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EAGER: DCL: SaTC: Enabling Interdisciplinary Collaboration: Combatting Disinformation and Racial Bias: A Deep-Learning-Assisted Investigation of Temporal Dynamics of Disinformation

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Computer and Information Science and Engineering (CISE)

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This project explores the diffusion of racial disinformation online and its social impacts, particularly focusing on Asian Americans. While the hatred and bias against Asian Americans have become notable amid the COVID-19 pandemic, Asian-targeting disinformation has yet been fully explored. The project's novelties are in unique multidisciplinary approaches to (1) detect Asian-targeting disinformation and its countermeasure messages, and understand how they are spread on the web, (2) examine how the spread of disinformation and countermeasure messages on the web is associated with the intensity of the bias and hate crimes against Asian Americans, and (3) develop various data-driven computational models to help understanding the disinformation dynamics. The project's broader significance and importance are to inform civil society, including advocacy organizations and the general public, about how to strategize communication efforts in battling racial disinformation, and to make the developed tools and outcomes publicly available for broader uses.The project takes three-staged approaches. The main objective of the first phase is to develop computational tools for the detection and analysis of the temporal dynamics between Asian-targeted disinformation and countermeasures on the Web. A specific focus is on developing automated identification tools and deep-learning classification models by feature-engineering unique characteristics of disinformation. The objective of the second phase is to understand to what extent the spread of disinformation and countermeasures online is associated with the societal trend of implicit bias and hate crime occurrences against Asian Americans in the real-world, which can be achieved via developing deep-learning causality models. The objective of the third phase is to design scalable data-driven deep-learning models of disinformation dynamics in macro and micro levels, identifying unknown dynamics from the real-world measurements, which also enables simulations of the learned 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: A New Multiscale Framework for Integrating Socio-Economic Processes, Vector-Borne Disease Control, and the Impact of Transient Events

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Mathematical and Physical Sciences (MPS)

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Mathematical models for infectious diseases are critical tools for informing effective disease interventions. The disease management strategies informed by models of vector-borne diseases (such as diseases spread by mosquitoes) led to the elimination of malaria from the United States and other countries. However, malaria continues to have an enormous impact on resource-challenged regions of the world, while other existing and emerging vector-borne diseases such as West Nile Virus, Dengue Virus, and Zika Virus continue to impact both advanced and resource-challenged economies. A shortcoming of many existing modeling approaches is the absence of an explicit connection between economic factors and disease transmission – two factors that influence each other. In an increasingly connected and complex world, accounting for the synergistic effects of these factors is essential for effective disease control. For example, the COVID-19 pandemic caused sudden economic shifts that affected the progress of malaria control programs, resulting in increases in malaria-related deaths. In collaboration with an ecologist/economist and with an entomologist and a biostatistician from Africa, the investigators will build mathematical tools to bridge this gap, which will be used to inform public health and economic growth policies. The project will train graduate students through involvement in the research. Workshops on feedback between economic and disease systems will train a new generation of undergraduate students from diverse backgrounds on this important topic. This work will also involve students from Africa, a continent that is highly affected by vector-borne diseases. Incorporating socio-economic factors into vector-borne disease (VBD) systems is key to identifying new interventions, by unraveling the intertwined relationship between VBD, socio-economic conditions, and transient events. The investigators aim to develop a new mechanistic framework for transient disease dynamics that accounts for feedback between VBDs and economic systems, the impact of fast-slow time scales, and sudden endogenous and exogenous events that shift the economic landscape. Factors including heterogeneous mosquito-biting and insecticide-treated-net (ITN) replacement times, demographic structure, and human behavior in relation to ITN use will be included. For even the simplest economic-VBD model, standard methods for computing basic epidemiological metrics and performing structural identifiability analysis break down. Hence, the investigators also plan to build the mathematical toolbox required to analyze and simulate these coupled systems. Although the framework will be applied to malaria as a prototype VBD using socio-economic and epidemiological data from Kenya, Madagascar, and West Africa to calibrate the parameters and validate the outcomes of the models, it is intended to be robust enough to be applicable to other VBDs. Furthermore, the framework will allow the investigators to realistically answer important public health and economic growth questions, including how to distribute ITNs across connected populations, and how to respond to spontaneous events affecting the economic landscape.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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RINGS: Resilient mmWave Networks via Distributed In-Surface Computing (mmRISC)

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Computer and Information Science and Engineering (CISE)

