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Doctoral Dissertation Research: Task Performance and Causal Claims: Leveraging Network Analysis and Large Language Models to Extract Information from Organizational Hazard Texts

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Crises – particularly those rising to the level of disasters –demand an organized response. In complex systems of organizational disaster response, core tasks such as communication and coordination of activities become large-scale challenges. This study evaluates such challenges in two research thrusts. The first thrust examines the broader discourse created by official online communications disseminated to the public during the COVID-19 pandemic. The second thrust examines the extent to which an organization's task performance impacts effective collaborations in an emergent multi-organizational disaster response network, with a specific case study of the 2005 Hurricane Katrina. The “lessons learned" from our communications studies are used to synthesize concrete organizational communication strategies that can help practitioners and officials better engage stakeholders, thereby building the trust between the public and officials that is critical for disaster management. The results of our collaboration study inform organizational training efforts for hazard events, which may decrease the friction of collaboration and coordination efforts in response to large-scale disasters and thus contribute to the protection of lives and property.    This study aims to provide insights at multiple levels of the organizational response process, from shared task performance among organizations to the broader structure of public-facing discourse around health hazards and the emergence of multi-organizational response networks. This study leverages the strengths of machine learning-based natural language processing methodologies and network analysis techniques to extract from, and analyze massive corpuses of hazards communications. Thus, this study contributes to methodologies of large-scale information extraction, decreasing costs previously associated with obtaining high-quality data from records while increasing the potential value of underutilized historical case studies.    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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Advantaging the National Artificial Intelligence Research Resource (NAIRR) Pilot: Leveraging the COVID-19 HPC Consortium Experience

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University of Illinois at Urbana-Champaign

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This project supports the broader efforts of the National Artificial Intelligence Research Resource (NAIRR) Pilot to address how powerful Artificial Intelligence (AI) resources can be used to accelerate scientific understanding and discovery and further the capabilities of AI models. It also develops efficient processes for providing these resources to researchers. This project builds on the successful model of, and lessons learned from, the COVID-19 HPC Consortium (C19HPCC), which demonstrated the power of public-private partnerships in addressing global challenges. By applying these lessons to the NAIRR Pilot, the project creates a robust framework for future government-academia-industry collaborations. This not only enhances the NAIRR Pilot but also paves the way for the full NAIRR program, ultimately supporting a broader range of research efforts and fostering innovation in artificial intelligence.    The project leverages lessons learned from the C19HPCC to enhance the National Artificial Intelligence (NAIRR) Pilot. The C19HPCC was a collaborative effort that brought together high-performance computing (HPC) resources from government, academia, and industry to accelerate research and discovery in the fight against COVID-19. The primary goals are to develop efficient processes for allocating AI resources, improve proposal review mechanisms, establish effective reporting methods, foster partnerships across government, academia, and industry, and establish and evolve governance structures and coordination mechanisms to manage the diverse set of resources and stakeholders involved. The scope includes leveraging prior policies, procedures, and tools from the C19HPCC to support the NAIRR Pilot and ultimately the full NAIRR program.  By applying these methods, the project aims to create a robust framework for future AI research and innovation.    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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NSF I-Corps Hub (Track 1): Northwest Region

