Energy Economics • AI Infrastructure • Applied Econometrics
I'm an applied economist focused on energy markets, electricity demand, and FERC policy. I'm completing an M.S. in Applied Economics at the University of Maryland and hold an MPA from the University of Montana.
Before economics I spent more than a decade running operations in hospitality. That background shaped how I think about uncertainty and decision-making under real constraints, where a model is only as useful as the decision it supports and being wrong has immediate consequences.
My primary research estimates how AI infrastructure investment is reshaping regional electricity demand and whether grid planning tools are built to see it coming. AI Infrastructure and Regional Electricity Demand: Evidence from U.S. Interconnection Queues estimates a 34.8 index-point divergence in ERCOT minimum hourly demand above a synthetic counterfactual, using a four-layer identification strategy across PJM, ERCOT, and MISO. The companion policy brief argues FERC should require grid operators to publish large load interconnection queue data on the same terms as generation, tracing thirty years of precedent and proposing a specific mechanism that does not require new legislation. Replication materials are archived on Harvard Dataverse.
End-to-end: data construction, model design, identification strategy, diagnostics, interpretation. Code and documentation are part of the deliverable. When clean identification isn't available I say so, rather than overclaiming. I work in Python, R, SQL, and Stata.
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