Grants

NIDDK - National Institute of Diabetes and Digestive and Kidney Diseases

RESPONSE: Diabetes technology, including continuous glucose monitoring (CGM), insulin pumps, and hybrid closed loop (HCL), is the gold standard for improving outcomes for patients with T1D. Recently, the consortium applying to this NOFO completed a randomized, crossover, automated insulin delivery in elderly (AIDE) trial, which demonstrated that older adults with T1D can safely use HCL and improve HbA1c with less hypoglycemia. Despite these promising results, a significant proportion of older adults with T1D are not considered for HCL therapy because of an inability to independently manage their diabetes due to cognitive impairment. “How can technology be harnessed for older people with special needs with cognitive or functional deficits” has been recognized as a crucial knowledge gap in a key consensus paper (PMID: 32946822). Yet, there currently exist no data to support HCL use in this complex and important population. It has been said that "the contribution of closed-loop technology to diabetes management will depend less on technological factors than on 'the real-life interactions between people and technology' including clinicians, family members, as well as people with diabetes themselves.” We propose research to assess the feasibility and acceptability of implementing a patch HCL system in older adults with cognitive impairment who require additional support from a "care partner." Before the intervention phase begins, we will conduct necessary formative research to anticipate barriers to successful HCL adoption and sustained use via a series of focus groups. Results will inform tailored training materials and protocols. In the intervention phase, we will enroll 54 cognitively and functionally impaired (i.e., cannot manage T1D independently) older adults with T1D alongside their 54 care partners, over 3 months of follow-up. Patient participants and care partners will proceed to the CGM run-in Phase, followed by tailored training on study patch pump integrated with CGM by clinic study staff. Glycemic management based on HbA1c and CGM metrics will be studied at pre-specified time points, alongside patient-reported, and care partner-reported, psychosocial outcomes, including burden. An important feasibility outcome is the ability to implement/evaluate treatment strategies across multiple clinics; three sites will be used in COPILOT. We will conduct a mixed-methods intervention evaluation to complement the outcomes by characterizing the key contextual factors and additional resources needed to scale an efficacious intervention in this vulnerable cohort. Findings from COPILOT will inform, for the first time, effects of and strategies required to implement HCL in older adults with T1D who are not independent in their self-care due to moderate-severe cognitive impairment.

NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development

The United States (U.S.) has a higher maternal mortality ratio than most other high-income nations. Severe maternal mortality (SMM) and maternal mortality rates are elevated in rural counties compared to national averages. Births covered by Medicaid, of which there is a higher percentage in southern states (and Arkansas specifically), are more likely to experience postpartum SMM and maternal mortality. Individuals in rural, resource-limited counties are more likely to experience SMM and maternal mortality. Arkansas, the location for this study, is largely rural (41% of Arkansans vs 14% U.S.), has higher poverty rates (16.3% in Arkansas vs 11.5% U.S.), has more births covered by Medicaid (54% in Arkansas vs 42% U.S.), and is ranked second worst for the health of women and children among U.S. states. The maternal mortality ratio for Arkansas from 2018-2021 (the most recent data) was 43.5 deaths per 100,000 live births, nearly double the national ratio of 23.5 for the same time period. An estimated 75% of SMM and 76% of postpartum deaths occur within 6 weeks of delivery, with the vast majority (68%) within the first 3 weeks. Multiple studies demonstrate that the majority of postpartum ED visits could be addressed through outpatient care. The standard for postpartum care is a comprehensive visit scheduled approximately 6 weeks postpartum. Given that most postpartum SMM and maternal mortality happens before 6 weeks, one or more early postpartum visit(s) in the first 3 weeks is recommended as part of the American College of Obstetricians and Gynecologists’ (ACOG) 2018 guidelines. However, this early check-in has not been widely implemented. CARE PATH includes three weekly telehealth visits from a perinatal CHW to ensure timely identification and treatment of complications following ACOG guidelines; it is a fully powered RCT conducted among 500 rural postpartum individuals from a range of geographic and economic backgrounds, designed to compare: A) CARE PATH and B) usual care on postpartum visit completion, early detection of postpartum complications, postpartum hospital readmission and ED visits, and reproductive life planning/contraceptive counseling. CARE PATH has potential to reduce SMM and maternal mortality because it is conducted among rural postpartum individuals who experience the most pressing maternal health access challenges and are less often reached by existing health system innovations. CARE PATH directly addresses gaps in current scientific knowledge identified by systematic reviews and is expected to inform policy and practice, while meeting the needs and preferences of individuals residing in areas with limited healthcare infrastructure in Arkansas.

