Quantitative scientist with an economics PhD background, working across statistical modelling, econometrics, machine learning evaluation, and behavioral research.

I build and evaluate models for structured data, with attention to uncertainty, error analysis, and the conditions under which quantitative results are useful for decisions.

• RAG Search Engine
  Python search system combining BM25, Sentence Transformers, CLIP, reciprocal rank fusion, query enhancement, multimodal search, caching, and evaluation workflows.

• CitiBike Demand, Risk, and Net Flow Analysis
  Applied analysis of CitiBike usage, bike-type mix, membership patterns, station-level risk, and net-flow dynamics using trip and collision data.

• Economic Theories and Machine Learning
  Research code for evaluating economic theories using machine learning techniques.

• Efficiency Wages with Motivated Agents
  Research repository connected to work on efficiency wages, prosocial motivation, and effort provision.

Quantitative: statistical modelling, econometrics, hypothesis testing, model evaluation, error analysis
Programming: Python, pandas, NumPy, scikit-learn, statsmodels, SQL, R, Stata, Git, Linux
Research: behavioral economics, personnel economics, organizational economics, contract theory, incentives, social preferences

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