Rakotoarivelo, T., Ladjal, D., Zhan, C., & Pardo, A. (2021). Privacy-Driven Learning Analytics. In E. McKay (Ed.), Manage Your Own Learning Analytics: Implement a Rasch Modelling Approach (Vol. 261, pp. 1-22). Switzerland: Springer International Publishing.
    DOI Scopus12

  • Conference Papers
    Year Citation
    2023 Deho, O. B., Joksimovic, S., Liu, L., Li, J., Zhan, C., & Liu, J. (2023). Assessing the Fairness of Course Success Prediction Models in the Face of (Un)equal Demographic Group Distribution. In L@S 2023 - Proceedings of the 10th ACM Conference on Learning @ Scale (pp. 48-58). Copenhagen: Association for Computing Machinery, Inc.
    DOI Scopus5
    2022 Deho, O. B., Liu, L., Joksimovic, S., Li, J., Zhan, C., & Liu, J. (2022). Assessing the Causal Impact of Online Instruction due to COVID-19 on Students' Grades and its aftermath on Grade Prediction Models. In ACM International Conference Proceeding Series (pp. 32-38). Limassol, Cyprus: Association for Computing Machinery.
    DOI Scopus5
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