Laura Greenstreet

Buses in New York City are slow and unreliable. Through a Cornell Siegel PiTech fellowship in collaboration with the non-profit Riders Alliance, I developed a simulation framework to discover what factors drive long rider wait and trip times and compare five proposed improvements. While the project is ongoing, simulations and optimization have shown that reducing delays at the start of routes has more potential than time point based strategies, even with live data.

Learn more about the project in this blog post.

Fishery income is highly variable, where low income years can heavily impact small communities and result in millions of dollars of federal support. Diversification in multiple fisheries can reduce income variability, but fisherman may not diversify due to financial costs, difficultly obtaining necessary 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 or cultivation 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 medium resolution 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.