NINR - National Institute of Nursing Research

ABSTRACT: On any given day, nearly 2 million people are incarcerated in the US. People of color, disabled people, LGBTQ people, and people from lower socioeconomic statuses are incarcerated at much higher rates than white, able bodied, cis, straight, economically privileged people. This population experiences high rates of chronic illness and infectious disease prior to incarceration and continue to experience worse health during and after incarceration. The level of healthcare provided in correctional facilities varies, but in all cases when the medical need of a patient exceeds the capabilities of the institution, they are transferred to the community setting for care. While incarcerated patients are entitled to many of the same decision-making and privacy rights as non-incarcerated patients, little is known about how patients form relationships with their care team and make healthcare decisions while incarcerated, particularly in the community hospital setting. While most hospitals have policies on shackling, police presence, and other security measures, our preliminary research indicates that many clinicians are unaware of these policies and even more clinicians are unclear how to navigate care decisions within a complex policy landscape, perpetuating systemic inequities in care delivery. We will use the San Francisco General Hospital as a case study to examine how institutional, local, state, and federal policies converge and the conflicts that may arise as providers and correctional staff implement said policies with a diverse and vulnerable patient population. We will interview local and institutional policy makers from the hospital, the jail, and the sheriff’s department (N=45) and survey clinicians (N=500) about their knowledge, understanding, and utilization of existing policies (Aim 1). Through institutional ethnography, including participation observation and interviews with clinicians (N=50), patients (n=50), and correctional staff (N=25) we will develop a multifaceted understanding of care delivery, models of consent and decision making, and ethical challenges for incarcerated patients in the hospital setting (Aim 2). We will then use methods of Human Centered Design, within a Participatory Action Research framework, to translate empirical research findings into community driven policy and practice solutions (Aim 3). We are intentionally involving people with lived experiences of incarceration in all phases of the research (as co-investigator, research staff, advisory council members, and partners in policy development). We will engage in community-led co-learning of research best practices for collaboration with people impacted by the criminal legal system. Through this research, we will generate the needed evidence to develop community-based policy and practice recommendations to improve care, consent, and decision making for incarcerated patients in the hospital setting, advancing health equity and extending the research capacity of people with lived experience of incarceration. We will advance new understandings of the ethical principles of autonomy and justice for incarcerated patients while removing structural barriers to research through community-led efforts.

NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development

ABSTRACT We propose the K12 Career Advancement and Development in Rehabilitation Research (CADRRE) program. The CADRRE program will recruit and train junior faculty who are occupational or physical therapists. The program will support eight K12 Scholars per year who will be recruited from across the nation, and our program will provide them with the skills and research experience necessary to become independent investigators and leaders in rehabilitation. The goal of the CADRRE program is to train leaders who will enable a sustainable and cutting-edge rehabilitation science infrastructure in the United States. The University of Florida (UF) and the University of Southern California (USC) form a partnership that provides a research infrastructure and a critical mass of senior rehabilitation investigators. The strength of this partnership is illustrated by the highly successful K12 program (K12HD055929), that we co-directed from 2007-2024. During that time, we successfully mentored 43 scholars. Building upon that foundation, we propose a new career development program that expands the scope of the mentoring to train scholars at outside institutions in addition to scholars at UF and USC. We have identified 16 highly regarded partner institutions from across the nation that have enthusiastically agreed to participate. We will utilize a two- phase training model which has proven highly successful over a 15-year period of prior K12 support. At the onset of Phase 1, each Scholar will prepare an individualized career development plan under the guidance of an experienced K12 Lead Mentor (i.e., individuals at UF or USC that have a demonstrated record of success in mentoring junior faculty). Scholars at a partner institution (i.e. not at UF or USC) will also have an experienced local Institutional Mentor. The Phase 1 appointment will last up to 3 years and will include a structured didactic program involving research methodology, specialized courses and seminars, training in AI as well as the responsible conduct of research and mentored grant writing experiences. The Phase 1 Scholars will be integrated into an Interdisciplinary Research Team. These teams consist of successful rehabilitation researchers at UF and/or USC with relevant content expertise. The research team members do not replace the Lead and Institutional Mentors, but they will provide a further mentoring resource to assist the Scholars. In Phase 2, Scholars transition to independent research positions and will no longer receive salary support from the K12. Phase 2 Scholars will remain associated with the CADRRE program and continue to be mentored by the Lead Mentor, members of the Interdisciplinary Research Team, and the Institutional Mentor. The entire group of CADRRE scholars (Phase 1 and 2), mentors and advisory boards will gather yearly for an in-person annual meeting in which the research and overall progress will be evaluated and guidance provided by a network of expert advisors in the field. The latter meeting will also serve to help the Scholars network and build strong relationships with peers and senior mentors.

