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
CachePrune: Neural-Based Attribution Defense Against Indirect Prompt Injection Attacks
2026
SceneAlign: Aligning Multimodal Reasoning to Scene Graphs in Complex Visual Scenes
2026
MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning
2026
Advantage-Conditioned Flow Policy for Offline Reinforcement Learning in Recommendation
2026
RegionSLM: Region-aware Question Answering on Document Screenshots
2026
Latest News
Three papers accepted to ACL 2026
Xiaocong's work 'Advantage-Conditioned Flow Policy for Offline Reinforcement Learning in Recommendation' accepted to SIGIR 2026
Chao's work 'RegionSLM: Region-aware Question Answering on Document Screenshots' accepted to SIGIR 2026
Four papers accepted to SIGIR 2026
Congratulations to Dr Haodong Lu on thesis submission