EAGER: SAI: Social Wayfinding-Inspired InFrasTructure (SWIIFT) for the Design of Public Spaces
Funding Agency: National Science Foundation
PI: Jacob Feldman (Rutgers University)
Co-PIs: Mubbasir Kapadia (Rutgers University), Fred Roberts (Rutgers University), Mathew Schwartz (New Jersey Institute of Technology), and Karin Stromswold (Rutgers University)

In 2020, many public spaces were hastily redesigned to optimize pedestrian flow in order to minimize the spread of COVID-19. Unfortunately, conventional methods for simulating how people move through public spaces don’t take into account social factors that affect how people actually navigate in the presence of other people (social wayfinding). For example, these methods don’t incorporate how people move to avoid others’ personal space and navigate around slower-moving people, or how they follow instructions from other people. Even worse, existing simulations usually assume everybody has identical abilities. The goal of this project is to develop a comprehensive system for simulating the flow of people through public spaces, including realistic models that incorporate people with a variety of abilities and disabilities. The result will be more realistic and informative simulations of pedestrian flow through public spaces. This will make it possible to modify public infrastructure in a way that is more efficient, more cost-effective, and more accessible to everyone.

To accomplish this goal, this project uses a Social Wayfinding-Inspired InFrasTructure (SWIIFT) design framework. The framework has three interlocking parts: human subjects experiments on human wayfinding, computational simulations of the flow of people through public spaces, and evaluation metrics for assessing design and re-design of real public spaces. In the experiments, human subjects will be immersed via Virtual Reality headsets into simulated spaces. These spaces will contain different numbers of simulated people, including people with variations in mobility (using wheelchairs, canes or walkers; pushing strollers; carrying heavy bags), sensory ability (e.g., visual impairments, hearing impairments), knowledge, and attention. Human subjects will receive different cues about which way to go, including visible pathways, signage, and verbal instructions. Data about the choices they make as they navigate through the virtual spaces will be incorporated into simulations, allowing us to develop realistic models of how people flow through spaces under natural conditions. Finally, the SWIIFT framework will use these simulation models to evaluate potential modifications to real spaces, allowing potentially expensive changes to be accurately evaluated before they are carried out. The ultimate goal of this work is to enable public spaces to be made more efficient and more accessible for everyone, regardless of ability.

RAPID: Countering Language Biases in COVID-19 Search Auto-Completes
Funding Agency: National Science Foundation
PI: Vivek Singh (SCI)
Co-PI: Pamela Valera (SPH)

The novel coronavirus (COVID-19) has resulted in sharp increases in online search activity about the disease, its spread, and remedial actions. Hence, search engines can significantly influence public perceptions of the disease and the actions undertaken by the public. If there are language biases in the results of searches, there may also be biases in perceptions and actions taken. This project will systematically analyze the differences in COVID-19 related search auto-completes that are provided to English and Spanish speakers. The results will generate new knowledge on the emergence of algorithmic bias and help ensure equity in health information dissemination at scale amid large-scale health emergencies. The findings will be shared in easy-to-understand terms on an urgent basis in multiple languages to help ensure equal access to health information in the COVID-19 pandemic. This feedback could help improve the health outcomes for numerous individuals facing the COVID-19 pandemic.

RAPID: Privacy-Preserving Crowdsensing of COVID-19 and its Sociological and Epidemiological Implications
Funding Agency: National Science Foundation
PI: Jaideep Vaidya (RBS)
Co-PIs: Vivek Singh (SCI), Periklis Papakonstantinou (RBS), Stephanie Shiau (SPH)

The successful containment of pandemics such as COVID-19 requires the ability to record the presence of infections and track its spread within communities. While testing is the primary source to collect such information, the lack of testing resources and the resultant under-testing significantly hampers this effort. Mobile crowdsensing is an alternative technological approach that can be effective in such situations if used by a significant fraction of the population. However, privacy concerns as well as the stigma associated with the pandemic prove to be huge barriers that inhibit the accurate collection of information in this way. The goal of this project is to develop an infrastructure and platform to collect data from the population and distill it into aggregate information to provide insight to both users and policymakers while protecting privacy. The project also aims to gain a broader understanding of privacy and decision making in extreme situations and learn how humans value their privacy and the choices they make in such situations. The project will enable the collection of real-time data, which is not available otherwise, and will enable a more effective response to the COVID-19 pandemic. The increased dissemination of localized information to users can help encourage social distancing from a psychological perspective and thus contribute to the well-being of individuals in society. The improved understanding of privacy from a socio-cognitive perspective to be gained from this project will improve the quality of data privacy solutions that are developed in the future.

Artificial Intelligence (AI), Pandemics, and Mental Health
Funding Agency: Rutgers Center For COVID-19 Response and Pandemic Preparedness Social Science Grants
PI: Vivek Singh (SCI)
Co-PIs: Vincent Silenzio (SPH), Sara Pixley (Cognitive Science), Fred Roberts (DIMACS), David Pennock (DIMACS), Kostas Bekris (CS), Roseanne Dobkin (Psychology)

With the recent pandemic, new physical and emotional stressors have emerged, creating unexpected challenges for the average family. Many behavioral patterns have been disrupted and an ever-larger proportion of human behavior is mediated by digital technology. We posit that the combination of digital traces,
AI techniques, and self-reported surveys conducted longitudinally over pandemic (and potentially post pandemic) scenarios can yield fundamental new insights into mental health assessment. Over a 12-week period, we will longitudinally track mental health, social outcomes, and digital trace data (phone logs, social media, search logs) of individuals to better understand the interconnections between mental health and digital behavior traces. Specifically, the temporal relationship (e.g., precede, co-occur, or follow) between changes in behavior and changes in mental health will be evaluated using AI techniques for better assessment of mental health, which will open doors for timely interventions in the future. The project will lay the foundations for a transformational Rutgers effort that combines AI techniques, social theories, and clinical insights for creating AI enhanced approaches to address mental health and prevent a crisis during a pandemic and in its aftermath.

