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.
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Appointments
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 -
Education
Date Institution name Country Title 2016 - 2020 University of South Australia Australia PhD -
Research Interests
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Journals
Year Citation 2024 Zhan, C., Joksimovic, 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.
Scopus52024 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.
Scopus22023 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.
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.
Scopus122022 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.
Scopus52022 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.
Scopus442020 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.
Scopus10 Europe PMC42020 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.
Scopus11 Europe PMC82018 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.
Scopus42 WoS332018 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.
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 Scopus52022 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
Connect With Me
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