Fan Yang
Quantitatively rigorous data scientist holding an MSc in Economics from the London School of Economics and a First Class BSc Hons Economics from the University of Nottingham. I possess a solid technical foundation in statistical modelling, Python, and SQL. My execution focuses on designing machine learning pipelines and engineering LLM workflows. I have built ensemble models combining Random Forest and XGBoost to predict market behaviour and applied NLP techniques to extract structural signals from unstructured texts. I am actively constructing applications driven by autonomous AI agents, leveraging Claude Code and Antigravity. I operate with strict attention to detail and am highly equipped to build, evaluate, and ship production-grade NLP and machine learning models in fast-paced environments
Work by Fan Yang
- THE GALLERY BY F.Y. | SILENT FLÂNERIE
Personal Vibe Coding Project; Github Repository: https://github.com/RossyKenobi/my-gallery.git; Website Access: penumbrae.uk/