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The broader impact/commercial potential of this I-Corps Hubs project is the development of infrastructure needed for entrepreneurial training for academic science, technology, engineering, and math (STEM) researchers and high potential community teams.  This training will accelerate the commercialization of cutting-edge technologies and enhance regional innovation. It will also support workforce readiness in a region that is rapidly changing as the result of post-pandemic, economic and geographic dynamics of “meta cities,” and net in and out migration to and from rural and underserved areas throughout the region.  In addition, Hub activities will provide the training needed to power other NSF initiatives promoting commercialization and innovation. Developing these entrepreneurial skills for both academic researchers and throughout the region’s workforce amplifies the economic and societal impact of NSF and other-funded basic research while accelerating the growth of startups, providing economic benefit to the region and beyond.  This will be accomplished in an inclusive way to multiply opportunities, increases national competitiveness, and secures an economic future for all.       This I-Corps Hubs project is based on the aim to advance the translation of deep technologies into societal and economic impact. This collaboration covers a large geographic area, inclusive of both urban and rural locations throughout Alaska, California, Oregon, and Washington. The region shares distinct commonalities between the proposed Partners and synergies that may be leveraged to serve a uniquely diverse population and maximize economic impact throughout the region. The proposed Hub activities will be designed to support regional and national I-Corps training through team expansion, fuel regional and national economic growth, produce actionable entrepreneurial research, and broaden participation among underrepresented areas and populations.  The Hub Partners share a mission to reduce time and risk associated with translating top research from lab-to-market, while expanding educational and economic opportunity throughout the region. Through education, evidence, and experience, the Hub will drive creation of sustainable, scalable technology-based startups with both regional and national impacts. The Hub will strive to raise awareness of the value of entrepreneurship among science and engineering faculty and students, using a variety of programs designed for inclusivity and meeting scientists and engineers at their knowledge and skill level, whether they are curious about the fit of their technology to solve an industry problem or are committed company founders.    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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Computer and Information Science and Engineering (CISE)

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Enormous health inequality persists in the United States. Even prior to the COVID-19 pandemic. In many areas of the country, people with a higher income live up to decade longer than those in the lowest income levels. Additionally, the pandemic itself has hit low income and under-served populations especially hard. Biased medical decision-making contributes to this health inequality. For example, previous work has shown that one of widely used health risk prediction algorithms assesses African-American patients as less sick than equivalently sick White patients. This research will make medical decision-making fairer by statistically analyzing the decisions made both by humans and by algorithms. The research will identify sources of bias (for example, when medical tests are given to patients with better access to healthcare rather than to patients most likely to have a disease), and propose solutions (for example, reallocating tests to patients who are predicted to have the highest disease risk). This will not only make healthcare fairer; it can also make it more efficient, by allocating medical resources where they will do the most good. The project will also create a publicly available class on how to design fair algorithms, and conduct a large-scale study of how engineers can be trained to design fairer algorithms, to improve the preparedness of the engineering workforce.    Because important medical decisions are made both by humans and by algorithms, the research pursues three objectives: 1) detecting bias in human medical decision-making, focusing on three high-stakes medical settings: allocation of medical testing, healthcare quality assessment, and interpretation of medical images. Further, the project will also build algorithmic decision-aids to reduce human bias, by drawing clinicians’ attention to medically relevant features they may have overlooked. Finally, the project targets making algorithmic decision-making more equitable, by examining the features it is appropriate to include in a medical algorithm. The research will be conducted in collaboration with clinicians to maximize translational benefit to patients. The methods developed, which draw on techniques in Bayesian inference and deep learning to provide interpretable models of how bias arises, are more generally applicable to decision-making across a host of high-stakes domains—including lending and hiring—and thus can impact a wide range of fields concerned with equity in decision-making, including law and economics.    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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Equipment: Course-Based Undergraduate Research Experiences: Engaging Historically Underrepresented Students Using Stress Block Image Correlation Simulation