NSF

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Attention is dynamic and tends to fluctuate between external and internal states, and thus focusing attention on an instructor in undergraduate science courses for a long period of time is extremely taxing. While external attention, the selection and modulation of sensory information (e.g., focusing on an instructor’s voice while ignoring background noise), has been studied extensively, very little is known about the functional significance of internal attention, the selection and modulation of internally generated thoughts (e.g., quietly thinking about a prompt while ignoring distracting thoughts). Some researchers and practitioners have suggested that “thinking” periods can promote the development of student ideas, while others argue that internally-generated thoughts are primarily off-task and lead to poor learning. The current CAREER project at the University of Connecticut will attempt to reconcile these competing claims by exploring how opportunities to focus attention internally during a biology lecture can support or jeopardize student learning. Further, since it is currently unknown how often students switch between internal and external states of attention and how long these states last, this project will manipulate the frequency and duration of “thinking” periods during a lecture and examine the impact on students’ conceptual understanding of core biology concepts. Whereas most prior research on student attention relies on self-reports, this project will explore and validate neural indicators of attention, measured with electroencephalography (EEG), which can be collected continuously without interfering with naturally occurring learning activities. Finally, the project will explore an innovative method of engaging undergraduate biology students as study participants as well as investigators of their own attentional states and associated brain activity. Taken together, this project will propel research both in the cognitive sciences and in discipline-based education research and inform the design of learning experiences in undergraduate science education that leverage attention fluctuations. The project is funded as a CAREER award from the EHR Core Research (ECR) program, which supports work that advances the fundamental research literature on STEM learning, with co-funding from the Perception, Action, Cognition (PAC) program in SBE. Little is known about internal attention and how it can support or hinder student learning. Some Discipline-Based Education Researchers (DBER) and practitioners have proposed that providing students with opportunities to focus their attention internally during a lecture (such as the first step in the “think-pair-share” classroom activity) can promote student learning, but the results of cognitive science research on the topic has been inconclusive. Some studies link internally-generated thoughts to poor learning, whereas other studies have linked it to increased creativity, problem solving, and goal setting. The current project will attempt to reconcile this divide in the literature by: (1) investigating how internal attention (“thinking”) periods during a biology lecture can promote or hinder student learning by manipulating the frequency and duration of these periods as well as the guidance provided to students; (2) identifying neural signatures of attention fluctuations during biology learning using EEG (more specifically, EEG spectral power and brain-to-brain synchrony across students at different frequency bands will be extracted and compared between periods of internal and external attention); and (3) developing Course-based Undergraduate Research Experiences (CURE) in which biology students will investigate their own attention states and associated brain activity with low-cost, portable EEG technology. By investigating student attention using ecologically-valid biology materials and EEG technology that can be later deployed in classrooms, and by developing and disseminating an innovative CURE, the project will contribute to undergraduate STEM education and pave the way for future DBER-cognitive science cross-disciplinary research. 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.

Office of Temporary and Disability Assistance (OTDA)

Funded through the federal Temporary Assistance for Needy Families (TANF) block grant, the Career Pathways program links educational and occupational training to subsequent employment through educational programs. Training is combined with support services to enable eligible participants to advance both to higher levels of education and to higher wage jobs in targeted occupational sectors.

Department of Health

To address statewide workforce shortages, the CPT program will fund education and participant support services to provide holistic educational and professional placement supports for those newly entering the workforce and those seeking to advance in their careers. Participants will make a three-year commitment of service, in the new professional title, to Medicaid providers that serve at least 30 percent Medicaid members and/or uninsured individuals.

Office for People With Developmental Disabilities

This training is directly delivered to people with I/DD to increase specific skills needed for employment. There is a gap in Medicaid Waiver services, prevocational services can only teach work-related, non-task specific skills. CSVT was developed to address this gap to teach people task specific skills related to specific industries to meet today's workforce and business labor needs.

National Institutes of Health

Career Transition Award for NINDS Intramural Clinician-Scientists (K22 Clinical Trial Not Allowed)

National Institutes of Health

Career Transition Award for NINDS Intramural Clinician-Scientists (K22 Clinical Trial Required)

NSF

Climate risks impact food supply chains. As the geographic separation grows between food production and consumption, food supply chains are increasingly exposed to local and distant climatic disruptions. Current research has focused mostly on the production of a few staple crops, leaving substantial gaps in understanding domestic and international climate risks to an array of U.S. food supply chains. This project will pioneer new approaches to systematically detect, attribute, and track individual climate disruptions to food production along the entire food supply chain to locations of consumption in the U,S. Research findings will increase national capabilities to identify and track climate-associated food supply chain disruptions, thereby supporting investments and policies to enhance the resilience and competitiveness of the U.S. economy and protecting national food security against climate-related disruptions. The project will support a comprehensive education plan to achieve widespread reach at K-12, undergraduate, and graduate levels and local, state, and national scales. The overarching question addressed by this project is: How, where, and to what extent do climate-related disruptions to food production travel through domestic and international supply chains to affect U.S. food supply? This project aims to create a quantitative framework to detect, track, and attribute climate disruptions throughout the entire food supply chain and the full food basket. The central hypothesis is that specific combinations of food items, climate extremes, and supply chain structures can collectively undermine the resilience of food supply chains. This research will systematically quantify climate risks and identify opportunities to enhance resilience through three objectives: i) measure climate vulnerability of U.S. crop production, ii) characterize national and global food trade networks, and iii) identify opportunities to reduce climate vulnerabilities. These activities will integrate with the project’s education goal to enhance understanding of sustainability and resilience through systems thinking. This plan includes developing science modules for middle and high school students and creating summer research opportunities for high school and undergraduate students, aiming to increase interest in STEM careers and interdisciplinary collaboration. This project is jointly funded by the ENG/CBET Environmental Sustainability program and the Established Program to Stimulate Competitive Research (EPSCoR). 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.

