# data[].attributes.title
CAREER: Memory-Efficient, Heterogeneity-Aware and Robust Architecture for Federated Intelligence on Edge Devices

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Computer and Information Science and Engineering (CISE)

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The recent trend of migrating computation from the centralized cloud to distributed edge devices is reshaping the landscape of today’s Internet, especially under the unprecedented challenges of the COVID19 pandemic. With privacy being a critical concern in data aggregation, Federated Learning emerges as a promising solution to such privacy-utility challenge. It pushes the computation towards consumer’s edge devices, where the data is generated. By exchanging statistical information, the participants perform collaborative learning in a distributed fashion. Unfortunately, the original design still faces new system-architectural challenges from limited memory, software/hardware heterogeneity, security and statistical diversity from different edge devices. The overarching goal of this CAREER project is to design, optimize and implement a memory-efficient, heterogeneity-aware and robust architecture for federated learning on consumer’s edge devices. In particular, it aims to: 1) remove the memory barriers of running the computational-intensive learning tasks; 2) resolve the software and hardware heterogeneity among various kinds of devices; 3) secure the information exchange and the machine learning backend. The research will provide a stack of solutions to address the urging needs in realizing collaborative intelligence on edge devices with computation/memory/energy-efficiencies, security and robustness. This research will address an urgent problem to bridge the gap between the vast data available from consumer’s edge devices and the rising interest of utilizing such private data to improve our wellbeing. The algorithms and tools developed in this CAREER project will lay the foundations to a plethora of new applications on massively distributed edge devices, as the essential elements for building a smart, connected and resilient community. The CAREER program will advance STEM education by developing new educational components related to machine learning, edge computing and security. This includes diverse outreach plans of cybersecurity summer camps, junior research symposium, high school instructor mentorship, coding competitions and the inclusion of underrepresented minority and women engineers. The potential use cases will be also explored with the collaborating industrial partners to enrich their business models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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DDRIG in DRMS: An Investigation of Harm Perceptions from Communications on Social Media about COVID Vaccines

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During health crises such as COVID-19, dissemination of health information on social media that is not backed by science has been shown to result in serious harms to communities. These harms include both physical harms (such as losses of life, long- and short-term health harms, or financial harms) and psychological harms (such as loss of trusts on others, emotional sufferings, or confusion). These negative effects can lead to wrong decisions such as drinking bleach to cure the viral infection. In investigating this type of harm, this proposal focuses on two specific research questions: (1) How can the perceptions of harm from information not backed by science be characterized in health crises? (2) How are harm perceptions associated with social media conversations in a health crisis? Results have the potential to advance national health and welfare. Official health organizations such as the CDC and WHO, may gain a better understanding of vulnerable population’s threat and need perceptions. An understanding of people’s perceptions of harmful inaccurate information ultimately helps improve the reliability of social media channels. Broader impacts include the support of a doctoral dissertation, dissemination of findings at professional outlets, and teaching modules related to this research project are part of a course at University of Texas at San Antonio, a Hispanic Serving Institution (HSI).Despite prior efforts handling harms from inaccurate information in both COVID-19 pandemic and COVID-19 vaccine contexts, to our knowledge, there is no research that has characterized harm perceptions from such information in health crises. This project takes a sharp focus on perceived harms, rather than on actual harms, and examines the interplay between perceived harms and actual harms. Focusing on the COVID-19 vaccine context, studies examine several COVID-19 vaccine scenarios containing inaccurate information, and characterize harm from that information using three characteristics, i.e., unknown and dread and manageability. Using text mining the project examines the association between harm perceptions and nature of communication in Twitterverse. This is an early attempt at quantitatively measure the harm that results from inaccurate information in the pandemic context. The project advances knowledge in an under-studied area and use conversations in Twitterverse to investigate the nature of communication with respect to harm characteristics. This research bridges social media data with communication theory to unpack the role of social media around crisis communication.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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RAPID: Science Teachers as Public Health Educators: How Has the COVID-19 Pandemic Reshaped the Roles and Experiences of K-12 Science Teachers?

