Research

My research rethinks how we operate society's energy systems by treating randomness as a computational resource. I work at the intersection of power systems engineering, statistics, and graph algorithms.

Research Themes

Randomized Network Design. Many infrastructure problems are intractable mixed-integer programs. By solving for switching probabilities rather than deterministic configurations, we bypass combinatorial complexity. [arXiv]

Energy Affordability. My work introduced locational marginal burden (LMB), quantifying how grid parameters affect the fairness of electricity prices. This framework enables planners to target infrastructure investments that reduce energy burden for vulnerable communities. [e-Energy '24]

Network Tomography. Recovering network structure from corrupted measurements–-phaseless, quantized, or low-rate. My work characterizes conditions for identifiability and develops practical algorithms for topology learning and predicting grid behavior [IEEE TPWRS '24], [IEEE CDC '24].

The road ahead: Extending randomized numerical techniques to problems involving the operation and planning of power networks that leverage the underlying physics and domain structure.

For a complete list, see all papers. For application materials, please contact me.