Professor · UNSW Sydney
Lina Yao
AI researcher advancing the frontiers of few-shot learning, deep reinforcement learning, and brain-computer interfaces. ARC Future Fellow and Clarivate Highly Cited Researcher.
“知行合一” — Unity of knowledge and action
Highly Cited Researcher
Clarivate 2024 & 2025
ARC Future Fellow
Commencing 2026
Senior Member
ACM & IEEE
Research Highlights
Few-Shot & Zero-Shot Learning
Developing methods that enable machine learning models to generalize from very few or even zero labeled examples. Our work explores meta-learning, transfer learning, and prompt-based techniques to achieve strong performance in data-scarce settings.
Deep Reinforcement Learning
Building intelligent agents that learn to make sequential decisions through trial and error. We investigate model-based RL, multi-agent systems, and safe RL with applications in robotics, autonomous navigation, and resource optimization.
Self-Supervised & Generative Learning
Exploring contrastive learning, masked autoencoders, and diffusion models for learning rich representations without manual labels. Applications include image generation, video understanding, and scientific data analysis.
Brain-Computer Interface
Decoding neural signals (EEG, fMRI) using deep learning for understanding brain activity and enabling direct brain-to-device communication. Our work bridges neuroscience and AI for assistive technology and cognitive computing.
Recommender Systems
Advancing personalized recommendation through causal inference, knowledge graphs, and multi-modal understanding. We focus on fairness, explainability, and cross-domain generalization in recommendation scenarios.
Selected Publications
Unifying Stable Optimization and Reference Regularization in RLHF
2026
PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generation
2026
DrunkAgent: Stealthy Memory Corruption in LLM-Powered Recommender Agents
2026
Gaussian Mixture Flow Matching with Domain Alignment for Multi-Domain Sequential Recommendation
2026
Dual conditional diffusion models for sequential recommendation
2026
Latest News
Congratulations to Dr Jingcheng Li on his future endeavors
Li's work 'Unifying Stable Optimization and Reference Regularization in RLHF' accepted to ICLR 2026
Shiyi's work 'DrunkAgent: Stealthy Memory Corruption in LLM-Powered Recommender Agents' accepted to WebConf 2026
Xiaoxin's work 'Gaussian Mixture Flow Matching with Domain Alignment for Multi-Domain Sequential Recommendation' accepted to WebConf 2026
Congratulations to Dr Hongtao Huang on future endeavors