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Wireless networks are undergoing a radical transformation with the aim to provide the critical information infrastructure for the 21st century and foster new economic opportunities, innovation and many emerging applications. To facilitate this,  there is a focus on new spectrum in the millimeter-wave frequency range that has the ability to support ultra-high data rates with low latency needed for applications in automation, robotics and cyber-physical systems, smart health, and autonomous vehicles and systems. The spectrum can also support wireless backhaul links to bridge the last mile connectivity, and provide broadband wireless access---the importance of which is clearly highlighted during the Covid-19 pandemic.  However, these connectivity links are prone to physical channel disruptions including blockages and channel propagation variations, and therefore not resilient. The proposal involves a multi-disciplinary approach across three different research groups towards addressing these problems and ensuring resilience and scalability in such networks. The proposed concept of smart reflecting surfaces aims to enable dynamic and on-demand control of wireless channels to create favorable transmission allowing robust wireless connectivity in mobile mmWave WLANs. The success of this project can enable the next-generation, ubiquitous, and low-cost mmWave wireless access, including flexible deployment of wireless backhauls addressing the last-mile connectivity, satellite communication, and intelligent wireless sensing systems for smart cities and cyber-physical systems. The project will address the need for developing US-centric capabilities in semiconductors and wireless technology, through training of students across the undergraduate and the PhD program in a rigorous multi-disciplinary research effort. This project, mmRISC, builds Resilient mmWave Networks via Distributed In-Surface Computing.  We investigate mmWave, multi-band hybrid surfaces with embedded custom-silicon ICs that provide on-surface signal amplification and computing abilities for multi-user localization, tracking and ambient sensing.  Our proposed surfaces enable resilient and reconfigurable distributed networks that maintain low latency, energy and spectral efficiency. We pursue a holistic, cross-system research approach focusing on scalable, spectrally-agile, low-power, and low-cost hybrid surfaces operable across multiple mmWave bands with controlled amplification. We will also focus on embedded computing for on-surface sensing, and resilient network architectures supporting such smart surfaces allowing capacity optimization. Through cross-layer design approaches, our proposed work will inform the architecture of NextG surface-assisted wireless networks for the future.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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Conference: Student and Junior Faculty Support for the International Symposium on Sustainable Systems and Technology (ISSST) 2022

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2224644 (Lu). The International Symposium on Sustainable Systems and Technology (ISSST) conference will engage participants from multiple disciplines to focus on sustainability and engineering challenges. This conference, to be held June 21-23, 2022 in Pittsburgh, PA, will address a spectrum of issues for assessing and managing products and services across their life cycle, as well as the design, management, and policy implications of sustainably engineered systems and technologies. ISSST 2022 will highlight five themes: (1) Integrated human-physical systems – how to integrate human dimensions into sustainability design and implementations; (2) Infrastructure sustainability and resiliency – quantitative assessment of the sustainability and resiliency of the built environment; (3) Advances in sustainability assessment methods – the development of new sustainability assessment methods, including data science and analytics, machine learning, multi-objective analyses, and other methods for emerging systems and technologies; (4) Sustainability education – pedagogical approaches to teaching sustainability at different levels and lessons learned in designing sustainability curriculum; and (5) Other creative sustainability-related topics – new methods and ideas that demonstrate unique partnerships, big ideas, perspectives from non-STEM (science, technology, engineering, math) disciplines. The conference will also host special sessions to bring participants together in educational sessions, workshops, communications, and professional skills development. The conference is designed for significant participation from students and young faculty members, as this is critical for developing the next generation of sustainable solutions. This grant will help to expand the participation of students, underrepresented groups, and junior faculty from across the United States in ISSST 2022. Social equity, differential access and impacts, and environmental justice will be prominent across all of the five themes of the 2022 conference. Several sessions are also expected to address the implications of the COVID-19 pandemic on production and consumption, pollution, and critical infrastructure systems. A diverse group of members will share their work in sustainability science and engineering through paper and poster presentations, as well as formal and informal discussions. The conference will also feature a variety of plenary/keynote speakers, including Dr. Aurora Sharrad, Director of Sustainability, from University of Pittsburgh and Dr. Ranran Wang from Leiden University, Netherlands. All of this will provide participants a venue for discussing visions, educational approaches, challenges, as well as provide educators and students opportunities to gain insight on research on sustainability in a holistic manner.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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SBIR Phase I:  A Portable Oxygen Concentrator with High Flow Rates for In-home Therapy (COVID-19)

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Technology, Innovation and Partnerships (TIP)

