Hello! 👋 I am Shuai Wang (王帅), a Ph.D. candidate in the School of Computer Science and Engineering at the University of Electronic Science and Technology of China (UESTC). From September 2026, I will join the Language Model and Human–Computer Interaction Center at Shenzhen Loop Area Institute as an Academic Rising Star researcher.

My research focuses on brain-inspired computing, spiking neural networks, efficient multimodal models, event-based object tracking, and model compression. I am particularly interested in developing accurate, energy-efficient, and hardware-friendly spiking models for visual, auditory, and multimodal intelligence on resource-constrained edge platforms.

I have published more than 20 papers in CCF-A conferences and top-tier journals, including eight first-author papers and two corresponding-author papers. Representative works have appeared at NeurIPS, ICLR, ICML, CVPR, AAAI, IEEE TNNLS, and Neural Networks.

Email · Google Scholar · GitHub · Citation Record

📖 Education and Experience

• 2021.09 – 2027.06 (Expected), Ph.D. Candidate, School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China.
• 2026.09 – 2027.06 (Expected), Academic Elite Joint-Training Researcher, Language Model and Human–Computer Interaction Center, Shenzhen Loop Area Institute, Shenzhen, China.
• 2016.09 – 2020.06, B.Eng., School of Computer Science and Technology, Xidian University, Xi’an, China.

🔬 Research Interests

• Brain-inspired computing and spiking neural networks
• Spiking Transformers and efficient sequence modeling
• Multimodal brain-inspired models for edge intelligence
• Quantization, pruning, distillation, and hardware-efficient deployment

🎖 Honors and Awards

• 2026.06, First Prize Award(一等奖), ACM Multimedia 2026 AdoDAS Emotion Analysis Challenge (CCF-A).
• 2025.08, Best Dataset & Benchmark Award（最佳数据集及基准奖）, IJCAI 2025 Spike-CV Challenge (CCF-A).
• 2025.10, National Scholarship for Ph.D. Students（博士国家奖学金）, Ministry of Education of China.
• 2026.03, Academic Rising Star（学术新秀）, University of Electronic Science and Technology of China.
• 2025, Spotlight Paper (Top 3%), NeurIPS 2025, for Bipolar Self-attention for Spiking Transformers.
• 2025, Oral Paper (Top 0.96%), CVPR 2025, for Rethinking Spiking Self-Attention Mechanism: Implementing α-XNOR Similarity Calculation in Spiking Transformers.

💬 Academic Services

• Conference Reviewer: NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, AAAI, and ACM Multimedia.
• Journal Reviewer: IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Transactions on Cognitive and Developmental Systems, Neural Networks, and Neurocomputing.

📝 Selected Publications

^† Equal contribution. ^* Corresponding author. My name is shown in bold.

Representative Papers

NeurIPS 2025 Spotlight

Bipolar Self-attention for Spiking Transformers CCF-A Spotlight

Shuai Wang, Malu Zhang, Jingya Wang, Dehao Zhang, Yimeng Shan, Jieyuan Zhang, Yichen Xiao, Honglin Cao, Haonan Zhang, Zeyu Ma, Yang Yang, Haizhou Li

NeurIPS 2025, Spotlight (Top 3%)

• Introduces bipolar self-attention to model multi-polar membrane-potential interactions in Spiking Transformers.
• Develops Shiftmax for efficient low-entropy attention allocation while preserving spike-driven computation.

[Paper] [OpenReview]

ICLR 2025

Spiking Vision Transformer with Saccadic Attention CCF-A

Shuai Wang, Malu Zhang, Dehao Zhang, Ammar Belatreche, Yichen Xiao, Yu Liang, Yimeng Shan, Qian Sun, Enqi Zhang, Yang Yang

• Introduces Saccadic Spike Self-Attention to improve spatial relevance and temporal interactions in Spiking Vision Transformers.
• Achieves strong performance across visual tasks with linear computational complexity.

[Paper] [arXiv]

ICLR 2026

Robust Spiking Neural Networks Against Adversarial Attacks CCF-A

Shuai Wang, Malu Zhang, Yulin Jiang, Dehao Zhang, Ammar Belatreche, Yu Liang, Yimeng Shan, Zijian Zhou, Yang Yang, Haizhou Li

• Identifies threshold-neighboring neurons as a key factor limiting adversarial robustness in directly trained SNNs.
• Proposes Threshold Guarding Optimization to reduce state flipping under small perturbations.

[Paper] [arXiv]

IEEE TNNLS

SNN-FT: Temporal-Coded Spiking Neural Networks for Fourier Transform 中科院一区

Shuai Wang, Haorui Zheng, Yukun Chen, Ammar Belatreche, Guoqing Wang, Yeying Jin, Jibin Wu, Malu Zhang, Yang Yang, Haizhou Li

• Develops a temporal-coded spiking implementation of the Fourier transform.
• Provides an efficient neuromorphic signal-processing primitive with reduced latency and energy consumption.

[Paper]

CVPR 2025 Oral

Rethinking Spiking Self-Attention Mechanism: Implementing α-XNOR Similarity Calculation in Spiking Transformers CCF-A Oral Co-first

Yichen Xiao^†, Shuai Wang^†, Dehao Zhang, Wenjie Wei, Yimeng Shan, Xiaoli Liu, Yulin Jiang, Malu Zhang

• Rethinks binary similarity estimation in Spiking Transformers through an α-XNOR attention mechanism.
• Equal contribution.

