I am a Data Scientist & Quantitative Analyst specializing at the unique intersection of advanced financial risk forecasting and production-grade Generative AI framework evaluation. Currently pursuing my Master of Applied Science in Computer Science at the University of Pennsylvania.
- AI & LLM Core: LLM & RAG Evaluation, Agentic Workflows (LangChain, LangGraph), Prompt Engineering, Synthetic Data Generation, AI Trust & Safety
- Data & Analytics: Statistical Analysis, A/B Testing & Experimentation (CUPED), Metric Design, Quant Model Validation, Stress Testing, Python, SQL, R
- Developed an adversarial evaluation platform using synthetic model-failure datasets to stress-test LLM workflows.
- Built a Streamlit validation dashboard benchmarking precision, recall, and confidence to map hallucination patterns.
- Tech Stack: Python, LLM APIs, Streamlit, LangGraph
- Spearheaded a simulated feed-ranking A/B test integrating SRM checks, power/MDE analysis, and CUPED variance reduction.
- Analyzed segment-level treatment effects to translate statistical insights into data-driven launch plans.
- Tech Stack: Python, pandas, scipy, statsmodels
- Quantitative Analyst @ CoStar Group (Current)
- Data Scientist / Consultant @ Guidehouse
- Graduate Intern @ Federal Reserve Board of Governors
๐ซ How to reach me: yullieyang@gmail.com | LinkedIn