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When a global health crisis emerges, students at all levels turn to their science teachers for information and, at times, reassurance, according to researchers at Horizon Research, Inc. (HRI). Science teachers serve a critically important public health function and become an important part of the nation’s response efforts. Since 2020, tens of thousands of science teachers in the United States have helped students grapple with the reality of SARS-COV-2 and COVID-19. This includes not only supporting the emergent scientific learning opportunities, but also functioning as public health educators and advocates in the midst of the global pandemic. HRI explored the early role of science teachers during the pandemic with a prior RAPID project, which demonstrated that science teachers were engaged in a wide variety of educational activities with students related to the pandemic and public health education. However, now nearly two years into the global pandemic, there are a number of new topics that have arisen to explore with respect to science education, including vaccines and long-term immunity that were not able to be assessed early in the pandemic. This study will further the field's understanding of the role that science teachers play in adapting their instruction during a public health crisis, how they address emergent ideas throughout the unfolding of the pandemic, and the impacts that the pandemic has had on science teachers themselves. The research will build on the prior RAPID award (2027397) and return to the sample of science teachers studied in that context. HRI will collect survey data to explore the following questions: (1) How does the pandemic continue to influence teachers’ science instruction (for example, instructional time, instructional strategies), and how has that influence shifted? (2) How has teaching about COVID evolved? What new topics (for example, vaccines) have they taken up in thecontext of COVID? (3) What factors now exert the greatest influence on science teachers’ teaching about COVID, and how do those differ from the factors at play in the spring of 2020? (4) What are the impacts of the pandemic on science teachers themselves, including manageability of workload, opportunities for professional growth/development, physical/mental wellness, and job satisfaction?  Using the Theory of Planned Behavior, the survey will be constructed to identify factors that predict whether teachers take up the topic. The survey will also collect data about how teachers address the virus and its transmission with their students. HRI will disaggregate survey data by school-, class-, student-, and teacher-level variables to identify patterns in student opportunities. Survey data will be supplemented by interviews with 50 survey respondents to gather more in-depth information related to the constructs of interest. Study findings will be immediately shared through a preliminary report that focuses on the survey data; presentations and dissemination through practitioner and research organizations; and policy briefs to the CDC and NIH to acknowledge the role of science teachers as public health advocates and encouraging those agencies to make resources available to science teachers to support this role.This RAPID award is made by the DRK-12 program in the Division of Research on Learning. The Discovery Research PreK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics by preK-12 students and teachers, through the research and development of new innovations and approaches. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for the projects.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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RAPID: Measuring Uptake, Persistence, and Impact of Behavioral Interventions On Respiratory Viruses

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This study investigates the reasons for differences in disease transmission between populations as a result of behavioral changes chosen by individuals and separating those effects from top-down, mandated changes. Top-down behavioral interventions are used to reduce the transmission of pathogens when pharmaceutical treatments (e.g., drugs and vaccines) are not available. They are often designed to minimize movement and contacts and include school and workplace closures or event cancellations. In additionally, individuals may choose to temporarily change their behavior to reduce their risk of infection. Such individual changes are difficult to predict or measure, making it challenging to measure their effectiveness in reducing disease incidence and spread. The emergence of SARS-CoV-2 prompted widespread county-level restrictions alongside individual behavioral changes, such as staying at home and wearing masks. Despite experiencing the same county level restrictions, university students experienced very high rates of COVID-19 per capita, while non-students reported very few COVID-19 cases per capita. In contrast, neither population experienced seasonal influenza during the first winter of COVID-19. By comparing behaviors and disease incidence in these population, this research will help plan and design more effective and efficient outbreak management strategies for re-emerging or novel pathogens. This project supports training of early-career scientists. This study investigates the 1) uptake, 2) persistence over time, and 3) impact of top-down behavioral interventions and individual behavioral changes on COVID-19 and influenza incidence. By integrating passive and active data collection, researchers will quantify changes in behaviors in central Pennsylvania. Comparing behavioral changes during top-down interventions to other times helps disentangle mandated behavioral changes from individual choices. Longitudinal passive surveillance data will provide frequent measures of population-level movements before and after SARS-CoV-2 emerged. Traffic cameras will quantify vehicular and pedestrian traffic while mobile devices will provide proxy measures of total visits to points of interest, time spent outside the home, mixing between students and non-students, and so forth. This project also will longitudinally survey students (N=684) and non-students (N=1313) to measure individual adherence to top-down interventions and the voluntary adoption of other behavioral changes. Surveys will ask about movement, staying home, gatherings, masking, hand washing, vaccination, and so forth. At each time step, survey participants will provide blood for serological analyses to detect prior infection. Researchers will incorporate dynamic measures of behavioral interventions and individual behaviors into disease models to estimate the dynamics in the force of infection for COVID-19 and influenza in students and non-students. These models will measure the impacts of specific behaviors on disease transmission and will inform future interventions.This project was funded in collaboration with the CDC to support rapid-response research projects to further advance federal infectious disease modeling capabilities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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RAPID: A comparative online simulation tool for COVID-19 vaccine allocation

