Ph.D. in Computer Science

University of Liverpool, UK

Master of Research in Computer Science

University of Liverpool, UK

B.S. in Mechanical Design and Manufacture and Automation

Harbin University of Science and Technology, China

Dr. Zhiqiang Gao joined the Department of Computer Science at Wenzhou-Kean University as an Assistant Professor in August 2024. Before entering academia, he worked in managerial and engineering roles at major manufacturing companies, including Delta Electronics and Shantui Construction Machinery, where he gained substantial experience in industrial practice and engineering problem-solving. This combination of industrial experience and academic research has shaped his strong interest in developing AI methods that remain reliable and effective in complex real-world environments. Dr. Gao’s research focuses on trustworthy machine learning, robust learning, and the reliable deployment of artificial intelligence models in complex real-world environments. In particular, he studies how AI systems can maintain strong accuracy, stability, and generalization under distribution shifts, noise, limited data, and continuously changing conditions. His work addresses a common challenge in practical AI deployment: models that perform well in controlled laboratory settings may become unreliable when the operating environment differs from the training data. His research therefore spans distribution-shift analysis, robust learning methods, generative data augmentation, and real-world validation.

Dr. Gao has published multiple high-quality papers as first or corresponding author, with work appearing at ICML, CVPR, ICCV, AAAI, and ACM Multimedia. He serves as Principal Investigator on one project funded by the Zhejiang Provincial Natural Science Foundation and one Wenzhou Applied Basic Research Project, focusing on high-fidelity and diverse medical image generation for robust segmentation. His current research also extends to medical imaging, low-altitude unmanned aerial systems, intelligent manufacturing, and logistics inspection, with the goal of improving the reliability of AI systems in complex and changing environments.

Trustworthy Machine Learning, Vision-Language Models, Model Generalization and Transfer Mechanism, Adversarial Robustness and Corruption Robustness, Medical Image Generation and Analysis, Embodied AI, Robust Low-Altitude Perception, Intelligent Manufacturing.

1. Jinping Wang, Qinhan Liu, Zhiwu Xie, Zhiqiang Gao*, “Fix the Loss, Not the Radius: Rethinking the Adversarial Perturbation of Sharpness-Aware Minimization.” Proceedings of the International Conference on Machine Learning (ICML), 2026. [CCF-A]

2. Jinping Wang, Zixin Tong, Zhiwu Xie, Zhiqiang Gao*, “Rethinking Loss Reweighting for Imbalance Learning as an Inverse Problem: A Neural Collapse Point of View.” Proceedings of the International Conference on Machine Learning (ICML), 2026. [CCF-A]

3. Zhihao Dou, Qinjian Zhao, Zhongwei Wan, Dinggen Zhang, Weida Wang, Towsif Raiyan, Benteng Chen, Qingtao Pan, Yang Ouyang, Chaoda Song, Zhiqiang Gao*, Shufei Zhang*, Sumon Biswas, “Plan then Action: High-Level Planning Guidance Reinforcement Learning for LLM Reasoning.” Proceedings of the International Conference on Machine Learning (ICML), 2026. [CCF-A]

4. Jinping Wang, Zhiqiang Gao*, Dinggen Zhang, Zhiwu Xie, “Escaping Optimization Stagnation: Taking Steps Beyond Task Arithmetic via Difference Vectors.” Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2026. [CCF-A, Oral Presentation]

5. Jinping Wang, Zhiqiang Gao*, Zhiwu Xie, “Space Alignment Matters: The Missing Piece for Inducing Neural Collapse in Long-Tailed Learning.” Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2026. [CCF-A]

6. Zhibin Wan†, Zhiqiang Gao†, Mingjie Sun, Yang Yang, Cao Min, Hongliang He, Guohong Fu, “Rethinking Hard Training Sample Generation for Medical Image Segmentation.” Pattern Recognition, 2026. [CCF-B, JCR Q1]

7. Zhibin Wan†, Zhiqiang Gao†, Mingjie Sun, Yupei Wu, Guohong Fu, Ran Yi, “Attention-Guided Energy Optimization for Label-Aligned Anomaly Generation.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Findings, 2026. [CCF-A]

8. Kunpeng Qiu, Zhiqiang Gao*, Zhiying Zhou, Mingjie Sun*, Yongxin Guo*, “Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. [CCF-A]

9. Zhiqiang Gao, Kaizhu Huang, Rui Zhang, Dawei Liu, Jieming Ma, “Towards Robustness against Common Corruption for Unsupervised Domain Adaptation.” Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023. [CCF-A]

10. Zhiqiang Gao, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Rui Zhang, Chaoliang Zhong, “Certifying Better Robust Generalization for Unsupervised Domain Adaptation.” Proceedings of the ACM International Conference on Multimedia (ACM MM), 2022. [CCF-A]

11. Zhiqiang Gao, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Chaoliang Zhong, “Gradient Distribution Alignment Certificates Better Adversarial Domain Adaptation.” Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021. [CCF-A]

1. Zhejiang Provincial Natural Science Foundation – Exploratory Project, “An Orthogonalized Stage-wise Framework for High-Fidelity and Diverse Medical Image Generation toward Robust Segmentation,” Principal Investigator, Jan. 2026–Dec. 2027.

2. Wenzhou Applied Basic Research Project, “High-Fidelity and Diverse Medical Image Generation for Robust Segmentation,” Principal Investigator, Sep. 2025–Sep. 2028.