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With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Educational Instrumentation (EI) track aims to engage students in course-based undergraduate research experiences (CUREs) in a lab-led freshman physics course (PHYS 2425) along with eight other courses. This approach will provide students with valuable opportunities to conduct various hypothesis-driven research projects related to fatigue loading using the ElectroForce (EF) 3330 equipment and an in-house developed innovative fixture called the Stress Block (SB). Economic shifts, societal changes, alternative career paths, and the lingering effects of COVID-19 have all impacted undergraduate enrollment. With a strong job market for non-degree roles, more high school graduates are considering direct entry into the workforce. However, in today’s competitive job market, the value of higher education remains critical, offering specialized skills and advantages that can elevate quality of life. For many physics and mechanical engineering undergraduates, insufficient high school preparation can create obstacles in problem-solving and understanding complex measurements, often hindering success in rigorous university programs. CUREs will provide essential support by allowing students to apply theoretical concepts to real-world scenarios, reinforcing understanding through hands-on learning. This experience will also enable students to build a supportive network with faculty and peers, contributing to their professional growth. Through active participation, CUREs will foster a sense of ownership and deeper engagement with learning. The SBICS-CUREs project, utilizing ElectroForce (EF) 3330 and Digital Image Correlation (DIC) technology, is designed to enhance these experiences while also contributing to reducing the gender gap in STEM fields.    The hypothesis for this project proposes that integrating the ElectroForce (EF) 3330 equipment with a custom-designed Stress Block (SB) fixture in a freshman physics course will significantly enhance students’ understanding and application of hypothesis-driven research. The specific aims are to (1) connect the SB attachment to the ElectroForce (EF) 3330 and (2) apply DIC techniques on samples tested with this setup. The methodology includes six steps: 3D printing samples, speckle deposition for image correlation, setting up a GoPro for reference images, mounting the SB fixture on the ElectroForce (EF) 3330, applying sinusoidal loads, and conducting DIC analysis to assess deformation and strain. This process gives students hands-on testing and simulation experience, bridging theoretical knowledge with real-world applications. Reflective learning is central to this project, utilizing DIC software like Ncorr, a free tool for full-field, non-contact optical measurements of deformation and strain in mechanical components. Findings will be shared through conferences, peer-reviewed publications, and YouTube videos, with plans to connect with industry leaders like Boeing, KBR, and agencies such as NASA and National Science Foundation. Additionally, partnerships with local Independent School Districts (ISDs) will enable high school students to participate, building a recruitment pipeline for UHCL STEM programs. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims.    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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MPOPHC: Quantitative design of effective testing-based policies through infection trajectory modeling

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Mathematical and Physical Sciences (MPS)

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Diagnostic tests play a crucial role in the management of infectious disease transmission. Testing is the fastest most reliable way to inform a person whether they are infected, and thus whether they should adjust their behavior to prevent onward spread. Testing policies have long contributed to public health, including in the control of HIV, tuberculosis, and malaria. During the COVID-19 pandemic, various test-based policies were successful, including pre-event screening (e.g., testing before entering a sporting event), traveler screening (e.g., testing before boarding a flight), and regular screening (e.g., weekly testing at universities). Such policies could also help control the spread of other existing and novel respiratory pathogens. However, we currently lack a robust, data-driven framework to estimate the potential impact of testing-based infection control strategies in general. To fill this gap, this project will develop a flexible modeling framework to simulate how different testing policies might perform for various pathogens, tests, and human behavioral scenarios. This project will also develop the statistical tools needed to infer how diagnostic test results, infectiousness, and behavior relate to one another, informed by data on SARS-CoV-2 and other respiratory pathogens. To maximize the impact of these findings, this project will build mature, open-source software products to compare testing-based policies, accompanied by tutorials for policymakers and a new open-source data hub to consolidate information relevant to testing-based policies. The successful completion of this project will improve our ability to control existing respiratory pathogens and enhance our preparedness for future pandemics.     Fundamental to this project is the characterization of how infectiousness, detectability, symptoms, and behaviors change over the course of a respiratory infection – a collection of features called an infection trajectory. While the details of an infection trajectory can be omitted for some types of policy assessments, testing-based policies depend critically on an accurate and statistical understanding of infection trajectories. Infection trajectory-based models allow for the separation of individual-level features of disease transmission from the between-host dynamics, permitting a “plug-and-play” approach to policy design, without compromising the ability to tailor solutions to local needs and populations. This project’s policy modeling framework will develop a stochastic description of infection trajectories, represented by a joint distribution of an infection’s measurable variables. This will allow the researchers to assess variability in policy outcomes and to identify cross-policy interactions. This project will develop a framework to infer infection trajectory distributions from multimodal data and will deploy that framework to guide the design of studies for collecting new infection trajectory data. Finally, this project will create a suite of software, educational, and data tools for informing infection trajectories and associated policies. For the public health policy community, successful completion of this project will produce new, high-quality policy design models and assessment tools, complemented by educational and interactive exploration webpages. For the scientific community, this project will provide statistical tools and data sharing standards for infection trajectory data, supporting advances in virology and modeling. This award is co-funded by the NSF Division of Mathematical Sciences (DMS) and the CDC Coronavirus and Other Respiratory Viruses Division (CORVD).    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: Translation potential of plant PYR1 biosensors for the rapid testing of environmental contaminants

