Jifan Shi (史际帆）

Doctoral supervisor for Complex Systems Science and Engineering.

Stochastic modeling, algorithms and their applications; Mathematical theory in biology and life sciences.

• Complex systems: modeling and network inference, constructing undirected/directed networks, quantifying dynamical causality, inferring gene regulations etc.

• Critical phenomena analysis: critical transition, tipping point, early-warning signal etc.

• Biological data modeling and analysis: scRNA-seq, multi-omics data, EEG/fMRI data etc.

1. 《动力学刻画的数据科学理论和方法》, 陈洛南、刘锐、马欢飞、史际帆. 科学出版社, 北京, 2024. ISBN 978-7-5088-6449-5. [专著]

Dynamics Characterized Data Science: Theory and Methods. Luonan Chen, Rui Liu, Huanfei Ma, and Jifan Shi.  Science Press, Beijing, Oct 2024, ISBN 978-7-5088-6449-5. 

2. 《基于理工信的医学数据采集与分析》, 沈百荣、杨恩策主编. 编写组成员. 北京大学医学出版社, 北京, 2024. ISBN 978-7-5659-3198-7. [“101计划” 核心教材]

Medical Data Collection and Analysis Based on Science, Engineering and Information Technology. Bairong Shen, and Ence Yang. Editorial board member. Peking University Medical Press, Beijing, Jul 2024, ISBN 978-7-5659-3198-7.

1. Integrative cross-sample alignment and spatially differential gene analysis for spatial transcriptomics. Yecheng Tan, Zezhou Wang, Ai Wang, Yan Yan, Wei Lin*, Qing Nie*, and Jifan Shi*. BioRxiv, https://doi.org/10.1101/2025.06.05.653933. In submission.

2. Dynamical causality under invisible confounders for biological network reconstruction. Jinling Yan#, Shao-Wu Zhang*, Chihao Zhang#, Weitian Huang, Jifan Shi, and Luonan Chen*. ArXiv, https://arxiv.org/abs/2408.05584. In submission.

3. Deciphering interventional dynamical causality from non-intervention complex systems. Jifan Shi*, Yang Li, Juan Zhao, Siyang Leng, Rui Bao, Kazuyuki Aihara*, Luonan Chen*, and Wei Lin*. ArXiv, https://arxiv.org/abs/2407.01621v2. In submission.

4. Kolmogorov-Arnold autoencoder for conditional mutual information estimation. Yiqiao Chen, Jifan Shi*, Siyang Leng. Journal of Fudan University (Natural Science), 64(4), 2025. (In Chinese, selected as the Cover Article).

5. Machine learning prediction of short cervix in mid-pregnancy based on multimodal data from the first-trimester screening period: An observational study in a high-risk population. Shengyu Wu#, Jiaqi Dong#, Jifan Shi, Xiaoxian Qu, Yirong Bao, Xiaoyuan Mao, Mu Lv, Xuan Chen, and Hao Ying*. Biomedicines, 13(9), 2025.

6. Utilizing causal network markers to identify tipping points ahead of critical transition. Shirui Bian#, Zezhou Wang#, Siyang Leng, Wei Lin, Jifan Shi*. Accepted by Advanced Science, 2025.

7. Reservoir cross mapping as a nonlinear framework for detecting dynamical causality. Ren Cao, Jintong Zhao, Chun Guan, Huanfei Ma*, Jifan Shi*, and Siyang Leng*. Cell Reports Physical Science, 6(7): 102683, 2025.

8. diffMIN: Reconstructing sample-specific differential gene regulatory networks based on mutual information. Songwei Ai, Yiwei Zhou, Wenkui Zou, Emeli Chatterjee, Guoping Li, Jifan Shi*, Junjie Xiao*, and Juan Zhao*.  IEEE Transactions on Computational Biology and Bioinformatics, https://doi.org/10.1109/TCBBIO.2025.3576169, 2025.

9. Transfer learning of multicellular organization via single-cell and spatial transcriptomics. Yecheng Tan#, Ai Wang#, Zezhou Wang, Wei Lin*, Yan Yan*, Qing Nie*, Jifan Shi*. PLoS Computational Biology 21(4): e1012991, 2025.

10. A deep learning approach of artificial neural network with attention mechanism to predicting marine biogeochemistry data. Mingzhi Liu, Yipeng Wang, Guoqiang Zhong, Yongxin Liu, Xiaoqing Liu, Jifan Shi, Yangli Che, and Rui Bao*. Journal of Geophysical Research-Biogeosciences, 130(3): e2024JG008386, 2025.

11. Unveiling the mechanism of compound Ku-Shen injection in liver cancer treatment through ingredient-target network analysis. Wenkui Zou, Jiazhen Liu, Zexin Wei, Chunhua Peng, Ying Zhao, Yue Ding, Jifan Shi*, and Juan Zhao*. Genes 15(10): 1278, 2024.

12. Detecting dynamical causality via intervened reservoir computing. Jintong Zhao, Zhongxue Gan*, Ruixi Huang, Chun Guan, Jifan Shi*, and Siyang Leng*. Communications Physics 7: 232, 2024.

13. Detecting dynamical causality by intersection cardinal concavity. Peng Tao#, Qifan Wang#, Jifan Shi#, Xiaohu Hao, Xiaoping Liu, Bin Min, Yiheng Zhang, Chenyang Li, He Cui*, and Luonan Chen*. Fundamental Research, https://doi.org/10.1016/j.fmre.2023.01.007, 2023.