COE Initiative on COVID-19 Supply Chain
Funding Agency: Department of Homeland Security
PI: Fred Roberts (CCICADA and DIMACS)
Co-PI: Dennis Egan (CCICADA and DIMACS)

Healthy supply chains remain invisible as we go about daily life. But, behind the scenes, they assure reliable flow of the raw materials, components, and end products that are essential for the economic security of our country, the smooth functioning of government, and the well-being of society as a whole. When disaster strikes, supply chains can be disrupted, placing vulnerable populations at risk. The coronavirus pandemic has disrupted supply chains worldwide and revealed vulnerabilities on a scale never before witnessed. From shortages of personal protective equipment to delays in obtaining replacement parts, we are seeing firsthand the fragility of global supply chains. Making supply chains more resilient will help us recover from the current pandemic and help us prepare for future disasters.

Because problems of supply chains are of such importance to the Department of Homeland Security (DHS), the DHS University Centers of Excellence (COEs) have a significant role to play. In this context, a COE COVID-19 Supply Chain Initiative has been established, under the leadership of the Command, Control, and Interoperability Center for Advanced Data Analysis (CCICADA), directed by the PI. This COE Initiative aims to: 1) identify tools and technologies that might be of help with the current pandemic; 2) identify key lessons learned from the current situation that will better prepare our supply chains for future disasters; and 3) identify areas where new tools and technologies can help us be better prepared for future disasters. It aims to take advantage of the varied skills and tools of the full network of COEs to develop collaborative efforts in the future, partnered with government agencies and the private sector.

This initiative is focusing on the supply chain for Medicines, Vaccines, and PPEs; the supply chain for Food; the supply chain for Labor during the pandemic; and the Enhanced Crime Affecting the Supply Chain during the Pandemic.

RAPID: NSF-BSF: Analysis of the Spreading Patterns and the Efficiency of Quarantine Measures for COVID-19, based on available world-wide spatio-temporal data
Funding Agency: National Science Foundation
PI: Lazaros Gallos (DIMACS)

The recent COVID-19 pandemic has created an urgent need to understand the spread of coronavirus (SARS-CoV-2). The special features of this virus and the unprecedented global response present potentially novel paths of disease transmission that have not been observed in modern times. These paths create continuously evolving spatial and temporal patterns of observable properties, such as number of infections or virus-related deaths. Using tools from statistical physics and network science, this project seeks to understand the special features of this complex behavior and to compare these patterns in different geographical areas. The availability of high-resolution data will enable the construction of computational models which will attempt to explain these patterns in terms of quarantine measures, timing of such measures, and environmental conditions. Such an understanding could assess the efficiency of quarantine measures implemented around the world and also identify the effects of temperature and humidity. This project aims to make these mathematical and computational tools be readily available at a next possible outbreak wave and to allow immediate analysis of epidemiological data. The outcome of this research has the potential of providing early detection of geographical areas in high risk, informing thus the decisions of policy makers. This project also provides significant opportunities for the training of STEM researchers.

The research will focus on the scaling and correlation behavior of epidemic characteristics between different areas. The scaling analysis will study how the observed quantities change for different scales of time and space, characterized by power-law exponents. Different exponents indicate different propagation patterns which can provide information about the effectiveness of location and timing of tests and enforcement of quarantine measures. Similarly, the extent of spatial correlations and their time evolution are key indicators of spreading patterns. The analysis of the correlation lengths and correlation times can indicate how close the virus spreading process is to criticality. This is particularly important because critical systems are in general much more vulnerable to rapid spreading. The data analysis will be complemented by appropriate mathematical and simulation epidemic models to test the influence of population and environmental factors on the empirical data.

NRT-FW-HTF: Socially Cognizant Robotics for a Technology Enhanced Society (SOCRATES)
Funding Agency: National Science Foundation (NSF)
PI: Kristin Dana kristin.dana@rutgers.edu
Co-PIs: Clinton Andrews (Co-Principal Investigator), Kostas Bekris (Co-Principal Investigator), Jacob Feldman (Co-Principal Investigator), Jingang Yi (Co-Principal Investigator)

Ubiquitous robot assistants that improve the quality of life remain mostly a vision. A key challenge of the program is Robotics for Everyday Augmented Living (REAL), i.e., semi-automated systems that focus on tasks and work that impact human life including in the context of the recent pandemic. To make this vision a reality, important considerations include safety, adaptability to human desires, and nuanced societal impacts, such as dignity, consent, privacy, and fairness. Traditional social sciences often study the effects of technology on individuals and society only after it is deployed. Given the potential impact of robotics as well as the potential for unintended negative social consequences, technology should adapt to humans rather than the other way around. Current robotics training does not equip researchers with the interdisciplinary tools necessary to address this challenge. This National Science Foundation Research Traineeship (NRT) award to Rutgers University will train a new type of professional, the socially cognizant roboticist, with the skills in technology, social science, and public policy needed to bridge this gap. This training program aims to instill an awareness of human involvement into every phase of the design of new technology, so that these technologies can provide positive human value wherever they are introduced.