Hi! I'm Jenna Epstein.

My team developed a predictive model for forecasting pavement conditions in El Paso, and built a complementary tool to assist the Capital Improvements Department prioritize road repair projects.

I explored hurricane vulnerability in NYC through the lenses of population density, social vulnerability, and hurricane evacuation center accessibility.

My team used remote sensing and deep learning techniques develop a classification model to identify crops in Rwanda, with a focus on staple crops (legumes, maize, and bananas).

My team created a linear wildfire risk prediction model incorporating environmental and spatial risk factors. We also developed an interactive web application for evacuation kit distribution, combining Census tract-level risk and the CDC's Social Vulnerabilty Index.

My team developed a machine learning algorithm to predict which areas of a city are at the highest risk of flood inundation. This model was developed and iteratively tested to achieve optimal accuracy in Calgary, and applied to Denver to assess its generalizability.

I co-authored an interactive report by the Housing Initiative at Penn exploring the impacts of a national universal housing voucher across multiple dimensions. I was responsible for data cleaning and all mapping work.

In my graduate studio course, I created maps showing existing site conditions, executed flood simulations, and collaborated on a proposal focused on best practices in stormwater management and green infrastructure, adaptation, and environmental and social resilience.