About Me
I am a third-year PhD student at King's College London, supervised by Prof. Jie M. Zhang, and a visiting student at the Southern University of Science and Technology, where I am supervised by Prof. Yepang Liu. Previously, I earned my MSc from the University of Birmingham with Prof. Edward Tarte and my BEng from Guangzhou University with Prof. Zhijia Zhao. I am committed to advancing trustworthy and reliable AI software and agents. My research interests primarily focus on AI agents, AI ethics, AI4Healthcare, SE4AI.
I am currently on the job market and seeking a postdoctoral position in research related to AI agents, AI alignment, SE4AI and AI4Healthcare.
Links
News
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Dec 17th
Our paper "Fairness Is Not Just Ethical: Performance Trade-Off via Data Correlation Tuning to Mitigate Bias in ML Software" (by Ying Xiao, Shangwen Wang, Sicen Liu, Dingyuan Xue, Xian Zhan, Yepang Liu, Jie Zhang) is accepted by ICSE 2026.
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Nov 22nd
Our paper "Mitigating Medical Bias in Large Language Models by Prompt Engineering: An Empirical Study of Effectiveness and Trade-offs" (Ying Xiao, Zhenpeng Chen, and Jie M. Zhang) is accepted by Philosophical Transactions of the Royal Society A.
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Apr 15, 2024
Our paper "MirrorFair: Fixing Fairness Bugs in Machine Learning Software via Counterfactual Predictions" (by Ying Xiao, Jie M. Zhang, Yepang Liu, Mohammad Reza Mousavi, Sicen Liu, Dingyuan Xue) has been accepted by FSE 2024.
Selected Publications
- Fairness Is Not Just Ethical: Performance Trade-Off via Data Correlation Tuning to Mitigate Bias in ML Software
- Mitigating Medical Bias in Large Language Models by Prompt Engineering: An Empirical Study of Effectiveness and Trade-offs
- Software Fairness Dilemma: Is Bias Mitigation a Zero-Sum Game?
- MirrorFair: Fixing Fairness Bugs in Machine Learning Software via Counterfactual Predictions
- A Comprehensive Study of Real-World Bugs in Machine Learning Model Optimization
Preprints
- Bias in Large AI Models for Medicine and Healthcare: Survey and Challenges
- AMQA: An Adversarial Dataset for Benchmarking Bias of LLMs in Medicine and Healthcare
- FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Software
Invited Talk
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2026
Mitigating machine learning software bias via correlation tuning, London, United Kingdom.
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2024
Mitigating machine learning software bias via ensembling counterfactual predictions, Porto de Galinhas, Brazil.