Zhengxiao Yu

Developed a Python framework for simulating and unfolding synthetic data used in Atals experiments of LHC. The framework models detector inefficiencies and mis-measurements to reconstruct the true distribution of the data, utilizing NumPy and SciPy for processing and manipulation. Designed modular functions to handle efficiency and resolution corrections in synthetic event data, preparing for more sophisticated data distributions beyond simple Gaussian models. Used Matplotlib for visual analysis of the unfolding results, enabling clearer interpretation of the reconstructed true distributions.

Read and organize cutting-edge research reports in the Journal of Financial Economics, and study the latest effective models and indicators. Use Python to assist the instructor in developing quantitative strategies, including data extraction, using chip distribution to build relevant indicators for model development, and multi-factor backtesting. Later practice to independently develop quantitative strategies

Worked with a mentor on various projects in 2 months, e.g., conducting market research for a chemical company’s exporting plans in Southeastern Asia, and market & policy research for Chinese car companies exporting to the European market; did report on China’s insurance market in next decade, and on future risk resolution for real estate companies. Learned and trained in data-collecting/analyzing and report-writing skills.