NSF

Funded by the Chemical Mechanism, Function, and Properties Program of the Chemistry Division, Professor Xiaoran Hu of the Department of Chemistry at Syracuse University is developing a new class of force-sensitive molecules (called mechanophores) that respond to mechanical forces by changing their stereochemical structures, leading to measurable or observable changes in material properties (e.g., color changes). The proposed research could establish “force-stereochemistry coupling” as a foundational mechanism for designing highly sensitive molecular force probes, with the most mechanosensitive structures having the potential to significantly enhance sensitivity of irreversible mechanophores and enable the study of previously unobservable nanoscale mechanical behaviors. These high-sensitivity force-sensing molecules could facilitate our understanding of nanoscale mechanical behaviors across scientific disciplines ranging from polymer physics to mechanobiology. Moreover, covalent doping of mechanosensitive structures in polymers holds promise for enhancing sustainability by enabling plastics to autonomously monitor and report mechanical damage, thereby enhancing safety and reducing the need for unnecessary preventive replacements. The proposed research is also integrated with innovative and interactive outreach and education activities. Outreach initiatives at the local museum and a local high school will educate a broad audience about smart responsive materials and their applications, enhancing scientific literacy and curiosity about cutting-edge science. Additionally, the proposed research will create research opportunities for graduate, undergraduate, and high-school students, directly contributing to training the next generation of scientists and promoting careers in STEM. This CAREER project could advance the fundamental understanding of mechanochemical reactivities by systematically investigating a mechanistically distinct type of noncovalent-yet-chemical force-matter interactions—specifically, force-triggered stereochemical conversion in atropisomeric mechanophores. It could establish “force-stereochemistry coupling” as a general strategy for designing highly sensitive mechanochemical transformations and could develop comprehensive structure-property relationships elucidating the effects of regiochemistry, “lever-arm” length, and steric factors on mechanically induced stereochemical conversions. Rational variation of mechanophore structures to exhibit diverse readout modes—including changes in color, circular dichroism, and fluorescence—could establish a framework for tailoring these structures for advanced signal analysis techniques, paving the way for applying the “force-stereochemistry coupling” mechanism in high-sensitivity force sensing across complex environments. 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.

NSF

In prokaryotes, the expression of clusters of genes, typically functionally related, often is controlled by a single regulatory sequence leading to a single transcript molecule that encodes multiple proteins. In contrast, in eukaryotes, each nuclear gene has its own regulatory sequence, and each gene leads to a separate transcript encoding a single protein. A few organisms in the animal kingdom show exceptions to this paradigm, and transcripts encoding multiple proteins have recently been found in green algae, which are related to land plants. This suggests that the expression of clusters of genes resulting in transcripts encoding multiple proteins might be more common in the plant kingdom than previously thought. This project uses DNA and RNA sequencing, bioinformatics approaches, and a combination of in vitro and in vitro experiments to explore the presence and functional significance of transcripts encoding multiple proteins among species in the plant kingdom. Plants are the primary producers on our planet, and a better understanding of the mechanisms they employ for gene expression, may enable new approaches for plant breeding. The research program will be integrated with educational and outreach activities designed to engage learners of all ages and increase exposure to STEM disciplines. The education-by-research component of this project will help inspire and train the next generation of scientists. Recent studies have provided evidence of polycistronic gene expression in diverse green alga, highlighting the evolutionary conservation of polycistronic mRNAs among Chlorophytes. Polycistronic mRNAs in land plants have often been regarded as the result of accidental read-through, and little research has studied the prevalence of polycistronic gene expression across Archaeplastida. This CAREER project aims to determine how widespread polycistronic expression is in Archaeplastida and investigate its functional and evolutionary significance. Research activities will (1) generate a map of the polycistron landscape of key Archaeplastida species using long-read sequencing platforms for whole genome sequencing and poly(A)-tailed RNA sequencing, along with database mining and bioinformatic approaches; (2) study the evolutionary dynamics of polycistronic transcripts through ribosome deep sequencing to assess active translation; and (3) investigate the mechanisms of polycistron translation using in vivo and in vitro assays. These experiments will test the hypothesis that polycistrons are evolutionarily conserved at the structural and functional levels within Archaeplastida. The project integrates an education and outreach program, which includes Science Discussion Seminars and training in all aspects of research. It also includes science demonstrations, hands-on activities, and the development of educational materials for K-12 students and the public. A central product of these efforts will be a public database documenting polycistronic sequences across Archaeplastida species. The outcomes of this research are expected to significantly advance our understanding of the evolutionary dynamics of gene organization and expression in Archaeplastida and shed light on the broader implications of polycistronic gene regulation. 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.

