Personal Profile
Dr. Lingfeng Li currently serves as an assistant professor at the Hetao Institute of Mathematics and Interdisciplinary Sciences (Shenzhen) (HIMIS). Prior to joining HIMIS, he worked as an associate researcher at the Cardiovascular and Cerebrovascular Health Engineering Center in Hong Kong, under the supervision of Prof. Raymond Chan. He earned his PhD in Mathematics from Hong Kong Baptist University in 2022, under the supervision of Prof. Xue-Cheng Tai and Prof. Jiang Yang. Additionally, he holds a Master's degree in Financial Mathematics and a Bachelor's degree in Applied Mathematics from Rutgers University and Sun Yat-sen University, respectively. His research interests primarily focus on the application of machine learning in scientific computing and the theoretical analysis of neural networks. Furthermore, he possesses extensive research experience in the field of variational models in image processing. His research findings have been published in numerous international academic journals, such as the Journal of Computational Physics, the Journal of Scientific Computing, and Communications in Computational Physics.
Research Interests
The application of machine learning in scientific computing
Generalization error analysis of neural networks
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
Published papers
1. 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.
2. 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.
3. Tai, X. C., Liu, H, Chan, R. H. F., & Li, L. (2024). A mathematical explanation
of UNet. Mathematical Foundations of Computing.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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