Laura Greenstreet

New York City buses are slow and unreliable. While transit systems collect large volumes of data on vehicles and riders, there is a gap in using this data to inform operations. As a Siegel PiTech Fellow, I developed a digital twin using MTA Open Data that can compare a variety of interventions to understand design effective interventions and understand what factors drive performance. This summer I worked with the MTA to test the framework on internal data, finding that targeting bus spacing is often more effective than faster speeds or boardings.

Fishery income is highly variable, where low income years can heavily impact small communities and result in millions of dollars of government support. Diversification in multiple fisheries can reduce income variability, but fisherman may not diversify due to financial costs, difficultly obtaining new skills, or uncertainty about future risk. Working with fisheries ecologist Suresh Sethi, I developed an optimization framework to explore the relative impact of interventions targeted at each challenge. The optimization found there is potential to reduce income variance across the system by over 50% and significant benefits to reducing barriers to obtaining new skills.

Aquaculture, or the farming of aquatic organisms including fish and shellfish, has the potential to create economic growth with lower land-use, freshwater use, and carbon emissions than traditional livestock. Aquaculture is growing rapidly in the Amazon. However, the location, extent, and life-cycle of operations are poorly understood. Working with Dr. Carla Gomes, I helped develop deep learning methods to detect aquaculture ponds from satellite data, using temporal information, attention, and contrastive learning to deal with issues including label imbalance, label bias, and generalization to new regions. The model was used by to understand land use change and associated carbon emissions in the Brazilian Amazon.