I am a PhD candidate in Computer Science at the University of Warwick, working at the intersection of artificial intelligence, neuroimaging, and computational neuroscience.

My research develops fine-grained brain foundation models for learning generalizable representations from large-scale fMRI and EEG data. I am particularly interested in functional brain organization, neurodevelopment, and individual variability. I further connect individualized brain representations with personalized brain parcellation and computational neuromodulation, with the long-term goal of linking brain representation, organization, and intervention within a unified framework for personalized neuroscience.

For more on our fMRI foundation-model research, please visit fMRIatlas.

News

2026.09
FlatClip was accepted to NeurIPS 2026.
2026.09
BrainWorld was selected for an oral presentation at NeurIPS 2026.
2026
Omni-fMRI and FlexiBrain were accepted to ICML and ECCV; Brain-DiT was selected for the MICCAI Best Paper Shortlist.

Selected Publications

^† Equal contribution; ^* co-corresponding author.

NeurIPS 2026

FlatClip: A Geometry-Aware Surface-Level Baseline for fMRI Representation Learning

Mo Wang, Wenhao Ye, Zihan Ning, Jiayu Zuo, Junfeng Xia, Hongkai Wen^*, Quanying Liu^*

FlatClip reuses a frozen image foundation model over geometry-aware cortical flatmap sequences, providing a practical surface-level baseline between ROI and voxel representations.

NeurIPS 2026 Oral

BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamics

Junfeng Xia, Wenhao Ye, Junxiang Zhang, Xuanye Pan, Mo Wang^*, Quanying Liu^*

A structural-prior-conditioned generative model for long-horizon whole-brain 4D fMRI dynamics and transferable multimodal representation learning.

Paper

ECCV 2026

FlexiBrain: A Mamba-JEPA Architecture for Voxel-Level Representation Learning from Variable-Resolution Native fMRI

Mo Wang^†, Wenhao Ye^†, Junfeng Xia^†, Minghao Xu, Hongkai Wen, Quanying Liu

Voxel-level representation learning for variable-resolution native fMRI through flexible spatial-temporal patching and Mamba-based predictive learning.

Paper Code Model

MICCAI 2026 Best Paper Shortlist

Brain-DiT: A Universal Multi-state fMRI Foundation Model with Metadata-Conditioned Pretraining

Junfeng Xia, Wenhao Ye, Xuanye Pan, Xinyu Shen, Mo Wang^*, Quanying Liu^*

Metadata-conditioned diffusion pretraining for multi-state fMRI and downstream demographic and clinical prediction.

Paper Code

ICML 2026

Omni-fMRI: A Universal Atlas-Free fMRI Foundation Model

Mo Wang^†, Wenhao Ye^†, Junfeng Xia, Junxiang Zhang, Xuanye Pan, Minghao Xu, Haotian Deng, Hongkai Wen, Quanying Liu

Atlas-free representation learning for broad fMRI analysis across cohorts, brain states, and downstream tasks.

Paper Code Model Docker

NeurIPS 2025

DCA: Graph-Guided Deep Embedding Clustering for Brain Atlases

Mo Wang^†, Kaining Peng^†, Jingsheng Tang^†, Hongkai Wen, Quanying Liu

Graph-guided deep clustering for data-driven atlas construction and interpretable personalized functional organization.

Paper Code

NeuroImage 2023

Multi-objective optimization via evolutionary algorithm (MOVEA) for high-definition transcranial electrical stimulation of the human brain

Mo Wang, Kexin Lou, Zeming Liu, Pengfei Wei, Quanying Liu

A multi-objective evolutionary framework for individualized stimulation planning across intensity, focality, and avoidance-region constraints.

Paper Code

Additional Publications

Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-ANS Dynamic.
Zhoujie Hou, Song Wang, Kexin Lou, Mo Wang, Chen Wei, Quanying Liu. MICCAI 2026. Paper · Code
A 5.0 T Ultra-High-Field fMRI Dataset for Naturalistic Visual Scene Processing.
Gengchen Ye^†, Mo Wang^†, Chiyin Li, Yihao Peng, Yilin Qian, Yutao Wang, Xinyi Si, Shaoxin Xiang, Fanzhi Jiang, Lu Wang, Ming Zhang. Scientific Data, 2026. Paper
OpTI-Mouse: Optimization for Targeted Temporal Interference Stimulation in the Mouse Brain.
Jingsheng Tang^†, Zhengkang Zhou^†, Yingyue Xin, Zihan Ning, Pengfei Wei, Mo Wang^*, Quanying Liu^*. EMBC 2026. Paper
Personalized transcranial electrical stimulation: A review of computational modeling and optimization.
Mo Wang, Kexin Zheng, Yawen Xin, Xinyi Chen, Yifei Liu, Huichun Luo, Ti-Fei Yuan, Hongkai Wen, Pengfei Wei, Quanying Liu. Journal of Neural Engineering, 2026. Paper
Transcranial temporal interference stimulation precisely targets deep brain regions to regulate eye movements.
Mo Wang^†, Sixian Song^†, Dan Li, Guangchao Zhao, Yu Luo, Yi Tian, et al. Neuroscience Bulletin, 2025. Paper
Frequency-specific and state-dependent neural responses to brain stimulation.
Huichun Luo^†, Xiaolai Ye^†, Hui-Ting Cai^†, Mo Wang^†, et al. Molecular Psychiatry, 2025. Paper
Stimulation of an entorhinal-hippocampal extinction circuit facilitates fear extinction in a post-traumatic stress disorder model.
Ze-Jie Lin, Xue Gu, Wan-Kun Gong, Mo Wang, Yan-Jiao Wu, Qi Wang, Xin-Rong Wu, Xin-Yu Zhao, Michael X. Zhu, Lu-Yang Wang, Quanying Liu, Ti-Fei Yuan, Wei-Guang Li, Tian-Le Xu. The Journal of Clinical Investigation, 2024. Paper
Explainable fMRI-based brain decoding via spatial temporal-pyramid graph convolutional network.
Ziyuan Ye, Youzhi Qu, Zhichao Liang, Mo Wang, Quanying Liu. Human Brain Mapping, 2023. Paper

Education

2022 - 2026
PhD candidate in Computer Science
Department of Computer Science, University of Warwick, UK
2019 - 2020
MSc in Computer Science
Department of Computer Science, University of Birmingham, UK
2015 - 2019
BS in the Internet of Things
Department of Computer Science, Northwest University, China

Experience

2025 - 2026
Visiting Student
Department of Biomedical Engineering, Southern University of Science and Technology, China
2021 - 2022
Research Assistant
Southern University of Science and Technology, China
2020 - 2021
Research Assistant
University of Birmingham, UK