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Technology, Innovation and Partnerships (TIP)

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The broader impact of this I-Corps project is the development of sensors for a wide array of previously undetectable chemicals. Global industrialization has created advanced materials and chemicals that persist in the environment with lasting effects on human health. Current technologies that test for environmental contaminants using chromatographic methods and laboratory test kits are slow, expensive, and inaccessible to consumers. This chemical sensor technology may provide portable test strips (similar to those used to test for COVID-19) to test for small molecules characteristic of pharmaceuticals, pesticides, and per- and polyfluoroalkyl substances (or PFAS). This technology may make field-based and in-home testing of pesticides and PFAS possible for the first time, giving consumers and regulators a way to alleviate safety concerns about pollutants in drinking water and food.     This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of PYR1 biosensors, plant hormone receptors that, when mutated, may be used to identify a wide variety of chemicals, including environmental contaminants (e.g., organophosphate pesticides and PFAS). Ligand recognition occurs exclusively in the PYR1 subunit, not the HAB1 partner, which makes the system significantly easier to engineer for new ligands than previously developed methods. The efficacy of these sensors has been demonstrated in yeast, bacteria, plants, and in vitro to test for substances of abuse in blood, urine, and saliva. These sensors also have been stabilized for high temperature and used as sensors in living plants. To date, the sensors have been designed for hundreds of target molecules, and ongoing refinement of the pipeline methodology makes it possible to identify sensors for new targets in less than a week.    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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MPOPHC: Incorporation of Game Theory Tools to Improve the Policy Making to Mitigate Epidemics of Respiratory Diseases

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Mathematical and Physical Sciences (MPS)

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University of Illinois at Urbana-Champaign

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During the COVID-19 pandemic, it was observed that individuals did not always follow mitigation policies closely. Instead, they behaved according to their own objectives, where demographic and socioeconomic factors seemed to have influenced their responses to the set policies. Therefore, this project aims to improve the policymaking processes to mitigate the transmission of respiratory pathogens by incorporating the individuals’ decision-making and socio-demographic heterogeneities. To do this, the investigators propose to develop and study game theoretical mathematical models, as well as simulation tools and numerical approaches that can be adapted to specific public health problems of interest to practitioners and researchers. These tools will be made publicly available. This project will also involve interdisciplinary training for graduate students in applied mathematics, statistics, operations research, epidemiology, and quantitative biology.    To model many interacting agents, the investigators will develop and study extensions of mean field games (MFGs).  First, they will focus on building multi-population MFGs and graphon games to incorporate socio-demographic heterogeneities while finding the Nash equilibrium responses of individuals under different disease mitigation policies (e.g., vaccination policies and non-pharmaceutical interventions). Furthermore, different equilibrium notions to incorporate altruism in the populations will be explored through the introduction of mixed multi-population MFGs that include both cooperative and non-cooperative individuals. Later, the investigators will focus on finding optimal mitigation policies by using Stackelberg MFGs that include the optimization of a regulator (e.g., a governmental institution). The extensions of Stackelberg MFGs that include heterogeneities in the mean field populations, altruistic behaviors, and possible state variables for the regulator will be developed and analyzed. Surveys and analyses of publicly available data will be conducted to calibrate and parameterize the mathematical models to capture real-life patterns. Finally, numerical approaches and simulation toolboxes will be implemented to solve large dimensional and more complex models, which will allow policymakers to adapt and parametrize our models according to their specific needs.     This award is co-funded by the NSF Division of Mathematical Sciences (DMS) and the CDC Coronavirus and Other Respiratory Viruses Division (CORVD).    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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OPUS: SYNTHESIZING THEORY AND DATA IN THE ECOLOGY OF ZOONOTIC DISEASES: A MULTI-SYSTEM PERSPECTIVE

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University of Georgia Research Foundation Inc