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This project will develop a mathematical model of SARS-CoV-2 transmission that takes into account the use of several types of vaccines, genetic variation in the virus, and multiple age groups. The COVID-19 pandemic has produced immense suffering throughout the world, with over 5 million deaths as of January 2022, but low- and middle-income countries have carried an increased burden both from the health crisis and from the economic crisis arising from the pandemic. While high-income countries have vaccinated the majority of their eligible populations, less than 2.3% of the population of low-income countries have been immunized. This is expected to change in the following months, as more vaccines will be available to these countries. As more vaccines become available to them, public health officials in these countries will likely develop vaccine allocation strategies based on a mix of vaccines (there are currently over 20 vaccines available worldwide) that differ in effectiveness against circulating variants, arriving at different times, and in different quantities. However, these countries usually lack resources to develop models to guide vaccine allocation. This project will develop a free, user-friendly online tool that will simulate country-specific multi-strain COVID-19 epidemics in conjunction with vaccination campaigns with multiple vaccines. By simulating the epidemic and producing key outcomes including expected numbers of deaths, hospitalizations, and cases, this tool will allow decision-makers in up to 177 countries around the world to compare scenarios of vaccine allocation.This project will develop a multi-strain, multi-vaccines mathematical model of SARS-CoV-2 transmission and vaccination that will then be used to develop an open-source, online, free, user-friendly comparative tool for vaccine allocation. This tool has the potential to to evaluate in-silico different vaccine allocations with country- or region-specific characteristics. The tool will allow users to input a variety of country-specific parameters including: the type, effectiveness and quantities (coverage, number of doses, distribution) of vaccines; co-circulating variants with different transmissibility, virulence, and cross-protection; social distancing interventions, demographics; and variable time horizons. It will simulate different vaccine allocation scenarios and project for each of them key outcomes (expected numbers of deaths, hospitalizations, and cases). This project was funded in collaboration with the CDC to support rapid-response research projects to further advance federal infectious disease modeling capabilities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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DDRIG in DRMS: Lay Understanding of Vaccine Efficacy

