Bio

I am a Data Science Manager at Archer. Here, I work on the machine learning models, optimization models, and the software suite that support planning and operations, including Prime Radiant (see here for more details). Prime Radiant is Archer’s data science technology that allows us to better understand how people travel within cities around the world and informs the company’s strategic decisions on vehicle design and go-to-market strategy.

Previously, I worked as an Operations Research Scientist in the Data Science team at Convoy. There, I worked on building efficiency into our network of shipments. I developed, deployed, and maintained machine learning and optimization algorithms in production, and I provided quantitative insights to inform product decisions. A high-level description of the problem my team is solving for can be found here.

I received my Ph.D. from the Department of Energy Resources Engineering at Stanford University. My advisor was Prof. Adam Brandt and I was a Wells Family Stanford Graduate Fellow.

In my Ph.D. research, I focused on applying state-of-the-art computational tools at the intersection of machine learning and optimization to energy systems problems. As an example, I have worked extensively on the development of new algorithms and applications of time-series aggregation for infrastructure planning and operations. Out of my research, two open-source software packages have emerged: TimeSeriesClustering implements unsupervised learning methods for time-series data, and CapacityExpansion provides an extensible, data-driven infrastructure planning tool for energy systems.

During my studies, I interned at the battery software company Doosan GridTech; in the renewable energy forecasting division of Vaisala (formerly 3Tier); in the market optimization group at RWE Power, one of Europe’s largest utility companies; and at ThyssenKrupp, one of the world’s largest steel producers.