2021 Fall Capstone Projects

The CUSP Capstone Program is the crux of our graduate students’ hands-on experience. These projects approach real-world challenges through problem identification and scoping, data collection, and applying data analytics and visualization techniques. Deliverables can include urban data analytic reports, data visualizations including interactive applications, research websites, research publications, and prescriptive policy solutions.    

At the end of each year’s program, we host a Capstone Showcase where student teams present their work and celebrate their accomplishments. Did you miss the event? Check out the recording below! 

The Impact of Urban Agriculture on Food Chain Resiliency and Food Equity in New York City

Sponsor: Chet Van Wert and Alice Reznickova, NYU Stern Center for Sustainable Business 

CUSP Mentors: Wythe Marschall, Alice Reznickova

CUSP Students: Nico Ampuero, Jeremy Rucker, Xiaolin Li

Project Website: https://ampu3ro.wixsite.com/mapnyc

Covid-19 is driving interest in increasing food production within NYC. As new urban agriculture businesses adopt and innovate technologies for growing fresh food commercially in NYC, what can be learned from the many hundreds of community gardens, school gardens, gardens in land trusts, and gardens at public housing developments that already exist? Can locally grown food improve the resiliency and accessibility of fresh food for New Yorkers? This project seeks to map existing and planned agricultural production in the city, and to identify urban farming hotspots, distribution gaps, and neighborhood impacts, contributing to NYC’s food resiliency plans.

Bridging Graph Neural Network and Reinforcement Learning for Autonomous Drone Swarm Exploration

Sponsor: Giuseppe Loianno, NYU CUSP and David Mordecai, RiskEcon Lab for Decision Metrics

CUSP Mentor: Giuseppe Loianno, David Mordecai

CUSP Students: Daisuke Nakanishi, Gurpreet Singh, Kshitij Chandana

Project Website: https://1312gurpreet.github.io/droneswarm/index.html

Drone technology has been rapidly advancing in recent years mostly due to improvements in constrained computational capabilities and algorithmic development. Drone swarms have significant advantages over using an individual drone as the swarm would ideally be more robust to failures, flexible and scalable. Drone swarms also have significant application in the field of mapping, inspection, surveillance etc. In this project, we would like to achieve the task of optimal coverage (mapping) of an environment while performing obstacle avoidance with adaptive motion planning using autonomous drone swarms.

Internet-of-Things Security and Privacy

Sponsor: Danny Yuxing Huang, NYU mLab, NYU CUSP

CUSP Mentors: Danny Yuxing Huang

CUSP Students: Anbo Guo, Claudia Gomez Palacios, Letao Hou, Rachel Liu, Smriti Mohta

Project Website: https://lh3163.wixsite.com/website

Smart home IoT (Internet-of-Things) devices are gaining popularity in average consumer homes. These “smart” devices, such as cameras, plugs, TVs, dishwashers, etc, are also known to pose various security and privacy threats (e.g., your Alexa listening to you), but the opaque nature of these devices makes it difficult to discover security and privacy vulnerabilities. In this project, we plan to systematically discover Internet-of-Things (IoT) security and privacy issues from a data-driven perspective and develop mitigations from both a technical and policy perspective. We will build upon existing work from IoT Inspector (https://iotinspector.org/), analyze existing datasets, and/or extend IoT Inspector to gather more data-driven insights. At the end of this project, we will provide the public with either an improve tool or report to keep consumers informed and protected.

New York City Rezoning Evaluations

Sponsor: Lucien Wilson and Brandon Pachuca, Kohn Pedersen Fox

CUSP Mentor: Claudio Silva

CUSP Students: Jin Li, Qian Dong, Jaehee Kim, Karen Worthing, José Ramón Romero Pineda

Project Website: https://jljuli.github.io/nyc-rezoning/Web.html

This project’s objective is to promote optimal urban land use is critical to help neighborhoods thrive and make cities sustainable. While market forces encourage the most economic use of land, zoning serves as a check to assure the community is best served. Rezoning may be undertaken to create economic development, accommodate population growth, or respond to neighborhood needs and preferences. This study aims to determine what has caused results to deviate from intended goals in past rezonings to better anticipate and mitigate deviations in future rezonings. The project’s theoretical value is to understand how various land uses (e.g., residential, commercial) complement and compete with one another. The social value reflects the rezoned community’s needs. Data was collected based on interviews with residential and commercial real estate developers who explained what drives the economics of developing an urban parcel, and includes demographic, land use, and neighborhood quality data. The data was loaded into a supervised machine learning model in which the dependent variable was land use. In addition, a second model was developed to predict future built floor area ratio (FAR) by land use. We developed a visualization tool which could be used to understand current trends and anticipate future needs of neighborhoods.

Next Subway

Sponsor: Lucius J. Riccio, NYU CUSP

CUSP Mentor: Lucius J. Riccio, Kaan Ozbay

CUSP Students: Nicole Allegretti, Wenting Chen, Zhuoqi Niu

One of the keys to New York’s rise to being a world class city is its extensive subway system. As the 21st Century progresses, the expansion of that system may be the key to keeping it competitive with the other great cities in the world. New York likely needs a new subway line each decade for the next 100 years just to keep pace. Shanghai is doubling its system. Others are extending theirs. What plans does New York have? This report is an effort to design a study to result in an optimal strategy for expanding the system.

Studying the Relationships Between Food Acquisition and Health Outcomes During COVID-19

Sponsor: Huy Vo, NYU CUSP and CUNY Graduate School of Public Health & Health Policy

CUSP Mentor: Huy Vo

CUSP Students: Chris Carey, Maia Guo, Nuoyi Wang

Project Website: https://chriscarey.tech/nyu/capstone/

The COVID-19 pandemic, and efforts to contain its spread through business closures, indoor dining, work from home practices, affected our food system in numerous ways. Not only that the reduced availability of prepared meals and groceries made acquiring food more challenging, it also changed food shopping and cooking practices. These changes, however, varied by household profiles, and over time depending on policies (e.g., school openings) and the level of risk. In this project, we would like to study the relationships between food acquisition and health outcomes during the pandemic through a longitudinal study on human mobility at major U.S. cities.

COVID-19 Closure Impacts on Small Businesses and Their Surrounding Communities

Sponsor: Debra Laefer, NYU Urban Modeling, NYU CUSP

CUSP Mentors: Debra Laefer

CUSP Students: Ari Lewenstein, Shu Wang, Haochen Xing

Project Website: https://capstone1121.wixsite.com/capstone

Multi-week PAUSE orders are unprecedented in the US. Driven by public health concerns, the long-term impacts of these actions on small business are not yet fully know. Anecdotally, we are already seeing a high level of permanent closures especially of independently owned and operated businesses. This project will look at business health (Spring 2019-Spring 2021) with respect to individual business survival and impacts on the surrounding communities. Pre and post-COVID services and goods distributions will be considered in the context of new business closures (both Summer 2020 and Spring 2021) for a range of socioeconomically diverse NYC communities.