About
I am a power sector specialist with technical roots in statistical signal processing, machine learning, graph theory, and optimization. I have extensive experience working at the intersection of energy and computation across startups, academia, and international organizations. I hold a bachelor’s degree in Computer Science and a Ph.D. in Electrical Engineering and Computer Sciences, both from the University of California, Berkeley.
My research has focused on developing algorithms and measurement methods for enhanced grid visibility to bolster renewable integration, system resilience, and operational efficiency. Designing algorithmic tools that succeed within real-world constraints—such as measurement noise, system uncertainties, sensor coverage and resolution limitations, and human user needs—has been a consistent theme in my work. I have also worked on a diversity of energy system modeling projects, from network simulation to capacity expansion planning.
Outside the energy sector, my love of signal processing has, in the past, led me to work in domains such as agricultural automation and medical imaging.
Interests
Power system measurement and monitoring
I am interested in sensing technologies and data analytics to support situational awareness and decision-making across time scales in the power grid. I have worked with a variety of sensors -- synchrophasors, smart meters, plug-point sensors -- on diverse applications, including topology estimation, transmission event detection and classification, impact evaluation of grid investments, and power quality monitoring. My dissertation work focused on developing algorithms for grid monitoring that work under real-world constraints such as noise, limited sensing, and human user needs. I went on to develop and deploy grid monitoring tools for transmission utilities, leveraging high-resolution sensors, particularly in the context of renewable integration, novel large loads, and grid enhancing technologies; as part of that work, I led the delivery of a Department of Energy grant on machine learning for transmission event detection and classification. I remain interested in improving visibility at the grid edge with practical sensor and data constraints.
Optimization for grid operations and planning
I have applied optimization techniques to power systems applications including capacity expansion planning. I worked with the power systems planning team at the World Bank, where optimized power systems plans informed economic and financial analyses of Bank projects, and supported countries with long-term policy development. I led a CCAI grant to develop open data and tools that enable ML solver accelerations for the planning problem. At operational time scales, I have worked on optimization of distributed energy resources to provide grid services or defer grid investments.
Global south grids
I am interested in the particular challenges facing power grids in the global south, including widespread power quality issues and high technical losses that incur social, environmental, and financial costs. I have worked on new technologies that
Weather and remote sensing
I work on data and tools to support weather forecasting and remote sensing, as part of the Nimbus program.
Applied statistics and machine learning
I am broadly interested in applied statistics and machine learning in the energy sector and beyond. I have developed machine learning models for agricultural automation and medical imaging. I provided technical consulting for the statistically rigorous design of clinical trials.
