AMARI WYKING GARRETT
I'm a software engineer with a strong focus on data-driven systems and urban technology. My background spans full-stack development, mobile applications, and machine learning, shaped by industry experience at leading tech companies and graduate research at the NYU Center for Urban Science + Progress.
I bring a product-oriented mindset to technical challenges and hold a particular interest in learning how to build scalable tools that support complex systems. Whether it be digital products or researching urban problems, I love digging into problems that sit at the edge of software, data, and the real world.
A world where cities thrive without compromising our planet's future.
To support the deployment of data-centric technologies that bolster the sustainability of urban life.
Analyzes the societal impacts of climate-induced migration, using Erie County as a case study to examine population forecasts and their impact on urban socioeconomic indicators like employment, education, and housing, providing insights for policy development and urban preparedness strategies.
Explores the effectiveness of pre-trained transformer models and large language models in optimizing the selection of job candidates and quantifying the importance of various skills, comparing their performance to human evaluators.
Evaluates the effectiveness and environmental consequences of rapid grocery delivery services like Blinkit, focusing on capacitated vehicle routing to optimize deliveries from 'dark stores' in urban areas for environmental savings.
Reconstructs an electric vehicle charging network from session data using Graphical Lasso and develops a dashboard for analyzing charger usage and informing deployment strategies.
Explores the relationship between hyperlocal air quality, pedestrian mobility, and public health, aiming to enable citizens to avoid air pollution during non-motorized commutes.
Investigates the effectiveness of vulnerability indexes (National Risk Index and Social Vulnerability Index) for flood preparedness in New York City, particularly in the context of equity, by comparing them with socioeconomic data and NYC 311 flood complaints.
Analyzes the solar generation potential of the Southwest Bronx Con Edison power district using GIS data from OpenStreetMap and USGS LiDAR, calculating Sky View Factor and total building footprint area to assess solar power contributions to district demand.
Idaho National Laboratory
Software Engineering Intern
May 2024 - August 2024
Technologies & Skills
Idaho National Laboratory
Data Science Intern
May 2023 - August 2023
Technologies & Skills
Apple
Software Engineering Intern
May 2022 - August 2022
Technologies & Skills
Meta
Software Engineering Intern
May 2021 - August 2021
Technologies & Skills
Meta
Software Engineering Intern
May 2020 - August 2020
Technologies & Skills
Microsoft
Software Engineering Intern
June 2018 - August 2018
Technologies & Skills