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Emerging infectious diseases, driven by globalization, societal and economic activities, land use changes, and climate change, pose a significant threat to public health. This project addresses these threats by synthesizing data from NSF-funded studies on pathogens like West Nile virus, avian influenza, and MERS-coronavirus. By integrating these data with recent community initiatives, the project will create a comprehensive and interoperable database accessible through an online portal. By leveraging a data-driven approach combining ecology, data science, and mathematical modeling, the project will generate actionable knowledge for public health strategies and policy-making. Emphasizing interdisciplinary collaboration and cultural exchange between the US and UK, it will enhance global pandemic preparedness. The study will lead to new understanding of how diseases emerge and spread, which is crucial for predicting, preventing, and managing future outbreaks. The findings will be made available through user-friendly web dashboards, ensuring accessibility for scientists, policymakers, and the public, ultimately contributing to improved health and welfare for human and animal populations.    The goal of the study is to integrate knowledge from two decades of research, advancing understanding of pathogen dynamics, generating actionable knowledge for disease prediction, prevention, and management, and fostering interdisciplinary collaboration. Published data will be reviewed for consistency in format, variable names, and metadata, and then harmonized for interoperability with repositories such as EID239, GLOBI40, and the Verena dataverse. The harmonized data will be archived in Dryad and Figshare, ensuring long-term preservation and accessibility. Additionally, a sophisticated web interface will be developed to enable interactive exploration and analysis of the datasets, providing tools for visualization, filtering, and cross-referencing data points. A conceptual framework will be introduced to guide future research in the macroecology of emerging diseases and pandemics, enabling statistical methods and machine learning algorithms to identify patterns and predict disease emergence. This framework will serve as a foundation for interdisciplinary research, facilitating collaboration across fields such as epidemiology, ecology, data science, and public health.    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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BPC-AE: STARS Computing Corps: Extending a National Community of Practice for Developing BPC Change Leaders

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The New Jersey Institute of Technology will extend STARS Computing Corps BPC Alliance. STARS aims to address the challenge of increasing the number and representation of Black, Hispanic, and women students who graduate with computing degrees and who remain in the field of computing after graduation. STARS  serves as a national resource for transforming computer science and artificial intelligence education. Through this extension,  STARS will continue its national community of practice and associated resource center to build capacity in college computing departments for developing more inclusive computing and AI educational experiences. This work builds upon a multi-year study, which provided evidence that the STARS Computing Corps approach is effective for broadening participation in computing goals, and indicates the value of a community of practice that engages college computing and AI faculty and students with a shared commitment.  Ultimately, the work of STARS has the potential to increase student persistence in computing and AI research, degree programs, and careers.    STARS creates significant knowledge, institutional, and human resources that can increase the reach of BPC research to a larger audience of researchers, educators, and K-20 students, and builds capacity to dramatically increase the number of people taking action in efforts to broaden participation in computing. STARS conferences, programs, and networks propagate evidence-based BPC approaches and advance peer-reviewed BPC scholarship. The key indicator for STARS impact is increased persistence for Black, Hispanic, and women students (and intersections thereof) in computing degree programs in institutions of higher education.This extension will 1) include new members and partnerships that expand the reach of STARS and that emphasize participation of Black and Hispanic students and faculty; 2) build capacity for evidence-based BPC practices for K12-university partnerships; 3) establish connections to STARS Alumni in industry to support professional networking and mentoring for current STARS students and to promote the persistence of STARS Alumni in the computing workforce. The project will also research: 4) how the STARS system of BPC interventions have longitudinal impacts on persistence in computing degree programs and the computing workforce with sample sizes that uniquely enable analyses of differential impacts at intersections of race, ethnicity, and gender, 5) how to adapt interventions to consider the changing landscape of needs for BPC, including changing university demographics, legislation that impacts BPC initiatives in higher education, the impacts of COVID on college student and faculty engagement, and the need to advance AI education, and 6) how to provide inclusive computing education experiences in the context of HBCUs, eHSIs, and community colleges. Finally, this extension will enable further research on broadening participation in computing, by providing early research opportunities for undergraduate students from underrepresented groups in computing and advancing dissemination of BPC research through the RESPECT and STARS Celebration conferences.    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.