I'm working at the intersection of applied statistics, machine learning, and data engineering.
I’m a data-driven researcher with expertise in applied statistics, causal inference, and machine learning. My background in quantitative political science has given me broad experience working with high-dimensional, messy, and often spatially structured data. I enjoy building end-to-end analytical workflows: Cleaning and engineering data, designing identification strategies, developing predictive or causal models, and deploying reproducible pipelines that others can build on. Across my projects I’ve worked with causal inference frameworks (RDD, IV, diff-in-diff, matching), classical and modern ML methods, geospatial toolchains, and data engineering techniques for structuring large-scale workflows.
- Causal inference (RDDs, Instrumental Variables, Panel & Time Series Data)
- Spatial econometrics
- Machine learning & LLMs
- Data Engineering
| Category | Tools |
|---|---|
| Stats & ML & GIS |    |
| Workflow & Containers |   |
| Databases |  |
| Version Control |   |
| Web |   |
| Typesetting |  |
| Scripting & OS |   |
📫 Connect with me on LinkedIn.