National Science Foundation Rapid Grant Research | NYU Tandon School of Engineering

National Science Foundation Rapid Grant Research

illustration showing figures distancing themselves

Human interaction in a time of pandemic

Computer Science and Engineering Department
Center for Urban Science and Progress

Understanding how people move around  and interact with each other in the public sphere became more vital than ever when the COVID-19 pandemic hit. Torrens, whose research employs motion capture technology, is now exploring how new forms of spatial behavior emerge and testing how existing theories of spatial behavior hold under extraordinary circumstances. Torrens is fine-tuning emergent relationships among citizen behaviours and regulations — work that could inform policy in this and future public-health crises. As we navigate a new normal, human interaction will continue to help define us as a society, and Torrens is adding clarity to that definition.

Intelligent, wearable telehealth devices

Department of Electrical and Computer Engineering
Department of Mechanical and Aerospace Engineering

Department of Electrical and Computer Engineering
Department of Biomedical Engineering

In response to the pressing need for smart and scalable wearable technologies that could be produced rapidly to assist in monitoring COVID-19 patients, Atashzar and Wang developed a wireless smart IoMT (Internet of Medical Things) necklace containing sensors that can accurately, objectively, and continuously track multiple vital symptoms of respiratory malfunction and infection, thus covering a large spectrum of COVID-19 symptoms, and predicting the probability of health anomalies through machine intelligence and data modeling.

The device can be used in both home and hospital settings, and by means of novel machine learning models, the data it produces can aid practitioners in detecting early health anomalies and in predicting potential adverse events.

Resilient infrastructure systems

Department of Electrical and Computer Engineering
Center for Urban Science and Progress

When many cities began enforcing shelter-in-place policies in order to slow the pandemic, Dvorkin, who heads the Smart Energy Research (SEARCH) Group, part of the Department of Electrical and Computer Engineering’s Power Lab, knew it was likely to have an effect on vital physical infrastructure systems such as gas, electricity, water, and transportation. Given the looming danger that unusual demand could lead to outages and inability (or limited ability) to serve sheltered population groups, he designed a model that can represent infrastructure operations under various disease-outbreak scenarios and inform the development of efficient strategies to mitigate these vulnerabilities. His project, which bridges the gap between computational epidemiology and infrastructure modeling, also has applications in the fields of climate resiliency and environmental sciences and public health and response preparedness.

Leveraging mobility data

Department of Civil and Urban Engineering
Center for Urban Science and Progress

How effective is social distancing? Will some groups be more affected by the impacts of shelter-in-place mandates and the disease itself than others? Those questions swirled as the extent of the pandemic was becoming evident, and Kontakosta quickly marshalled anonymized smartphone location data from millions of users in New York City to study “exposure density” — a dynamic measure of neighborhood activity levels — thus allowing public health officials to estimate the likelihood of successful containment efforts for specific localities and predict where future localized outbreaks and chains of transmission could emerge.

3-D mapping hot spots

Department of Civil and Urban Engineering
Center for Urban Science and Progress

When the pandemic hit, Laefer immediately sent researchers into the field to observe potential hot spots outside hospitals and mass transit hubs to record what people were touching — and thus the most likely surfaces to carry the coronavirus. (Virus mapping dates to 1854, when John Snow traced the source of a cholera epidemic in London to infected wells.) NYU teams, however, used geospatial data to pioneer a more accurate and effective tool for this virus: 3D mapping. Their data is available through NYU’s Spatial Data Repository. Their study set the groundwork for machine learning models to speed the analysis of how a virus spreads in airports, grocery stores, and playgrounds — anywhere large groups of people come, touch things, and leave.

New York's unique challenges

Department of Mechanical and Aerospace Engineering:
Department of Biomedical Engineering;
Department of Civil and Urban Engineering;
Center for Urban Science and Progress

Department of Electrical and Computer Engineering

Department of Mechanical and Aerospace Engineering
(Visiting Professor)

At the start of the pandemic, researchers in New York were stymied by various factors: models from other contagions are not applicable to the novel coronavirus because they are confounded by its absence of symptoms in early stages, New York’s complex mobility patterns, and limited testing resources.

Using data from New Rochelle — the New York suburb first seriously afflicted by the virus — a team from NYU Tandon built a mathematical model specifically for the city’s unique social and transportation structures. Their goal: to help health and government leaders make smart, up-to-date testing and contact-tracing decisions.

In a city where few own cars for drive-through assessment, and testing is often conducted at hospitals already strained by the virus, they provided quick, real-time scientific insights. They factored in the type and timing of testing, asymptomatic occurrence, and hospitalization stages, making that information freely available to the public.

Shedding light on panic buying

Department of Electrical and Computer Engineering;
Center for Urban Science and Progress

Zhu understands that panic is a natural human behavior, but pair a propensity for panicked hoarding with the human predilection for sharing on social media, he knew, and a vicious cycle occurs: frantic buying drives Facebook and Twitter posts about shortages (real or perceived) beyond local markets, leading to further panic buying. That consumer behavior was likely to result in overstocking, shortage of essential products, and price hikes.

He and his team collected and analyzed data relating to consumer buying patterns from a multitude of online and offline sources and identified relationships between behaviors with the help of statistical techniques. They successfully predicted price hike points for such items as masks, sanitizers, and disinfectants during the pandemic, findings that are used to inform price policies and supply regulation of critical products to prevent hoarding-related shortages.

empty grocery store shelves