Lijun An

Lijun An

Postdoctoral Fellow, Lund University

I am currently a postdoctoral researcher (supervisor: Jacob Vogel) at Lund University, working with the BioFINDER community. Before moving to Sweden, I completed my Ph.D. at National University of Singapore under the supervision of Thomas Yeo.

My research focuses on neurodegenerative diseases, with an emphasis on developing machine learning models to analyze multiomics data, including imageomics, proteomics, and genomics. My research lines are:

  1. AutoDiag: Improve automated diagnosis using accessible data;
  2. Mechanism: Understand the underlying biology mechanism of neurodegeneration and aging;
  3. ToolDev: Build ready-to-use research/clinical toolkits.

​I have published 10+ peer-reviewed articles in top journals, including Nature, Nature Medicine, Nature Neuroscience, Science Advances, PNAS, and Medical Image Analysis. My research works can be found at .

🔥 News

=========================================================================================
08/2025, Zhang Chen's work about cross-dataset evaluation of dementia progression modeling is out in Human Brain Mapping!
07/2025, I give a talk at AAIC about our AI-proteomics project
07/2025, Leon, Csaba & Shaoshi's work about balancing scanning time and sample size of fMRI experimemts is out in Nature!
03/2025, I got ￡1000 UKB-RAP credits!
02/2025, Winnie's work about protective role of parenthood on brain aging is out in PNAS!



🎓 Education

  • 2019.01 - 2024.01, Ph.D., National University of Singapore, Singapore.
  • 2014.09 - 2018.06, B.Eng., Harbin Institute of Technology, China.

📝 Selected Publications

  • An, L., Zhang, C., Wulan, N., Zhang, S., Chen, P., Ji, F., … & Yeo, B. T. (2025). DeepResBat: deep residual batch harmonization accounting for covariate distribution differences. Medical Image Analysis, 99, 103354. [Free Download]

  • Chen, P., An, L., Wulan, N., Zhang, C., Zhang, S., Ooi, L. Q. R., … & Yeo, B. T. (2024). Multilayer meta-matching: Translating phenotypic prediction models from multiple datasets to small data. Imaging neuroscience, 2, 1-22. [Free Download]

  • Wulan, N., An, L., Zhang, C., Kong, R., Chen, P., Bzdok, D., … & Yeo, B. T. (2024). Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching. Imaging Neuroscience, 2, 1-21. [Free Download]

  • An, L., Chen, J., Chen, P., Zhang, C., He, T., Chen, C., … & Alzheimer’s Disease Neuroimaging Initiative. (2022). Goal-specific brain MRI harmonization. NeuroImage, 263, 119570. [Free Download]

  • He, T., An, L., Chen, P., Chen, J., Feng, J., Bzdok, D., … & Yeo, B. T. (2022). Meta-matching as a simple framework to translate phenotypic predictive models from big to small data. Nature neuroscience, 25(6), 795-804. [PDF]

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