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable easy oxygen delivery to patients with respiratory conditions.  Currently, patients requiring high flow-rates of oxygen above 4 L/min require oxygen tanks that are large, heavy and can be hazardous, limiting mobility and transportation options. The proposed system produces breathable oxygen at higher flow rates and lower energy compared to current portable oxygen concentrators, enabling sustained patient use. This enables sustained oxygen production in a portable manner to manage medical conditions causing oxygen deprivation, including Chronic Obstructive Pulmonary Disease (COPD) and Coronavirus Disease (COVID-19). This Small Business Innovation Research (SBIR) Phase I project will develop a portable system that utilizes a novel photocatalytic (light activated) reaction to separate oxygen from ambient air, trapped in a chemical solution, then released as needed through a temperature-controlled reaction. This project will monitor the capture and release reactions using absorption spectroscopy to determine the ideal conditions of oxygen production. Several photosensitizer chemical compounds (fullerene C70 and C60, rubrene, and methylene blue with urea) will be evaluated on system longevity by continuously cycling the systems under higher temperatures and light exposure, and monitoring their effects on oxygen production. A prototype will then be developed that generates targeted oxygen flow rates at the desired rate of energy consumption, and the oxygen produced validated as safe for inhalation using bench tests.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: Re-Thinking the Perception-Action Paradigm for Agile Autonomous Robots

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Autonomous robots will become pervasive in our society and will solve complex tasks, actively collaborating with each other and with humans. As the recent COVID-19 outbreak has highlighted, autonomous robots can solve a range of time-sensitive problems including logistics, reconnaissance, and disinfection of critical areas. Beyond pandemic, small-scale robots can help humans in complex or dangerous tasks such as search and rescue, security, and surveillance, and, thanks to their lighter weight, they pose only a modest risk to human safety. These time-sensitive tasks require robots to make fast decisions and agile maneuvers in complex and dynamic environments. State-of-the-art autonomous navigation approaches, while mature, are slow and brittle and prevent robust and resilient agile navigation. This Faculty Early Career Development (CAREER) Program studies the fundamental perception-action problem for agile navigation of autonomous robots in complex environments by planning a novel, low-latency, robust, adaptive, safe, and resilient paradigm. This project aims also to educate students on the technical aspects, societal benefits, and ethical use of autonomous systems by establishing a unique multi-disciplinary, and inclusive research and educational platform which includes a core curriculum on robot localization and navigation, and a series of online racing hackathons for a post-pandemic customized and inclusive research and educational experience. These will contribute to lowering the barrier to participation in research and education for students, particularly underrepresented minorities.This project will generate a new foundational theory, which includes models and algorithms resulting from a principled combination of perception, learning, and control to holistically design visual perception and action to create small-scale agile autonomous robots. The goal is to capture the strict cross–coupling effects between perception and action to jointly and concurrently resolve the perception-action problem to speed up the robots’ decision making process and increase their agility. The project is organized in three thrusts according to a series of objectives, culminating in innovations in robotics autonomy research and education. A compressed and unified representation of the perception and action spaces guarantees to reduce the robot's inference latency and naturally reveals the cross-coupling effects among them. Next, the robot will exploit using this representation its action-predictive information to enhance its inference capabilities and will employ an optimal control/planning approach to maximize its perception accuracy and quality.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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: Realizing the Potential of Behavioral Data Science for Population Health

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Computer and Information Science and Engineering (CISE)

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CAREER: Realizing the Potential of Behavioral Data Science for Population HealthDetailed behavioral data from phones, watches, fitness trackers and health apps offer an unparalleled opportunity to quantify and act upon previously unmeasurable behavioral changes in order to improve mental health and accelerate responses to emerging diseases. This is possible because these conditions often manifest themselves through behavioral and physiological changes (e.g., reduced activity, increased heart rate, depressed mood). Currently, such conditions exact a massive toll, with mental health conditions representing 19% of all years of life lost to disability and premature mortality and viral infections, such as COVID-19 and influenza, rising to the third leading cause of death in the US in 2020. Despite the significant potential of increasingly available data, broad and tangible impacts have yet to be realized, in part due to the unique challenges of integrating and modeling a broad range of behavioral and health data. This project seeks to address these challenges by developing and sharing computational tools that will enable researchers, clinicians and practitioners to improve mental health treatment and more rapidly respond to emerging diseases. The project will also provide an integrated research and educational program by: (1) increasing high-school students' exposure to computer science with a focus on health and well-being applications; (2) creating and disseminating materials for high-school teachers to use in their classrooms; (3) preparing undergraduate students to address health and well-being challenges through an interdisciplinary data science class; and (4) broadly disseminating the results of this work through public open-source software and workshops for researchers and practitioners. The goal of this project is to develop a unified representation learning framework that addresses the unique challenges of modeling fine-grained behavioral data. Specifically, the learned compressed representations must: (1) be highly predictive in spite of the challenges of integrating heterogeneous data sources from sensors, devices, app use, demographic and health information (e.g., highly seasonal time series, discrete events, and static features), (2) effectively generalize to new users, populations, and outcomes outside the training data and source domain, (3) be robust to commonly missing data, and (4) protect private identifying information when considering how to share data or models with others. Additionally, due to recruiting and participation costs, most behavioral health applications represent small data problems, making it particularly challenging to learn effective predictive models from individual datasets alone. To address these challenges, this project will develop and integrate new methods for representation learning, self-supervision, transfer learning, robustness to missing data, and the protection of identifying information. The research team will demonstrate and evaluate the performance of the representation learning framework across a diverse set of health applications, including behavioral monitoring of influenza and COVID-19 symptoms and personalizing sleep and mental health interventions. With these advancements, the project seeks to enable rapid model customization, significantly reduce the expertise and effort required to build new behavioral health research and applications, and help scientists and health professionals answer fundamental research questions about the impact of behavioral health conditions and the design of personalized interventions.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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I-Corps: Thermostable liquid formulations of mRNAs and mRNA lipid nano-particles pharmaceuticals

