About Me
I’m a PhD student at Cornell co-advised by David Shmoys and Andrea Lodi. My research focuses on developing AI and optimization methods with applications in transportation and resource management. Previously, I worked with Carla Gomes to develop methods to track aquaculture development from satellite imagery, with Claire Kremen to scale an analysis of functional connectivity to the global level, and with Geoffrey Schiebinger on optimization methods for single-cell genomics.
Projects
Improving Bus Performance in New York City
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.
Learn more about the project in this blog post.
Learn more about the project in this blog post.
Optimization for Fisheries Management
Fishery income is highly variable, where low income years can heavily impact small communities and result in millions of dollars of fedral 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.
Mapping Aquaculture in the Amazon
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.
Towards sustainable aquaculture in the Amazon
Nature Sustainability, 2025.
Detecting Aquaculture with Deep Learning in a Low-Data Setting
SigKDD, Fragile Earth Workshop, 2023.