[Paper]

Neural Networks 2025

Ternary Spike-Based Neuromorphic Signal Processing System 中科院二区

Shuai Wang, Dehao Zhang, Ammar Belatreche, Yichen Xiao, Hongyu Qing, Wenjie Wei, Malu Zhang, Yang Yang

• Develops threshold-adaptive encoding and a quantized ternary SNN for efficient signal processing.
• Supports speech and EEG recognition with substantially reduced memory and energy consumption.

[Paper] [arXiv]

NeurIPS 2024

Spike-Based Neuromorphic Model for Sound Source Localization CCF-A Co-first

Dehao Zhang^†, Shuai Wang^†, Ammar Belatreche, Wenjie Wei, Yichen Xiao, Haorui Zheng, Zijian Zhou, Malu Zhang, Yang Yang

• Integrates phase-locking auditory encoding with spike-based neural computation for sound-source localization.
• Uses Resonate-and-Fire neurons to capture biologically meaningful spectral and temporal cues.

[Paper] [OpenReview]

INTERSPEECH 2024

Global-Local Convolution with Spiking Neural Networks for Energy-Efficient Keyword Spotting CCF-B

Shuai Wang, Dehao Zhang, Kexin Shi, Yuchen Wang, Wenjie Wei, Jibin Wu, Malu Zhang

• Introduces Global-Local Spiking Convolution for sparse and energy-efficient speech feature extraction.
• Combines it with a Bottleneck-PLIF module to achieve competitive accuracy with a compact model.

[Paper] [PDF]

AAAI 2026

Towards Training-Free and Accurate ANN-to-SNN Conversion via Activation-Aware Redistribution CCF-A Corresponding

Honglin Cao, Shuai Wang^*, Zijian Zhou, Ammar Belatreche, Wenjie Wei, Yu Liang, Yang Yang, Rui Xi, Malu Zhang, Haizhou Li

• Introduces activation-aware redistribution to reduce the mismatch between ANN activations and SNN firing rates.
• Enables accurate, low-latency ANN-to-SNN conversion without retraining.

[Paper] [PDF]

ICML 2026

SpikingLM: Towards Fully Spiking Language Model CCF-A Corresponding

Yu Liang, Zijian Zhou, Wenjie Wei, Shuai Wang^*, Honglin Cao, Ammar Belatreche, Yu Yang, Malu Zhang, Yang Yang, Haizhou Li

• Develops a fully spiking architecture for efficient language modeling.
• Replaces major dense ANN operations with spike-driven computation while retaining competitive language-modeling capability.

[Paper]

Additional Selected Papers

1. Malu Zhang, Shuai Wang, Jibin Wu, Wenjie Wei, Dehao Zhang, Zijian Zhou, Siying Wang, Fan Zhang, Yang Yang. Toward Energy-Efficient Spike-Based Deep Reinforcement Learning with Temporal Coding. IEEE Computational Intelligence Magazine, 20(2):45–57, 2025. CCF-A

2. Dehao Zhang, Malu Zhang, Shuai Wang, Jingya Wang, Wenjie Wei, Zeyu Ma, Guoqing Wang, Yang Yang, Haizhou Li. Dendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling. NeurIPS 2025. CCF-A

3. Jieyuan Zhang, Xiaolong Zhou, Shuai Wang, Wenjie Wei, Hanwen Liu, Qian Sun, Malu Zhang, Yang Yang, Haizhou Li. Unveiling the Spatial-Temporal Effective Receptive Fields of Spiking Neural Networks. NeurIPS 2025. CCF-A

4. Wenjie Wei, Malu Zhang, Jieyuan Zhang, Ammar Belatreche, Shuai Wang, Yimeng Shan, Hanwen Liu, Honglin Cao, Guoqing Wang, Yang Yang, et al. S²NN: Sub-Bit Spiking Neural Networks. NeurIPS 2025. CCF-A

5. Dehao Zhang, Shuai Wang, Yichen Xiao, Wenjie Wei, Yimeng Shan, Malu Zhang, Yang Yang. Memory-Free and Parallel Computation for Quantized Spiking Neural Networks. ICASSP 2025. CCF-A

6. Yu Liang, Yu Yang, Wenjie Wei, Ammar Belatreche, Shuai Wang, Malu Zhang, Yang Yang. BSO: Binary Spiking Online Optimization Algorithm. ICML 2025. CCF-A

7. Yu Liang, Wenjie Wei, Ammar Belatreche, Shuai Wang, Malu Zhang, Yang Yang. Towards Accurate Binary Spiking Neural Networks: Learning with Adaptive Gradient Modulation Mechanism. AAAI 2025, Oral. CCF-A Oral

8. Kexin Shi, Hanwen Liu, Zeyang Song, Yang Liu, Jieyuan Zhang, Shuai Wang, Jibin Wu, Malu Zhang, Yang Yang. Temporal Interaction in Spiking Transformers with Multi-Delay Mixer. CVPR 2026. CCF-A

9. Jingya Wang, Xin Deng, Wenjie Wei, Dehao Zhang, Shuai Wang, Qian Sun, Jieyuan Zhang, Hanwen Liu, Ning Xie, Malu Zhang. Training-Free ANN-to-SNN Conversion for High-Performance Spiking Transformers. AAAI 2026. CCF-A

Preprints and Manuscripts

1. Shuai Wang, Malu Zhang, Dehao Zhang, Yimeng Shan, Jieyuan Zhang, Siqi Cai, Jibin Wu, Yang Yang, Huajin Tang, Haizhou Li. Towards High-Performance and Energy-Efficient Spiking Transformers with Bipolar Self-Attention. IJCV Manuscript under review.

2. Shuai Wang, Jieyuan Zhang, Yang Yang, Huajin Tang, Haizhou Li. Algorithm–Hardware Co-Design of Binary Spiking Transformers for Edge Intelligence. TPAMI Manuscript under review.