About

My interest in techno-economic analysis comes from working with complex systems where decisions are shaped by both data and real-world constraints. Early on, I spent time building tools and models designed to evaluate markets using structured datasets and generate insights at scale.

Through that experience, I began to see that while data can reveal important patterns, it does not always capture the full picture. Outcomes often depend on factors such as local variation, underlying assumptions, and limitations in how data is collected and structured.

This perspective has carried into my work in energy and techno-economic modeling, where I focus not just on generating analytical outputs, but on understanding how those outputs are used. In areas like infrastructure planning and policy, this means ensuring that analysis is grounded in both data and the practical realities it is meant to inform.

I studied economics at UC Davis with an interest in how markets and policy shape real-world systems. That interest led me to energy, where these dynamics are especially visible.

At Energeia, I worked on modeling and analysis related to electrification, infrastructure planning, and long-term energy transitions. This included evaluating how different technology and policy pathways impact costs, adoption, and system-wide outcomes over time.

Much of this work involved building data-driven models to understand how decisions made today translate into long-term economic and infrastructure outcomes, particularly in the context of decarbonization and grid modernization.

Alongside my consulting experience, I’ve also explored building data-driven tools, applying similar analytical thinking to problems outside the energy sector. This has strengthened my interest in not just analyzing systems, but designing ways to interact with and interpret complex datasets.

My interests focus on the intersection of energy economics, infrastructure planning, and data-driven decision making.

I am particularly interested in how electrification and grid modernization can be achieved while maintaining affordability for consumers. This includes understanding the tradeoffs between long-term system investment and near-term cost impacts on households.

I am also interested in how large, complex datasets can be structured and analyzed to support better decision-making, especially in domains where geographic and economic factors intersect.