Shangyang Li
Postdoc researcher
Email:lishangyang@@gdiist.cn
Personal Biography:
Graduated with a bachelor's degree in Physics from Beijing Normal University in 2019 and earned a PhD from the Academy for Advanced Interdisciplinary Studies at Peking University in 2024. Research focuses on computational neuroscience and machine learning, exploring brain information processing principles through computational modeling, understanding mechanisms of neurological diseases, and investigating machine learning algorithms. Emphasizes large-model technology and developing neuromedical large models tailored to the neuro-medicine field. Published several academic papers and serves as a reviewer for neuroscience journals and international machine learning conferences.
Our lab is actively recruiting full-time postdoctoral researchers with a background in machine learning, as well as interns in mathematics, statistics, biomedical engineering, and computer science. Outstanding interns may be offered full-time positions.
Research Fields：
Computational Neuroscience and Machine Learning
Representative Papers:
[1] Subgraph Federated Learning with Information Bottleneck Constrained Generative Learning (Shangyang Li, and Jiayan Guo) (ACM Transactions on Knowledge Discovery from Data, TKDD 2025)
[2] Empowering Cross-Patient Adaptive-Length Epilepsy Diagnosis with ECNorm: A Channel-wise Approach (Kaixuan Wang, Tao Lu and Shangyang Li) (CogSci 2025, corresponding author, CCF-B)
[3] Unified Fusion Network Model for EEG Signals (Chunchang Shao and Shangyang Li) (CogSci 2025, corresponding author, CCF-B)
[4] Harnessing Pre-trained Language Models for EEG-based Epilepsy Detection (Tao Lu, Shangyang Li) (ICME 2025, co-first and corresponding author, CCF-B)
[5] Spindle oscillation emerges at the critical state of the electrically coupled network in thalamic reticular nucleus (Shangyang Li, Chaoming Wang and Si Wu) (Cell Reports 2024)
[6] BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming (Chaoming Wang, Tianqiu Zhang, Xiaoyu Chen, Sichao He, Shangyang Li and Si Wu) (eLife 2024)
[7] BrainPy: a differentiable brain simulator bridging brain simulation and brain-inspired computing (Chaoming Wang, Tianqiu Zhang, Hongyaoxing Gu, Sichao He, Shangyang Li and Si Wu) (ICLR 2024)
[8] An Information Theoretic Perspective for Heterogeneous Subgraph Federated Learning (Jiayan Guo*, Shangyang Li* and Yan Zhang) (DASFAA 2023, co-first and corresponding author, CCF-B)
[9] Graph Adversarial Contrastive Learning (Jiayan Guo*, Shangyang Li*, Yue Zhao and Yan Zhang) (DASFAA 2022, co-first and corresponding author, CCF-B)
