Personal Profile
Dr. Lingfeng Li is an Assistant Professor at the Hetao Institute for Mathematical and Interdisciplinary Sciences (HIMIS), Shenzhen. Before joining HIMIS, he was an Associate Research Scientist/Postdoctoral Fellow at the Hong Kong Centre for Cardiovascular and Cerebrovascular Health Engineering under Prof. Raymond Chan. He received his Ph.D. in Mathematics from Hong Kong Baptist University in 2022, supervised by Prof. Xue-Cheng Tai and Prof. Jiang Yang. He also holds an M.S. in Mathematical Finance from Rutgers University and a B.S. in Applied Mathematics from Sun Yat-sen University.
Research Interests
Theory and applications of machine learning in scientific computing
Variational models and algorithms for image processing problems
Educational Background
Ph.D. / 2018-2022 Hong Kong Baptist University Major: Mathematics
Master's Degree/2016-2018 Rutgers University-New Brunswick Major: Financial Mathematics
Bachelor's Degree / 2012-2016 Sun Yat-sen University Major: Mathematics and Applied Mathematics
Work Experience
2026-present Hetao Institute of Mathematics and Interdisciplinary Studies (Shenzhen) Assistant Professor
2022-2025 Associate Research Scientist, Hong Kong Cardiovascular and Cerebrovascular Health Engineering Research Center
Honors and Awards
2022 Hong Kong Baptist University Yakun Graduate Scholarship
Publication
1. Li, L., Liu, H., Tai, X. C., & Chan, R. H. (2026). New Ways to Construct Graph Neural Networks from Variational Models and Control Approaches. Accepted by Multiscale Modeling & Simulation.
2. Chu, Y. K., Li, L., Kang, S. H., Zhang, J., & Tai, X.-C. (2026). A Unified Variational Framework for Deep Weakly Supervised Image Segmentation. Journal of Mathematical Imaging and Vision, 68(5), 63.
3. Tai, X.-C., Liu, H., Li, L., & Chan, R. H. (2026). A Mathematical Explanation of Transformers. SIAM Journal on Imaging Sciences, 19(3), 1542～1568.
4. Zhang, H., Li, L., Tai, X. C., & Chan, R. H. F. (2025). Parametrized sampling for 3D blood simulation in deformable vessels using Physics-Informed Neural Networks. Journal of Computational and Applied Mathematics, 117197.
5. Zhang, K., Li, L., Liu, H., Yuan, J. & Tai, X. C. (2025). Deep convolutional neural networks meet variational shape compactness priors for image segmentation.Neurocomputing, 129395.
6. Tai, X. C., Liu, H, Chan, R. H. F., & Li, L. (2024). A mathematical explanation of UNet. Mathematical Foundations of Computing.
7. Li, L., Tai, X. C., & Chan, R. H. F. (2024). A new method to compute the blood flow equations using the physics-informed neural operator. Journal of Computational Physics, 113380.
8. Li, L., Tai, X. C., Yang, J., & Zhu, Q. (2024). A priori error estimate of deep mixed residual method for elliptic PDEs. Journal of Scientific Computing, 98(2),44.
9. Li, L., Tai, X. C., & Yang, J. (2022). Generalization error analysis of neural networks with gradient based regularization. Communications in Computational Physics, 32 (4), 1007-1038.
10. Tai, X., Li, L., & Bae, E. (2021). The Potts model with different piecewise constant representations and fast algorithms: a survey. Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision, 1-41.
11. Li, L., Luo, S., Tai, X. C., & Yang, J. (2021). A level set representation method for N-Dimensional convex shape and applications. Communications in Mathematical Research, 37(2), 180.
12. Li, L., Luo, S., Tai, X. C., & Yang, J. (2021). A new variational approach based on level-set function for convex hull problem with outliers. Inverse Problems & Imaging, 15(2), 315.
13. Li, L., Luo, S., Tai, X. C., & Yang, J. (2019). A variational convex hull algorithm. In International Conference on Scale Space and Variational Methods in Computer Vision (pp. 224-235). Springer, Cham.
Research fund
(2026-2028) Hong Kong Research Grants Council Early Career Fellowship LU13300125, HKD 1,071,000, Graph Neural Networks
Mathematical Modeling and Analysis (Co-Investigator)
Patent
1. Li Lingfeng; Tai Xuecheng; Chen Hanfu; Zhang Yuanting (2023) Vascular information prediction method, device, equipment, and storage medium
Quality [CN117257244A]. China National Intellectual Property Administration
2. Chen Hanjie, Lv Liangyi, Li Lingfeng, Zhang Yuanting (2023) Method, device, and equipment for determining blood pressure based on PPG signals
Backup and storage medium [CN117257256A]. China National Intellectual Property Administration
academic report
1. Hong Kong Joint Universities Conference on Structured Matrices and Scientific
Computing, September 25 - September 28, 2025, Hong Kong, China
2. International Conference on Applied Mathematics, Hong Kong, China, May 28 -
June 1, 2024
3. Second Ph.D. Student Seminar in Computational and Applied Mathematics, Beijing, China, September 2 - September 4, 2019.
4. Seminars of Mathematical Theories and Methods in Image Processing and Analysis,
Shenzhen, China, July 19 - July 22, 2019.
5. Seventh International Conference on Scale Space and Variational Methods in Computer Vision, Hofgeismar, Germany, June 30 - July 4, 2019.
Journal review
SIAM Journal on Imaging Science, Journal of Mathematical Imaging and Vision, Inverse Problem & Imaging, Partial Differential Equations and Applications