NSF

This is a project in model theory, a branch of mathematical logic that studies the features of mathematical structures that can be expressed by formal languages. Model theory usually studies infinite structures, though, within model theory, there are techniques for taking a collection of finite structures--graphs, orders, groups, etc.--and producing an infinite limit structure that encodes useful information about the finite structures. For example, there are Fraïssé limits, like the Rado graph or the rational order, which allow for the study of all finite graphs and orders via their rich collection of symmetries, and ultraproducts, which are logical limits that reflect the asymptotic features of a sequence of structures detected by first order logic. Some of the most striking developments in model theory concern the 'smoothly approximable' structures, a special class of structures that can be seen simultaneously as both kinds of limit, which are built out of classical geometries over finite fields and have deep connections to combinatorics and group theory. The geometries that form their basic building blocks also can be considered over infinite fields and are important objects of study in several areas of mathematics. As with the infinite limits of finite structures, model-theoretic techniques allow for the construction of infinite-dimensional limits of these (infinite but) finite-dimensional geometries and new tools are needed to understand them. This project is focused on the development of those tools. The educational component involves organization of a summer school at University of Notre Dame and working with graduate students. In addition, the PI plans to work with students in several countries where access to high level math education is more limited. The PI's research agenda will develop a structure theory for NSOP_1 theories, pushing beyond the reaches of simplicity theory and enabling the treatment of smoothly approximable structures over infinite fields. The name NSOP_1 is an unfortunate label for a very natural class of theories, serving as a kind of 'compactification' of the simple theories and encompassing many important examples. The PI has proposed a two-part program. The first part would develop the 'geometric' aspects of the theory, by analogy with geometric stability theory, with a focus on definable groups and higher amalgamation. The second part proposes a far-reaching program to expand the reach of the existing structure theory for smoothly approximable structures developed, in its ultimate form, by Cherlin and Hrushovski. In that work, group theory, via work of Kantor, Liebeck, and Macpherson, provided a catalogue of the basic building blocks and classification theory, via the then-nascent theory of simple theories, explained how a smoothly approximable structure is assembled out of these basic pieces. For the analogue over infinite fields, there are significant questions both on the group-theoretic side, concerning the basic geometries, and the classification-theoretic side, concerning a 'geometric' theory of NSOP_1 theories. The PI aims to develop the analysis of geometries in two distinct regimes: the algebraic and the pseudo-finite. This will enable meaningful applications to algebra, representation theory, and combinatorics, particularly to groups of finite Morley rank and to the asymptotic behavior of finite permutation groups. 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.

NSF

The service sector contributes over 70 percent to the nation's Gross Domestic Product (GDP) and engages a workforce of more than 130 million. This Faculty Early Career Development Program (CAREER) grant will support research that attempts to contribute to the progress of science and the advancement of national prosperity and welfare by introducing and analyzing stochastic models at the level of interactions between customers and service providers, rather than via traditional system-level models. By capturing micro-level details that were previously overlooked, this project intends to address novel and societally important operational problems in the service sector, offering to recover wasted time and improve customer outcomes without additional resources. This project aims to improve managerial decision making, introduce new operational problems, and generally open novel avenues of analysis specifically for services. In its education plan, the award aims to expose students to modeling and quantitative reasoning through the development and dissemination of instructor-facing tools for integrating artificial intelligence and data science in undergraduate business education. As undergraduate business students comprise an extensive and representative population of future team members and leaders of the Nation's commercial enterprises, their understanding, involvement, and support of modeling can improve the overall quantitative literacy of the workforce and public, advancing the scientific and industrial competitiveness of the nation. Towards these aims, the research objectives of this award are to establish and analyze stochastic models of service at the level of interactions. In traditional queuing theory, a service time is viewed as a realization of a single random variable. However, service interaction data reveals that this perspective is too coarse: it overlooks the facts that (i) each service interaction is composed by a unique collection of multiple contributions, and (ii) these contributions come from two distinct and interacting sides, the customer and the service agent. This project’s interaction framework views service time not as a single random variable, but rather as a finite point process (modeled initially as a Hawkes process) of contributions driven by its history. The random variable describing service duration can arise in two different ways: either at the moment of the final contribution (if the service closes “naturally”) or at a stopping time on the filtration of the point process (if the service must be closed “systematically”). By moving from the prevailing macro-level perspective to the micro, these interaction models will identify and solve a new class of control and design problems and also re-contextualize classic approaches to these managerial decisions. 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.

NSF

This Faculty Early Career Development Program (CAREER) grant will contribute to scientific progress and to the advancement of national prosperity and welfare by supporting research on efficient scalable methods for characterizing and optimizing large-scale non-stationary stochastic systems, as well as modeling and predicting stochastic processes with time-dependent inputs. These problems arise in a wide array of critical applications, including environmental surveillance, personalized healthcare, and manufacturing process control. Under this award, the target application focuses on ensuring effective and efficient use of mental health resources on university campuses. This award pursues a fundamental understanding of stochastic systems that can be more effectively characterized through a novel analytical–computational approach. The accompanying educational plan aims to improve the academic success of students with challenges and to increase the engagement of students in STEM education through research integration, mentorship, and dedicated fast-track programs. This research seeks to establish an innovative Fourier-based framework to accurately characterize the transient behavior of large-scale non-stationary stochastic systems. The methodology leverages Euler’s formula to efficiently describe system behavior through a condensed form of the Chapman–Kolmogorov forward equations. A major advantage of this approach is that it incorporates crucial gradient information, which will be embedded in a gradient-based method, thereby enabling efficient optimization of complex stochastic systems. Additionally, this research looks to introduce a novel class of Gaussian process regression techniques that integrate time-varying covariates, facilitating the analysis of complex, temporally correlated data to predict student outcomes under different treatments. This effort will be complemented by a Bayesian optimization-based active learning framework aimed at constructing maximally effective treatments through a novel epsilon-optimal spatial branching scheme grounded in a new class of efficient convex underestimators. Together, these advancements intend to be used to expand students’ access to critical mental health services and to design treatment plans tailored to each student’s specific needs. The performance assessments of the project activities will be informed by data obtained through collaborations with a large-scale network of college counseling centers. 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.

