Shanghong Xie

Ph.D Student

Education

2020.09–2024.07    South China Normal University, School of Software, B.E. in Software Engineering

2024.09–Present    Peking University, Academy for Advanced Interdisciplinary Studies, Integrated Life Sciences – Computational Neuroscience, Ph.D. Candidate (Direct Doctoral Program)

2025.06–Present    Tsinghua University, Laboratory for Brain and Intelligence / IDG/McGovern Institute for Brain Research, Computational Neuroscience, Joint Ph.D. Program

Honors & Awards

National Scholarship, Award of Academic Excellence (Peking University), First-Class Comprehensive Scholarship, Outstanding Graduate, etc.

Contact

Email: shanghongyuukixie@stu.pku.edu.cn / xiesh25@mails.tsinghua.edu.cn

Research Interests

My research focuses on the interdisciplinary intersection of artificial intelligence, embodied intelligence, brain-computer interfaces, computational neuroscience, and human-computer interaction, aiming to develop edge-side machines with mental capabilities.

Brain-Computer Interface & Robotics:

Developing lightweight brain-controlled interaction systems to enable EEG-based control of multi-degree-of-freedom UAVs and ground vehicles.

Embodied Intelligence & Neuro-inspired Computing:

Leveraging neuroscience theories to build lightweight brain-inspired neural networks, enhancing robotic locomotion capabilities in indoor environments.

AI for Mental Health & Human-Computer Interaction:

Grounded in psychoanalytic, psychodynamic, and humanistic theories, developing multimodal AI systems for mental health that possess the capability to explore users' mental states.

Grants & Projects

(1) National College Student Innovation and Entrepreneurship Program (Key Supported Field): "Multi-Degree-of-Freedom UAV Control Based on Hybrid Brain-Computer Interface and Visual Obstacle Avoidance" (No. 202210574034, 2022–2024, Excellent Conclusion, Principal Investigator)

(2) National Science and Technology Major Project – Brain Science and Brain-Inspired Research (China Brain Project): "Neural Mechanisms of Affective Interaction and Intention Understanding and Multi-Scale Brain-Inspired Algorithms" (No. 2025ZD0215701, 2025–Present, Participant)

(3) National Natural Science Foundation of China: "Psychological State Assessment and Coping Strategies Based on Multi-Temporal Multi-Dimensional Data and Multimodal Machine Learning" (No. U2336214, 2023–Present, Participant)

(4) Beijing Natural Science Foundation – Shunyi New Energy Intelligent Connected Vehicle Innovation Joint Fund: "Key Technologies for Empathy-Driven Multimodal Cognitive Large Model Anthropomorphic Interaction" (No. L257019, 2025–Present, Participant)

Publications

[1] Wu, Q.*, Xie, S.*, Zeng, Z.*, Huang, Q.*, & Pan, J. (2023, June). A multiple command UAV control system based on a hybrid brain-computer interface. In 2023 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE.

[2] Xie, S., Gao, W., Zeng, Z., Wu, Q., Huang, Q., Ban, N., ... & Pan, J. (2024). Multi-degree-of-freedom unmanned aerial vehicle control combining a hybrid brain-computer interface and visual obstacle avoidance. Engineering Applications of Artificial Intelligence, 133, 108294.

[3] Ban, N., Xie, S., Qu, C., Chen, X., & Pan, J. (2024). Multifunctional robot based on multimodal brain-machine interface. Biomedical Signal Processing and Control, 91, 106063.

[4] Zeng, Z.*, Xie, S.*, Sen, S., & Tan, N. (2025). Review on Embodied Simulators: Advancements and Prospects. Robot, 1–21, July 2025.

[5] Xie, S., Zhang, X., Li, J., ... & Song, S. Automating Psychological Monitoring and Diagnostic Reporting from Longitudinal Conversations with Language Models. ACM SIGKDD Workshop 2026. Accepted. June 2026.

[6] Xie, S., Zhang, X., Li, J., ... & Song, S. From Dialogue to Decision: A Hierarchical Collaborative Framework for Psychological Monitoring and Clinical Decision Support with Lightweight Expert Models and Large Language Models. Under Review.

[7] Zhang, X., Ma, Z., Li, J., Xie, S., ... & Song, S. Deep Learning-Based Anomaly Detection Framework for Early Neurological Deterioration in Ischemic Stroke Patients. Under Review.

[8] Li J., Ma Z., Han X., Xia Z., Zhang X. Xie S., ... & Song S. EEG-JEPA: Structured Latent Prediction for EEG Foundation Models. AAAI 2026.