I am currently working in the Department of Mathematics at Jinan (暨南) University in Guangzhou, China. I got my PhD in applied mathematics at Sun Yat-sen University in 2022. My research focuses on developing advanced non-i.i.d.-oriented machine learning algorithms for heterogeneous biomedical data, including multi-omics and single-cell data, as well as histopathological images.
For students that want to work with me
Publications
- Wang W📧, Zhang X, Xiong Y. Transcriptomic-guided whole-slide image classification for molecular subtype identification. PLOS Computational Biology. 2026, 22(2): e1013950. [code]
- Lai Z-R, Wang W📧. Invariant risk minimization is a total variation model. In: Proceedings of the 41st International Conference on Machine Learning (ICML 2024). Vienna, Austria: 2024: 25913-25935. [code]
- Yang J, Wang W📧, Zhang X. scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision. Bioinformatics, 2024, 40(2): btae091.[code]
- Wang W, Zhang X, Dai D-Q. springD2A: capturing uncertainty in disease-drug association prediction. Bioinformatics, 2022, 38(5):1353–1360.[code]
- Zhang X, Wang W, Ren C-X, Dai D-Q. Learning representation for multiple biological networks via a robust graph regularized integration approach. Briefings in Bioinformatics, 2022, 23(1):bbab409.[code]
- Song W, Wang W, Dai D-Q. Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data. Briefings in Bioinformatics, 2022, 23(1):bbab398.[code]
- Liu Y, Wang W, Ren C-X, Dai D-Q. MetaCon: meta contrastive learning for microsatellite instability detection. In: 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Strasbourg, France: 2021:267–276.
- Wang W, Zhang X, Dai D-Q. DeFusion: a denoised network regularization framework for multi-omics integration. Briefings in Bioinformatics, 2021, 22(5):bbab057.[code]
📧Corresponding Author
Teaching
- Advanced Mathematics I for undergraduates (Autumn 2025, Autumn 2026)
- High-dimensional Data Analysis for MSc (Spring 2023, Spring 2024, Spring 2025, Spring 2026)
- Optimization Methods for MSc (Spring 2024, Sping 2025, Spring 2026)
- Calculus I for undergraduates (Autumn 2022, Autumn 2023, Autumn 2024)
- Probability and Mathematical Statistics for undergraduates (Spring 2023)
Recommended Books
Fundamentals
- Golub G.H. & Van Loan C. F. Matrix Compuations, 4th Edition.The Johns Hopkins University Press: Chapter 1 & Chapter 2; Chapter 7 & Chapter 8 (Optional)
- Wasserman, L. All of Statistics: A Concise Course in Statistical Inference. Springer Science & Business Media.
- Bishop, C. M. Pattern Recognition and Machine Learning. Springer: Chapter 1-4 & Chapter 9, 10, 12, 13, 14.
- 刘浩洋、户将、李勇锋、文再文编著. 最优化计算方法. 高等教育出版社.
Advanced Level
- Nesterov, Y. Lectures on Convex Optimization, 2nd Edition. Springer: Chapter 1-4.
- Beck, A. First-Order Methods in Optimization. SIAM.
- Vershynin, R. High-Dimensional Probability: An Introduction with Applications in Data Science, 2nd Edition. Cambridge University Press.
- Mohri, M., Rostamizadeh, A., & Talwalkar, A. Foundations of Machine Learning, 2nd Edition. MIT Press.
Useless Now but Interesting
- Tao, T. Topics in Random Matrix Theory. American Mathematical Society.
- Alon, N. & Spencer, J. H. The Probabilistic Method, 4th Edition. Wiley.
- Foucart, S. & Rauhut, R. A Mathematical Introduction to Compressive Sensing. Springer.
In an age of increasingly powerful LLMs, when knowledge is readily accessible, I sometimes wonder whether people still read books. Yet I keep this list as a reminder to myself that the intellectual achievements of those who came before us are still worthy of admiration and appreciation.