Academic Staff
- HHB719, Hung Hom Bay Campus
- +852 2766 6981
- shaonan.wang@polyu.edu.hk
- Personal Website
ORCID ID
SCOPUS IDBiography
My research operates at the intersection of artificial intelligence and neuroscience, pursuing two complementary directions: AI for neuroscience and neuroscience for AI.
In the first stream, I investigate the neural mechanisms underlying language representation and composition in both human brains and machines. Using natural language processing methods, I align computational models with human neural activity to study semantic representation, compositional processing, and multimodal integration. I also build language models from scratch trained on developmentally plausible input to study language acquisition, aligning these models with children's brain data to examine how neural language processing evolves with age.
In the second stream, insights from these neuroscience studies inform the development of brain-inspired language models that incorporate principles of human cognition.
Beyond foundational research, I focus on translational applications in brain-computer interfaces and healthcare AI. This includes designing multimodal brain-computer interfaces, advancing robust BCI systems for real-world deployment, and exploring novel applications in communication and clinical settings. I also develop AI-powered assistive technologies, particularly for language learning and language disorder rehabilitation.
Education and Academic Qualifications
- PhD in the Institute of Automation, Chinese Academy of Sciences
Academic and Professional Experience
- Associate Professor, Institute of Automation, Chinese Academy of Sciences
- Research Associate, Department of Psychology, Department of Linguistics, New York University
- Assistant Professor, Institute of Automation, Chinese Academy of Sciences
Teaching Areas
- Natural Language Processing
- Text Analysis
- Neuroimaging Data Processing
Research Interests
Selected Publications
- Wang, S., Kim, S., Binder, J. R., & Pylkkänen, L. (2025). Unlocking the complexity of phrasal composition: An interplay between semantic features and linguistic relations. Cognition, 254, 105986. https://doi.org/10.1016/j.cognition.2024.105986
- Lin, N., Zhang, X., Wang, X., & Wang, S. (2024). The organization of the semantic network as reflected by the neural correlates of six semantic dimensions. Brain and Language, 250, 105388. https://doi.org/10.1016/j.bandl.2024.105388
- Wang, S., Zhang, Y., Shi, W., Zhang, G., Zhang, J., Lin, N., & Zong, C. (2023). A large dataset of semantic ratings and its computational extension. Scientific Data, 10(1), 106. https://doi.org/10.1038/s41597-023-01995-6
Wang, S., Zhang, Y., Zhang, X., Sun, J., Lin, N., Zhang, J., & Zong, C. (2022). An fmri dataset for concept representation with semantic feature annotations. Scientific Data, 9(1), 721. https://doi.org/10.1038/s41597-022-01840-2
Wang, S., Zhang, X., Zhang, J., & Zong, C. (2022). A synchronized multimodal neuroimaging dataset for studying brain language processing. Scientific Data, 9(1), 590. https://doi.org/10.1038/s41597-022-01708-5
Sun, J., Wang, S., Zhang, J., & Zong, C. (2020). Neural encoding and decoding with distributed sentence representations. IEEE Transactions on Neural Networks and Learning Systems, 32(2), 589-603, 9223750. https://doi.org/10.1109/TNNLS.2020.3027595 - Wang, S., Zhang, J., Wang, H., Lin, N., & Zong, C. (2020). Fine-grained neural decoding with distributed word representations. Information Sciences, 507, 256-272. https://doi.org/10.1016/j.ins.2019.08.043
- Zhao, X., Sun, J., Wang, S., Ye, J., Zhang, X., & Zong, C. (2024, June). MapGuide: A Simple yet Effective Method to Reconstruct Continuous Language from Brain Activities. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 3822-3832. https://doi.org/10.18653/v1/2024.naacl-long.211
- Zou, S., Wang, S., Zhang, J., & Zong, C. (2022, May). Cross-modal cloze task: A new task to brain-to-word decoding. In Findings of the Association for Computational Linguistics: ACL 2022, 648-657. https://doi.org/10.18653/v1/2022.findings-acl.54
- Wang, S., Zhang, J., & Zong, C. (2018, April). Learning multimodal word representation via dynamic fusion methods. In Proceedings of the AAAI conference on artificial intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.12031
- Wang, S., Zhang, J., & Zong, C. (2017, August). Learning sentence representation with guidance of human attention. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, 4137-4143. https://doi.org/10.24963/ijcai.2017/578
- 2025 - 2028
Principal Investigator, From Learning to Communication: Building AI-Powered Tools with an Adaptive Preschool Tutor and a Multimodal Brain-Computer Interface. Start-up Fund PolyU - 2020 - 2023
Principal Investigator, Mechanisms of Semantic Representation and Brain-Inspired Models for Text Encoding. Natural Science Foundation of China (No. 61906189)
- 2021- 2025
Co-Principal Investigator, Cognitive Mechanisms and Computational Models of Language Comprehension. Natural Science Foundation of China, (No. 62036001)
- Senior AERA chair ACL 2026
- Co-Chair of Tutorial, COLING 2025
- 2022 - 2024
Exec Chair, Neuromatch Academy Curriculum - Co-Chair of Virtual Infrastructure Committee, ACL-IJCNLP 2021
- Member of Qingyuan Club in Beijing Academy of Artificial Intelligence (BBAI)
- China Association for Science and Technology Young Talent Support Project
- Young Talent Incentive of the Center for Excellence in Brain Science and Intelligent
- Technology of the Chinese Academy of Sciences
- Member of the Youth Work Committee of the Chinese Information Processing Society
- Member of Youth Innovation Promotion Association, CAS
