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 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.

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
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 Scopus8 WoS7
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 Scopus6 WoS2
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 Scopus17 WoS11
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 Scopus8 WoS6
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 Scopus59 WoS41
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), 13 pages. DOI Scopus14 WoS13 Europe PMC9
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), 14 pages. DOI Scopus11 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 Scopus44 WoS38
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
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 Scopus13

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 Proceedings of the 10th ACM Conference on Learning @ Scale (L@S 2023) (pp. 48-58). New York, NY, USA: Association for Computing Machinery (ACM). DOI Scopus8 WoS7
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). US: Association for Computing Machinery. DOI Scopus5

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
2025	Co-Supervisor	Comprehensive Analysis of the Human Immune System in Cancer and Health Using Single-Cell Multi-omics	-	Master	Full Time	Mr Venkatesh Kamaraj
2023	Co-Supervisor	-	Doctor of Philosophy	Doctorate	Full Time	Mr Zhengzheng Wang

Position: Grant-Funded Researcher (B)
Email: chen.zhan@adelaide.edu.au

Dr Chen Zhan

Dr Chen Zhan

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Dr Chen Zhan

Dr Chen Zhan

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Current Higher Degree by Research Supervision (Adelaide University)

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