Han Shu is a PhD Candidate at the University of Sydney whose research focuses on reinforcement learning and large language model (LLM) routing. His work aims to improve the decision-making capabilities of AI systems by combining adaptive control from reinforcement learning with efficient routing strategies for large-scale language models, enabling faster, more accurate, and context-aware responses.
In addition to his research, Han serves as a casual academic at the University of Sydney, teaching courses in predictive analytics, machine learning, and data mining. He has prior industry experience as a software engineer and research assistant, contributing to projects in distributed systems, deep learning, and intelligent systems. Han’s interdisciplinary background bridges machine learning, reinforcement learning, and AI system optimization.