My research interests sit at the intersection of market prediction, statistical learning, and systematic trading infrastructure. The notes below describe public-facing themes rather than employer-specific work or proprietary systems.

Market Prediction and Statistical Learning

Research Interest

I am interested in how noisy, non-stationary market data can be transformed into stable forecasting problems. My focus is on model design, feature validation, and diagnostics that help separate robust patterns from overfit artifacts.

  • Cross-sectional and time-series prediction under changing market regimes
  • Model diagnostics for signal stability, calibration, and error decomposition
  • Careful validation design to reduce leakage, selection bias, and false discovery
Machine Learning Forecasting Model Diagnostics Validation

Market Microstructure and Prediction Markets

Research Interest

I study how information, liquidity, and participant incentives shape market prices. Prediction markets are especially useful for thinking about price discovery, binary payoffs, and the relationship between beliefs and tradable contracts.

  • Price formation and liquidity in event-driven markets
  • Forecast interpretation for binary and bounded-payoff instruments
  • Risk-aware evaluation of signals under transaction costs and execution frictions
Market Microstructure Prediction Markets Price Discovery Risk

Research Infrastructure for Systematic Trading

Research Method

Much of my work is motivated by the gap between a promising research idea and a reliable research workflow. I care about reproducible experiments, explicit assumptions, and infrastructure that makes results easier to audit.

  • Data quality checks, experiment tracking, and reproducible research runs
  • Backtest design with attention to timestamps, missing data, and universe changes
  • Test coverage and code review practices for quantitative research code
Research Engineering Backtesting Testing Reproducibility

Blockchain Economics and Mechanism Design

Academic Work

My academic work uses game theory and mechanism design to analyze incentives in blockchain and data markets. I am particularly interested in how protocol rules shape behavior when participants have heterogeneous information and objectives.

  • Validator incentives and coordination problems in scaling mechanisms
  • Data-property-right allocation under asymmetric information
  • Welfare analysis using Bayesian games and related economic models
Game Theory Mechanism Design Blockchain Economics Bayesian Games