EgoBrain was accepted to ICLR 2026, connecting egocentric vision with brain signals for human action understanding.
Hi, I'm Yansen. Researcher, builder, and curious mind.
Yansen
Wang
I explore how brains and machines make sense of signals, time, and the world around us.
Based in Shanghai ยท Often thinking about brains, models, and good questions.
Brain-inspired AI+EEG foundation models+Multimodal learning+Time-series modeling+Neural decoding
Recent updates
Now & next
Research
Intelligence across brains, signals, and machines.
01
Neural signal intelligence
Foundation models and generative systems that translate EEG and other neural recordings into meaningful representations.
- EEG foundation models
- Brain-computer interfaces
- Neural decoding
02
Brain-inspired learning
Efficient sequence models inspired by neural dynamics, oscillations, and the computational motifs found in biological systems.
- Spiking neural networks
- Neural circuit modeling
- Sequential intelligence
03
Multimodal perception
Models that connect language, vision, behavior, and brain activity to understand perception in richer contexts.
- Brain and vision
- Language grounding
- Time-series modeling
Publications
Selected work
2026
ICLR 2026
EgoBrain: Synergizing Minds and Eyes for Human Action Understanding
N. Lin, Y. Wang, D. Han, W. Jiang, J. Li, R. Furuta, Y. Sato, D. Li
Paper2025
ICLR 2025
NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals
W. Jiang, Y. Wang, B. Lu, D. Li
Paper2025
NeurIPS 2025
SimSort: A Data-Driven Framework for Spike Sorting by Large-Scale Electrophysiology Simulation
Y. Zhang, D. Han, Y. Wang, Z. Lv, Y. Gu, D. Li
Paper2025
NeurIPS 2025
Toward Relative Positional Encoding in Spiking Transformers
C. Lv, Y. Wang, D. Han, Y. Shen, X. Zheng, X. Huang, D. Li
Paper2025
NeurIPS 2025
Chain-of-Model Learning for Language Model
X. Wang, K. Song, X. Tan, et al., Y. Wang, et al.
Paper2025
ICASSP 2025
Translating Mental Imaginations into Characters with Codebooks and Dynamics-Enhanced Decoding
J. Li, Y. Wang, N. Lin, D. Li
Paper2024
NeurIPS 2024
EEG2Video: Towards Decoding Dynamic Visual Perception from EEG Signals
X. Liu, Y. Liu, Y. Wang, K. Ren, et al.
Paper2024
ICML 2024
Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
C. Lv, Y. Wang, D. Han, X. Zheng, X. Huang, D. Li
Paper2024
NeurIPS 2024
Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators
C. Lv, D. Han, Y. Wang, X. Zheng, X. Huang, D. Li
Paper2024
CIKM 2024
Automated Contrastive Learning Strategy Search for Time Series
B. Jing, Y. Wang, G. Sui, J. Hong, J. He, Y. Yang, D. Li, K. Ren
Paper2024
AAAI Spring Symposium 2024
EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model
Y. Chen, K. Ren, K. Song, Y. Wang, Y. Wang, D. Li, L. Qiu
Paper2024
2023
NeurIPS 2023
ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
Y. Chen, K. Ren, Y. Wang, Y. Fang, W. Sun, D. Li
Paper2023
NeurIPS 2023
Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling
K. Yi, Y. Wang, K. Ren, D. Li
Paper2023
Preprint
Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals
Y. Lan, K. Ren, Y. Wang, W. Zheng, D. Li, B. Lu, L. Qiu
Paper2023
ICML 2023
CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling
Y. Wang, X. Jiang, K. Ren, C. Shan, X. Luo, D. Han, K. Song, Y. Shen, D. Li
Paper2022
CVPR 2022
RendNet: Unified 2D/3D Recognizer with Latent Space Rendering
R. Shi, X. Jiang, C. Shan, Y. Wang, D. Li
Paper2020
EMNLP 2020
Incorporating Multimodal Information in Open-Domain Web Keyphrase Extraction
Y. Wang, Z. Fan, C. P. Rose
Paper2019
AAAI 2019
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
Y. Wang, Y. Shen, Z. Liu, P. P. Liang, A. Zadeh, L. P. Morency
Paper2019
AAAI 2019
Story Ending Generation with Incremental Encoding and Commonsense Knowledge
J. Guan*, Y. Wang*, M. Huang
Paper2018
ACL 2018
Learning to Ask Questions in Open-Domain Conversational Systems with Typed Decoders
Y. Wang*, C. Liu*, M. Huang, L. Nie
PaperFor the latest citation counts and complete metadata, visit Google Scholar.
Background
Experience & education
My work has moved from language and multimodal interaction toward neural signals and brain-inspired intelligence.
2021 - present
Microsoft Research Asia
Senior Researcher, Shanghai AI/ML Group
2019 - 2021
Carnegie Mellon University
Master of Language Technologies, Language Technologies Institute
Advisor: Prof. Carolyn Penstein Rose2018
Carnegie Mellon University
Summer Research Intern, MultiComp Lab
Advisor: Prof. Louis-Philippe Morency2015 - 2019
Tsinghua University
B.E. in Computer Science and Technology
Advisor: Prof. Minlie HuangContact