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Efficacy rates for COVID-19 vaccines have received major attention in scientific journal articles and news media outlets. Further, many sources emphasize the high efficacy rates for the three vaccines with FDA or FDA EUA status in the United States. For example, the mRNA-based Pfizer-BioNTech vaccine boasts an efficacy rate of 95% against the original strain of the COVID-19 virus, meaning that over the duration of a 3-month period in which 1% of the population without the vaccine contracted COVID-19, we could expect about 0.05% of vaccinated people to contract COVID-19 (95% lower). However, many lay people may not understand how vaccine efficacy rates are computed, and the 95% efficacy rate could be misunderstood to mean that 95% of vaccinated people will be protected from disease, implying that 5% of vaccinated people will become diseased with COVID-19. This project examines how lay people (such as patients) use health-related numerical information, sheds light on the basic cognitive processes underlying misuse of vaccine efficacy numbers and speaks to the broader question of how to present numerical health information in a way that lay people can understand and effectively use. The research identifies the degree to which different facets of cognitive processing (e.g., knowing how to use the mathematical formula, understanding the concept of relative risk reduction, and engaging in deliberation or comparison processes) act as facilitators to providing the normative response. Findings from the proposed research have the potential to foster informed medical decision making and patient agency. Understanding how lay people understand vaccine efficacy is highly relevant during the COVID-19 pandemic when conversations about vaccine efficacy receive increased attention and interest from patients.  Given the strong association between perceived vaccine efficacy and vaccination intention, it is critical to identify and correct misconceptions in patients’ understanding of vaccine efficacy statistics.The COVID-19 pandemic prompted widespread discussion of efficacy rates for COVID-19 vaccines in scientific journal articles and news media outlets, but little is known about how well lay people understand vaccine efficacy, which entails a fairly sophisticated mathematical calculation. This research tests the prediction that lay people are lured by a plausible and computationally simple but incorrect calculation: post-vaccination risk of infection = 1 - efficacy. For example, the lure response would be that after receiving a 95% effective vaccine a person has a 5% risk of getting infected. Previous medical decision-making research demonstrates that patients, especially those low in numeracy, often have difficulty understanding numerical information, which has a consequential impact on their health decisions and health outcomes. Moreover, prior cognition research provides evidence of quick, intuitive responses that may be analogous to the lure response in the vaccine efficacy context. This series of studies investigates the extent to which laypeople improperly interpret efficacy and whether this misunderstanding is associated with individual characteristics such as numeracy, cognitive reflection, and scientific literacy. First, hypothetical scenario experiments are employed to better understand how lay people utilize information about vaccine efficacy. Scenarios that vary information about fictitious vaccines illuminate the extent to which lay people improperly understand efficacy (for example, by answering that a vaccine that is 95% effective means that 5% of vaccinated people will get infected). Additional studies test a tutorial designed to improve viewers’ conceptual understanding of vaccine efficacy. Two versions of the tutorial differ in their emphasis on the mathematical formula vs. the concept of relative risk reduction, enabling a test of which of these facets is the major barrier to normative responses. Final studies examine a debiasing method that prompts participants to make an implicit comparison of disease risk between untreated and treated groups, testing the hypothesis that misunderstandings about vaccine efficacy stem from a failure to consider a comparison between a placebo group and a vaccinated group.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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CAREER: Designing a Multi-Scale Framework for Trait Variation in Epidemiological Dynamics

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Mathematical and Physical Sciences (MPS)

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Virginia Polytechnic Institute and State University

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Virginia Polytechnic Institute and State University

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The COVID-19 pandemic demonstrates the devastation that infectious diseases can leave on modern society. Deciphering the dynamics of disease is essential to earlier and more effective interventions to mitigate disease spread. To fully understand these dynamics, information across systems that operate on multiple spatial and time scales, as well as evolving via feedback, must be combined. This research project aims to develop a mathematical framework to combine and analyze such information to further the understanding of infectious disease spread, providing insight for ongoing and future disease outbreaks. The results are also intended to be applicable to multi-scale questions in ecology and immunology more generally. The educational component will serve to train a diverse generation of interdisciplinary scientists, building the capacity of the STEM workforce. Through focused short courses aimed at providing specific, relevant skills in quantitative techniques and cross-disciplinary communication, graduate students in the mathematical and life sciences will learn to collaborate and communicate effectively. Training and mentoring of undergraduate and graduate students will take place through focused research projects and opportunities to garner translational skills, for example developing and implementing project-based work and participating in outreach activities. Additionally, interactive presentations will engage middle school students through exposure to cutting-edge mathematical biology research.The current COVID-19 pandemic highlighted the importance of interpretable, quantitative models that link mechanisms with data while accounting for variability. However, infectious disease dynamics remain incompletely understood, in part due to the lack of heterogeneity considered in models of immunological, ecological, and epidemiological aspects. Complex, non-linear feedbacks arise from heterogeneities; thus, novel quantitative frameworks are needed to better understand and control infectious disease. This project builds on work in ecology involving integral projection models to develop a framework incorporating trait-based variation. This framework aims to describe development of immunity in a population and, thus, determine disease risk over time. Results will be useful for monitoring infectious disease, selecting interventions, and informing public policy. The educational component aims to strengthen quantitative literacy to help produce a more interdisciplinary workforce. A group of graduate students, across applied, mathematical, and computational disciplines, will be introduced to fundamental quantitative skills such as statistical analyses, model building, parameter estimation, data visualization, and coding. Participants will connect with this material using a project-based approach with examples related to infectious disease dynamics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Collaborative Research: CIF: Medium: Group testing for Real-Time Polymerase Chain Reactions: From Primer Selection to Amplification Curve Analysis

