I build explainable causal AI—and ship it as usable work.
I’m interested in more than predictive accuracy. I care about mechanisms: what truly drives outcomes, what can be attributed to what, and how the system responds under counterfactual changes. My projects span climate extremes attribution, ESG return attribution, pricing & CLV analytics, and data extraction/automation tools.
Featured Projects
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Move beyond prediction: quantify drivers and attribute extreme-event risk.
Decompose signal vs noise and trace robust sources of return.
Explainable ML + simulation to support pricing decisions with clear tradeoffs.
Robust extraction + cleaning + dedup across messy VC portfolio pages.
About
Portfolio-style: how I work, not just labels.
How I Work
I structure problems into a reproducible chain: data selection → preprocessing & baselines → training & ablations → interpretation & attribution → robustness checks → delivery (plots, docs, code). I prefer falsifiable experiments over pretty metrics.
What I’m Looking For
I’m excited about collaborations at the intersection of causal inference, interpretability, climate/ESG risk, and decision-support tooling—especially projects with clear evaluation and concrete deliverables.
Contact
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Reach out
Email: syd8vc@virginia.edu
LinkedIn: replace the button link at the top
GitHub (optional): link it on each project page if you want to show code