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MeilinP/README.md

Meilin Pan

Quantitative Research | Machine Learning | Data Science

I am an applied data scientist and quantitative researcher with an M.S. in Applied Data Science from the University of Southern California and a B.S. in Statistics and Data Science from the University of California, Santa Barbara.

I currently research systematic equity signals as a WorldQuant BRAIN Research Consultant. My interests sit at the intersection of statistical modeling, machine learning, financial markets, and reproducible research.

I have engaged in independent discretionary options trading since October 2024, with a focus on volatility, option pricing, Greeks, position construction, and risk management.

Selected Work

Quantitative Research and Financial Modeling

  • Derivatives Pricing & Risk
    End-to-end chain from market quotes to a risk number: a 40-year SOFR OIS curve bootstrapped from SR3 futures and swaps under exact market conventions, a SABR calibration of the SPY implied-volatility surface with static-arbitrage diagnostics, Greek P&L attribution on an eight-leg option book with FRTB PLA testing, and a VaR/ES and stress-testing framework documented to SR 11-7. Second-order Greek terms cut unexplained P&L by 87.6%; the validation report states the framework's failures alongside its results.

  • IMC Prosperity 4 Review
    Reconstructed and evaluated trading strategies across market making, options, auctions, and multi-asset relative value using market replay, order reconciliation, and P&L attribution.

  • WorldQuant IQC 2026
    Developed 36 equity alphas from academic and practitioner research and finished 578th of 152,452 participants globally (top 0.4%).

Machine Learning and Data Systems

  • Audio Event Detection and Notification System
    Group project integrating mobile audio collection, AWS EC2, SageMaker inference, and automated alerts. I was responsible for data preprocessing and model training.

  • Hybrid Recommendation System
    Built a PySpark-based rating prediction pipeline combining collaborative filtering with XGBoost and gradient-boosting models using user, business, check-in, photo, and interaction features.

Highlights

  • WorldQuant International Quant Championship 2026: top 0.4% globally
  • IMC Prosperity 4: top 3.1% overall, 10th in China
  • Independent discretionary options trading experience since October 2024
  • Experience researching equity signals across 10 global regions and 50,000+ data fields
  • Experience analyzing semiconductor telemetry and test logs with Python and SQL

Tools

  • Programming: Python, SQL, R
  • Machine Learning: PyTorch, scikit-learn, XGBoost, statsmodels
  • Data: pandas, NumPy, SciPy, PySpark, Polars
  • Cloud and Systems: AWS EC2, SageMaker, Git
  • Quantitative Methods: option pricing and Greeks, volatility-surface modeling, interest-rate curve construction, market microstructure, stochastic processes, time-series analysis, Monte Carlo simulation, numerical optimization, statistical arbitrage, backtesting, and P&L attribution

Connect

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