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Computer and Information Science and Engineering (CISE)

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Group testing is a screening technique that relies on careful combinatorial mixing and testing of batches of samples. By using group testing instead of individual testing, for most problem settings of practical interest, one is guaranteed significant savings in the number of tests performed and consequently, significant reductions in reporting delays and experimental costs. Group testing is especially desirable when monitoring the spread of infectious diseases such as Covid-19, which requires frequent examinations of massive populations. Although many ad-hoc approaches to group testing for infectious diseases have been put forward, little work has addressed the problem of end-to-end group-testing protocol design, which includes the selection of genetic regions for viral/bacterial identification, mathematical modeling and analysis of the test results and the development of guiding protocols for communal testing strategies. The overarching goals of the project are to determine which group-testing methods can actually mitigate the spread of Covid-19 and other diseases and to what extent, to estimate the reduction in the number of infected individuals achievable through the use of pooled real-time polymerase chain reaction (RT-PCR) tests, and to aid in the employment of Mobile Testing Units that can reach geographically remote regions. Other broader societal impacts include increased readiness for fighting future pandemics and training a new cohort of young researchers on interdisciplinary topics involving machine learning, coding theory and bioinformatics. The project aims to develop specialized machine-learning, combinatorial and information-theoretic methods for (a) identifying genomic regions with predictably low-mutation rates that may be used as amplification primers for gold-standard real-time polymerase chain reactions (RT-PCR) and determining best mixing strategies based on the likelihood of infection; (b) developing adequate models for amplification curves generated by RT-PCR and corresponding test-errors; (c) formulating experimental-protocol-specific non-adaptive and adaptive semiquantitative group testing schemes that account for nonbinary test outcomes; (d) addressing the testing issues associated with high-viral load subjects and heavy-hitter communities; and (e) integrating the mathematical techniques developed into an agent-based model for disease spreading and control in order to assess the potential impact of group testing and recommend effective test-quarantine-retest strategies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Collaborative Research: Conference: 2022 Secure and Trustworthy Cyberspace PI Meeting

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Computer and Information Science and Engineering (CISE)

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This award supports development of the program for a 2-day workshop to bring together PIs from across the Secure and Trustworthy Cyberspace (SaTC) program. Specific objectives of the PI meeting include:- to stimulate coordination and collaboration amongst SaTC PIs working on different projects;- to foster new collaborations between SaTC researchers and researchers in other disciplines;- to share experiences and learn from others' experiences in transitioning research into practice; and- to develop ideas and share methods for improving education, recruitment, and career development in cybersecurity.Building a strong community among SaTC researchers helps identify new research topics, avoid duplication of existing research, and improve educational opportunities for graduate students.Some elements of the meeting are structured so as to mitigate risks associated with the spread of COVID-19 or the virtual participation that its spread might necessitate.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

# data[].attributes.title
Collaborative Research: Conference: 2022 Secure and Trustworthy Cyberspace PI Meeting

# data[].attributes.funder_divisions[]
Computer and Information Science and Engineering (CISE)

# data[].attributes.abstract
This award supports development of the program for a 2-day workshop to bring together PIs from across the Secure and Trustworthy Cyberspace (SaTC) program. Specific objectives of the PI meeting include:- to stimulate coordination and collaboration amongst SaTC PIs working on different projects;- to foster new collaborations between SaTC researchers and researchers in other disciplines;- to share experiences and learn from others' experiences in transitioning research into practice; and- to develop ideas and share methods for improving education, recruitment, and career development in cybersecurity.Building a strong community among SaTC researchers helps identify new research topics, avoid duplication of existing research, and improve educational opportunities for graduate students.Some elements of the meeting are structured so as to mitigate risks associated with the spread of COVID-19 or the virtual participation that its spread might necessitate.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.