Contact Dr Xiangqi Kong
Areas of expertise
- Human Factors
- Systems Engineering
Background
Xiangqi Kong is a Research Fellow in the Faculty of Engineering and Applied Sciences.
She obtained her PhD from Cranfield University in 2026 and received her master’s degree from the School of Mechano-Electronic Engineering at Xidian University in 2017.
Dr. Xiangqi Kong’s research explores human-AI collaboration in safety-critical applications. As AI becomes increasingly integrated into human decision-making, she develops methods and interfaces that enable users to better understand and interact with complex models and systems.
Research opportunities
Enhancing Machine Learning Transparency: Concentrating on enhancing the interpretability and robustness of machine learning models.
Improving Human-AI Interaction: Focusing on data-centric AI to optimize the collaboration between humans and AI.
Developing Trustworthy AI Interfaces: Creating reliable interfaces for AI systems considering human factors.
Current activities
Human-Swarm Teaming
Explainabel Interface Framework
Explainable Time Series Forecasting
Publications
Conference Papers
- Xing Y, Kong X & Tsourdos A. (2024). RGANFormer: Relativistic Generative Adversarial Transformer for time-series signal forecasting on intelligent vehicles
- Kong X, Xing Y, Liu Z, Tsourdos A & Wikander A. (2024). Enhancing performance and interpretability of multivariate time-series model through sparse saliency
- Liu Z, Kong X, Chen Y, Wang Z, Jia H, .... (2024). Mitigating no fault found phenomena through ensemble learning: a mixture of experts approach