NSF

This project will address an important and long-standing knowledge gap by exploring the functional relevance of rudimentary locomotor structures in birds. Over a 500-million-year history, animals have invaded dramatically different environments, acquiring new body plans and modes of locomotion. Theory can explain how these body plans work in their fully developed states, but if structures evolve slowly, through incremental, adaptive (beneficial) stages, how do organisms acquire new and complex structures that seem to be useful only in their fully assembled forms? In other words, what is the advantage of half a wing or only part of an eye? This “dilemma” of incipient or rudimentary stages has long troubled evolutionary biologists but is key to understanding the history of life. Rudimentary stages are less studied, but equally relevant, among developing organisms and animals with vestigial features, which navigate environments using underdeveloped structures that not only lack specializations, but often resemble features of extinct relatives. This project will address the dilemma of incipient stages by comprehensively exploring the functional relevance of rudimentary locomotor structures in three groups of birds: immature birds with developing wings, adult birds with reduced wings, and extinct birds with incipient “protowings.” The research will empower young scientists by (i) equipping participating students for careers in STEM at an institution where approximately one-third of the students are first-generation college students and more than half are women, (ii) incorporating research into classes and expanding a campus museum, and (iii) sharing the importance of rudimentary structures in a children’s book that imparts an encouraging and relatable lesson. How do rudimentary versions of highly specialized structures function? This research couples field observations with biomechanical analyses and musculoskeletal modeling to explore the gain and loss of wings on three time scales: (1) Incipient wings in extant developing birds. Most immature birds have small, “dinosaur-like” wings, which are vital to survival in waterfowl and gamebirds and likely play a key but diverse role in development. This project will examine how locomotor development (morphology, performance, behavior) in a wing-dependent long-distance flyer differs from that of a previously-studied, more leg-dependent, burst flyer. (2) Reduced wings in extant birds. Rudimentary flight apparatuses also occur in birds that are secondarily (semi)flightless or temporarily so due to molt or injury, and seem to provide important but under-appreciated contributions to locomotion. This project will quantify morphology and performance during a secondary loss of flight in waterfowl, and document behavior in zoo birds with small wings. (3) Rudimentary “wings” in extinct dinosaurs. The evolution of bird flight is preserved by dinosaur fossils with “protowings,” which are challenging to decipher but integral to understanding the avian body plan. This project will use biomechanical relationships established in living birds to investigate locomotor potential in an extinct dinosaur (Archaeopteryx). Collectively, this research will address an important knowledge gap, foster cross-disciplinary interactions, and train students at the undergraduate, Master’s and postdoctoral levels. In conjunction with the educational activities above, this work can help inspire a wide array of future scientists to creatively explore the development and evolution of body plans along the tree of life. 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.

NSF

Plastic pollution is a ubiquitous issue impacting the health of marine ecosystems worldwide. Yet, critical knowledge gaps surrounding the fate and transport of plastic once it enters the ocean impede remediation and prevention efforts. Predicting transport is difficult for any particle in the ocean, but microplastics present a particular challenge because their size and density fall outside the regimes of traditionally studied environmental particles such as low-density bubbles and high-density sediment. Thus, the goal of this award is to (1) observe plastic particles in realistic ocean flows recreated in a controlled laboratory setting, and (2) use the observations to build a modelling framework that describes the vertical mixing and transport of plastic at the ocean surface. By examining fundamental interactions between particles and ocean flows, this award will enable more accurate modelling of buoyant particles at the ocean surface. The investigator will partner with local beach-cleanup organizations to share this work through outreach events. This award will additionally support educational activities for both undergraduate and graduate students. The ocean surface boundary layer is a multiphase flow forced by the overlying wind, which generates waves and turbulence. These processes affect the transport and mixing of materials ranging from nutrients and pollutants to bubbles and organisms. Microplastics are a new environmental particle that is increasing in concentration at the ocean surface; yet, predicting the behavior of microplastics is non-trivial because it depends on poorly understood interactions among waves, turbulence, and particle inertia in the ocean surface boundary layer (where most microplastics reside). To help close this foundational knowledge gap in particle-laden flows and improve microplastics transport modeling, this award seeks to describe how the rise velocity of buoyant particles is modulated by the combined effects of waves and turbulence, the processes by which particles are entrained beneath the surface, and the controls on particle diffusivity in waves and turbulence. The research will be conducted in a laboratory facility that can generate both wind and waves. Advanced optical techniques will be used to track particles both at the surface and beneath it under a range of surface conditions, including breaking waves. These observations will be used to develop a wave phase-resolved model for the transport and mixing of buoyant particles in a free-surface boundary layer, a model which will be broadly applicable to microplastics, and other particles found at the ocean surface. 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.

