About Me

Hello! I'm Jiaming Yang (also known as June Yang), a graduate of UNSW Sydney with a Master of IT specialising in Artificial Intelligence (Class of 2026). My background spans telecommunications engineering, machine learning, and applied AI research, with a strong interest in building intelligent systems that operate reliably in real-world environments.

My work is centered on time-series forecasting for real-world systems, where I explore how machine learning models can be designed, constrained, and deployed to interact with physical, autonomous, and decision-making systems. I am particularly interested in applications involving energy AI, embodied autonomous agents, and structured decision-making under uncertainty.

Across my projects, I focus on bridging rigorous theoretical foundations with practical deployment—whether integrating temporal models with physical constraints, deploying learning systems on resource-limited platforms, or extending data-driven approaches with domain-specific language models. I value research that not only performs well in controlled settings, but also remains interpretable, robust, and meaningful when exposed to real-world complexity.

My guiding principle is simple: learn rigorously, apply responsibly. Every method I study must eventually face real-world conditions, and every deployed system should remain grounded in sound theory. I see research as a continuous loop between understanding, implementation, and refinement—where impact emerges through careful design rather than abstraction alone.