14. Energy landscape decomposition for cell differentiation with proliferation effect. Jifan Shi, Kazuyuki Aihara*, Tiejun Li*, and Luonan Chen*. National Science Review 9: nwac116, DOI: 10.1093/nsr/nwac116, 2022. (Preprint, SI, IICSnews, NSRnews).

15. Mean-field analysis of Stuart-Landau oscillator networks with symmetric coupling and dynamical noise. Yang Li*, Jifan Shi, and Kazuyuki Aihara. Chaos 32: 063114, 2022. (Preprint)

16. Embedding entropy: a nonlinear measure of dynamical causality. Jifan Shi*, Luonan Chen*, and Kazuyuki Aihara*. Journal of the Royal Society Interface. 19: 20210766, 2022. (Preprint, SI, code)

17. Criticality in the healthy brain. Jifan Shi*, Kenji Kirihara, Mariko Tada, Mao Fujioka, Kaori Usui, Daisuke Koshiyama, Tsuyoshi Araki, Luonan Chen, Kiyoto Kasai*, and Kazuyuki Aihara*. Frontiers in Network Physiology, section Networks of Dynamical Systems. 1: 755685, 2022. (SI)

18. Dynamics-based data science in biology. Jifan Shi, Kazuyuki Aihara, and Luonan Chen*. Natl Sci Rev. 8(5): nwab029, 2021. (a brief news report).

19. On the Mathematics of RNA Velocity I: Theoretical Analysis. Tiejun Li*, Jifan Shi*, Yichong Wu*, and Peijie Zhou*. CSIAM Trans. Appl. Math. 2(1): 1-55, 2021. (pdf)

20. Quantifying pluripotency landscape of cell differentiation from scRNA-seq data by continuous birth-death process. Jifan Shi, Tiejun Li*, Luonan Chen*, and Kazuyuki Aihara*. PLoS Comput. Biol. 15(11): e1007488, 2019. (SI, code) 

21. Quantifying Waddington’s epigenetic landscape: a comparison of single-cell potency measures. Jifan Shi, Andrew E Teschendorff*, Weiyan Chen, Luonan Chen*, and Tiejun Li*. Brief. in Bioinformatics. 21(1): 248-261, 2020. (SI, code)

22. 关于临界现象和强相关网络推断的多尺度建模与分析 (Multiscale modeling and analysis of critical transitions and network inference with strong connections). Jifan Shi. 北京大学博士学位论文(PhD thesis, in Chinese), 2018. (pdf)

23. Quantifying direct dependencies in biological networks by multiscale association analysis. Jifan Shi, Juan Zhao, Xiaoping Liu, Luonan Chen*, and Tiejun Li*. IEEE/ACM Trans. Comp. Biol. Bioinfo. 17(2): 449-458, 2020. (pdf, SI, code).

24. Detecting direct associations in a network by information theoretic approaches. Jifan Shi#, Juan Zhao#, Tiejun Li*, and Luonan Chen*. Sci. China Math. 62(5): 823-838, 2019.

25. Towards a critical transition theory under different temporal scales and noise strengths. Jifan Shi, Tiejun Li*, and Luonan Chen*. Phys. Rev. E 93(3): 032137, 2016.

1. 一种基于互信息的单样本调控网络构建方法. 史际帆, 赵娟, 艾凇玮, 邹文葵, 柳家振, 孙毅, 杨伟, 林伟. 国家知识产权局. 申请号202411593970.7.

• National Natural Science Foundation of China (NSFC), No. 42450192. 2025/01 --- 2027/12. PI. Ongoing.

• Shanghai Data Bureau. 2024/06 --- 2026/05. Participants. Ongoing.

• AI for Science Foundation of Fudan University, FudanX24AI041. 2024/07 --- 2025/12. PI. Ongoing.

• Fudan AI4S Foundation for Brain Science. 2023/11 --- 2024/10. Participants. Finished.

• National Natural Science Foundation of China (NSFC), No. 12301620. 2024/01 --- 2026/12. PI. Ongoing.

• National Key R&D, 2022YFC2704600/2022YFC2704604. Ministry of science and technology (MOST). 2022/12 --- 2025/11. Sub-project Core Member. Ongoing.

• Comprehensive Mathematical Understanding of the Complex Control System between Organs and Challenge for Ultra-Early Precision Medicine. Japan Science and Technology Agency (JST). Moonshot Research & Development Program, JPMJMS2021. 2020/12 --- 2025/11, direct costs 1,530,100,000 yen. Participants. Finished.

• Research and Development on Next-generation AI and its Key Technology Based on Nonlinear Dynamics. Japan Agency for Medical Research and Development (AMED). Strategic Research Program for Brain Sciences, JP21dm0307009. 2018/06 --- 2024/03, direct costs 94,500,000 yen. Participants. Finished.

• Establishing Theoretical Foundations for Mathematical Modeling of Pathological Biosystems and its Applications to Personalized Medicine. Japan Society for the Promotion of Science (JSPS). Grant-in-Aid for Scientific Research (S), JP15H05707. 2015/05 --- 2020/03, direct costs 148,000,000 yen. Participants. Finished.

• Chinese Mathematical Society (CMS)

• China Society for Industrial and Applied Mathematics (CSIAM)

• Operations Research Society of China (ORSC)

• Shanghai Society of Nonlinear Sciences

• Shanghai Society for Bioinformatics

1.  Academy Editor for PLOS Computational Biology 

2.  Editorial Board Member for Emerging Science and Technology

3.  Reviewer for over ten journals