NSF

Data protection regulations specify how personal data must be protected when used by others. Tracking and accounting for protections of data privacy has emerged as a pivotal requirement in contemporary data protection regulations. As a result, those who are responsible for using personal data, so-called data controllers, must actively enhance the privacy safeguards they provide. This project addresses the intricate challenges surrounding privacy accountability within the mobile software ecosystem, characterized by the opacity of third-party code modules, particularly third-party libraries. Existing methods for achieving privacy accountability primarily emphasize data transparency, often overlooking essential principles like data minimization and purpose limitation and facing integration challenges within mobile software development lifecycles. This research project seeks to address these limitations by presenting innovative approaches to enforce privacy accountability throughout the mobile software development process. The goal is to establish a more privacy-conscious and accountable mobile ecosystem, benefiting both users and data controllers. The outcomes of the research will contribute to educational curriculum and training to help developers achieve privacy goals plus additional outreach through workshop and bootcamp venues. The project's technical objectives are divided into three research thrusts: (1) understanding privacy accountability challenges in the mobile third-party code modules; (2) designing a privacy-accountable disclosure framework; (3) continuously enforcing privacy accountability properties in mobile software development lifecycle. The technical contribution of this research lies in advancing the socio-technical understanding of privacy non-compliance risks and accountability challenges within the mobile software supply chain. Additionally, it involves designing novel technical foundations that seamlessly integrate various methodologies and disciplines. This includes program analysis, formal methods, natural language processing, and human subject research, culminating in a privacy-accountable disclosure framework and continuous privacy accountability enforcement mechanism. These innovations are designed to be easily adoptable within the mobile software supply chain. The research will foster a holistic approach to enhancing privacy protection and accountability in mobile software development lifecycle and contribute to the creation of a safer and more privacy-conscious mobile ecosystem. 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.

NSF

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The research will develop methods by which to design and evaluate information and communication technology (ICT) measurement efforts such that deployment efforts can be held accountable to the citizens they are intended to serve. While there have been several federal efforts to increase the deployment of broadband infrastructure across the United States, many communities seem to be persistently excluded from the impacts of these deployment efforts. It can be especially challenging to quantify these inequities for mobile broadband technologies. Currently, government subsidized broadband deployment efforts rely on provider-generated coverage maps, which use radio propagation models to demonstrate where mobile broadband is available across space. Ad hoc investigations of these maps have revealed that propagation models substantially overstate the penetration of ICT infrastructure and point to a need for widespread measurements of ICT infrastructure to better assess the true extent of coverage. As ICT infrastructure becomes increasingly necessary for essential services such as education, healthcare, banking, and civic participation, accurate assessment of ICT infrastructure accessibility becomes essential. To measure ICT infrastructure at scale and in a manner that illuminates inequities, it is necessary to rely on crowdsourced measurements of ICT infrastructure. This research seeks to answer two fundamental questions that drive ICT infrastructure policymaking and deployments: How do we know that ICT infrastructure deployment efforts are actually improving access to ICT infrastructure and how should crowdsourcing platforms for ICT measurements be designed in order to ensure that representative data are generated by crowdsourced ICT measurement efforts? Research activities will seek answers in three successive strands: (1) an ethnographic investigation of conceptualizations around ICT measurement and mapping; (2) the design and implementation of an innovative measurement platform; and, (3) the deployment and evaluation of the measurement platform. Meta-analysis of data collected in each of these research strands will culminate in a transferable theory of design for inclusive crowdsourced ICT measurements. Moreover, education activities will work synergistically with the research to broaden participation in computing through inclusive problem-based curricular design and mentoring students through mixed-methods sociotechnical research approaches. 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.

NSF

Biodiversity is vital to the economy and future innovation opportunities, and we are currently witnessing an unprecedented loss of biodiversity. To better understand and hopefully mitigate this loss, networks of ground-level sensors, satellites, drones, and community scientists are deployed to collect natural-world data at unprecedented scales. There is valuable scientific information stored in these raw data, the vast majority of which are as-yet inaccessible due to the time and resources needed to process the data by small groups of relevant human experts. Computer vision (CV) will prove crucial to facilitate efficient extraction of scientific insights from quickly growing repositories of natural world imagery, but in order to realize the goal of global-scale, near-real-time biodiversity monitoring we must develop computer vision approaches keyed to challenges encountered in real world settings. This work formalizes and addresses cross-cutting limitations of current CV methods in the context of global-scale biodiversity monitoring, characterized in the following three research aims: (Aim 1) Robustly identify rare, visually similar, and even novel categories, all challenges separately for CV that co-occur in biodiversity data. We address this compounding challenge by augmenting limited training data and developing efficient active curation systems. (Aim 2) Adapt to new deployments and identify valuable data for specialized tasks. Biodiversity is non-uniformly distributed across the globe, and specialized models improve decision support. We introduce task-specific data selection as a specialization mechanism, and develop methods that adapt to new deployments over time while making optimal use of human effort. (Aim 3:) Share information and reason across modalities to fill data gaps and support scientific discovery. No single modality of biodiversity data captures the entire picture. We will develop data encoders that aggregate and share relevant information across modalities, and build on these encoders by developing interactive scientific AI agents that enable novel discoveries in data. These three research aims will be complemented by the development of cross-disciplinary educational programs that expand capacity at the intersection of CV and Ecology. Our proposed research innovations are necessary to enable robust, scalable CV deployments in ecological settings. Each aim outlined above will contribute towards our ability to deploy models with conservation partners and enable critical computer science/ecology collaborations. This combined effort will not only increase the utility of existing data and methods in the biodiversity domain, but will lead to advances in related application areas (including biology, astronomy, and neuroscience) as well as fundamental CV research challenges. 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.