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Technology, Innovation and Partnerships (TIP)

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University of North Carolina at Wilmington

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University of North Carolina at Wilmington

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The broader impact/commercial potential of this I-Corps project is the development of an RNA formulation technology for safe storage and transport. The ability to transport and store mRNA-based pharmaceuticals without ultra-cold requirements may significantly lower the costs of distribution of mRNA drugs and vaccines. Also, elimination of the requirements for ultra-freezer and lyophilization will greatly increase the capacity and speed of deployment of mRNA pharmaceuticals as well as their accessibility to remote areas. Finally, the superior thermostability of the new mRNA formulations may reduce the wastage of drugs/vaccines caused by unexpected exposure to elevated temperature during transportation and storage.This I-Corps project is based on the development of two methods for preparing thermostable liquid formulations of mRNA and mRNA-Lipid Nano-Particles (LNPs), respectively. The first method is to prepare an emulsion containing mRNA condensate-induced non-ionic polymers and salts. The second method is to stabilize mRNA-LNPs using acidic buffers with crowding agents.  The success of the COVID-19 mRNA vaccines showed the advantages of this pharmaceutical modality including rapid development, high efficacy, high safety, and ease of manufacturing (as compared to cell culture production). However, deployment of the COVID-19 mRNA vaccines has been met with major logistic challenges due to the intrinsic instability of mRNA. mRNAs undergo fast degradation in aqueous solutions and require freezing or lyophilization (freeze-drying) for transportation and storage. The requirements for ultra-cold chain or lyophilization facilities not only limit the speed of deployment and accessibility of mRNA pharmaceuticals, but also increase the costs and wastage during the distribution process. Using the proposed technologies, mRNA or mRNA-LNP solutions may be able to be stored at room temperature or under refrigeration for months without freezing or lyophilization.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: STEM faculty support to address impacts from COVID-19 on Tribal Colleges and Universities Program institutions

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A goal of the Tribal Colleges and Universities Program (TCUP) is to increase the science, technology, engineering and mathematics (STEM) instructional and research capacities of specific institutions of higher education that serve the Nation's indigenous students. Expanding the STEM curricular offerings at these institutions expands the opportunities of their students to pursue challenging, rewarding careers in STEM fields, provides for research studies in areas that may be culturally significant, and encourages a community and generational appreciation for science and mathematics education, and sustainability of capacity gains is significantly enhanced by retaining the talent of credentialed STEM faculty. This project aligns directly with that goal.The coronavirus pandemic of 2020-2021 caused major disruptions to institutions of higher education. However, for tribal colleges and universities, whose core operating funds are directly aligned with student enrollment, drops in enrollment equate to loss of funding. To mitigate against detrimental effects on STEM instructional capacity, this award will support the position of one full-time STEM faculty member, as well as other resources to maintain United Tribes Technical College’s STEM program as it recovers from the impact of the pandemic.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
RAPID: STEM faculty support to address impacts from COVID-19 on Tribal Colleges and Universities Program institutions

# data[].attributes.abstract
A goal of the Tribal Colleges and Universities Program (TCUP) is to increase the science, technology, engineering and mathematics (STEM) instructional and research capacities of specific institutions of higher education that serve the Nation's indigenous students. Expanding the STEM curricular offerings at these institutions expands the opportunities of their students to pursue challenging, rewarding careers in STEM fields, provides for research studies in areas that may be culturally significant, and encourages a community and generational appreciation for science and mathematics education, and sustainability of capacity gains is significantly enhanced by retaining the talent of credentialed STEM faculty. This project aligns directly with that goal.The coronavirus pandemic of 2020-2021 caused major disruptions to institutions of higher education. However, for tribal colleges and universities, whose core operating funds are directly aligned with student enrollment, drops in enrollment equate to loss of funding. To mitigate against detrimental effects on STEM instructional capacity, this award will support the position of one full-time STEM faculty member, as well as other resources to maintain Red Lake Nation College’s STEM program as it recovers from the impact of the pandemic.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.