NSF

Cognitive flexibility, or the ability to make different decisions to meet ever-changing demands from the environment, is essential for the survival of humans and other animals. To support this key ability, the brain needs to reorganize its activity patterns depending on the current behavioral context, but we still understand little about how this functional reorganization is accomplished. This project will investigate this fundamental question by asking how multiple mouse brain regions act in concert to support task switching, a common type of flexible cognitive behavior. Beyond leading to better understanding of basic brain mechanisms, this research could inform future work that aims to treat cognitive inflexibility, which is pervasive in brain disorders like autism, schizophrenia, and dementia. The project will also have other broader societal impacts. First, it will provide numerous opportunities for undergraduate and graduate education in neuroscience. Second, it will include outreach to schoolteachers and children to raise awareness about the detriments of excessive task switching, which has vastly increased with pervasive digital technologies like smartphones and particularly affects learning in school-aged children. The central goal of this proposal is to contrast two competing models of how the brain reorganizes its activity patterns task dependently: in the intracortical model, task-specific activity is fully generated within the cerebral cortex. In the external input model, extra-cortical input is required to actively reorganize cortical activity, enabling new cognitive computations. Based on preliminary studies, the hypothesis is that a loop between the prefrontal cortex, the cholinergic basal forebrain and the rest of the cortex is a key multi-region circuit that enables the generation of cortex-wide activity patterns depending on cognitive demands, compatible with the external input model. The project will use mice as the model organism due to their unique combination of sufficiently complex cognitive behavior and availability of experimental tools. It will leverage a new behavioral paradigm for head-fixed mice making navigational decisions in virtual reality, in which they switch between a simple and a complex task dozens of times within a behavioral session. This paradigm will be combined with cutting-edge genetic and optical tools to record and perturb different nodes of the hypothesized circuit loop, with cell-type and projection specificity. Further, statistical and mechanistic modeling will be employed to link neural activity patterns to task-specific behavior and extract generalizable computational principles from this specific circuit. Thus, this project will bring together recent developments in behavioral, computational, and circuit-dissection technologies to gain mechanistic insight into task switching, a canonical form of flexible cognitive behavior. 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.

NSF

Robotic systems increasingly operate in critical sectors such as manufacturing, healthcare, agriculture, and defense, where security and reliability are essential. These systems combine components from the computational and physical domains that interact closely with one another, creating complex interdependencies and vulnerabilities. Traditional security approaches focus on either the computational or physical domain in isolation, leaving significant gaps in protection. This project aims to address these challenges by creating a comprehensive security framework that models and protects the intricate dependencies between computational and physical components. The project's novelties are in its cross-domain approach to vulnerability analysis, real-time mitigation, and forensic investigation. The project's broader significance and importance are in improving the safety and trustworthiness of robotic systems in high-stakes environments, advancing cybersecurity education, and broadening participation in computing through outreach and hands-on learning. This project develops a cross-domain security framework that systematically mitigates vulnerabilities in robotic systems by integrating advanced modeling, real-time detection, and post-attack analysis. The research consists of three technical thrusts. The first thrust focuses on vulnerability analysis by constructing unified models that capture the interactions between computational and physical components, using static analysis, system identification, and stateful fuzzing to detect cross-domain weaknesses. The second thrust designs real-time attack mitigation methods by leveraging these models for predictive state monitoring, enabling early detection of discrepancies between expected and actual system behavior across both domains, and facilitating recovery from corrupted physical states. The third thrust introduces a post-attack investigation technique that applies deterministic replay and causal inference to trace the root causes of attacks through both computational and physical domains. Together, these efforts contribute to a new generation of tools that enhance the resilience of robotic systems. Results from the project will improve the development and deployment of secure robotic applications, influence cybersecurity education, and foster public trust in robotic technologies. 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.

NSF

To use a web service today, Internet users must often reveal their private information to the web-service provider. For example, Internet users upload their photographs to online photo albums, reveal their interests to web-search engines, and disclose their favorite websites to Internet service providers. Sending sensitive data to web-service providers is a serious privacy risk: the provider could lose the user's data in a data breach, decide to sell it later on, or be forced to disclose it to a foreign government. At the same time, web services are indispensable. Thus, computer users currently have no choice but to hand over their sensitive data to web-service providers and to suffer the accompanying privacy risks. This project will develop a new suite of privacy-protecting web services that never see or process any unencrypted user data. This project's goal is to make it possible for everyday Internet users to enjoy the tremendous benefits of today's web while shielding them from the accompanying privacy risks. In addition, the educational aspects of the project will focus on the development of undergraduate content and an openly available textbook to teach security and systems in tandem. This project consists of three parts, each dedicated to the development of a different private web service. The first part focuses on private machine-learning inference: allowing a client to evaluate a large server-side machine-learning model on its private data (e.g., the client's photos) while revealing no information about the client's private input data to the machine-learning service. The second part focuses on private search: allowing a client to search over a server-side corpus of billions of documents (e.g., web pages) while revealing no information about its search query to the search engine's servers. The third part focuses on private web browsing: allowing a client to browse a web of hundreds of millions of text-based pages while revealing no information about which pages it is reading. Building each of these three private web services will require new technical tools. In particular, this project will develop a suite of new low-level cryptographic primitives, including new high-speed protocols for private matrix multiplication, new protocols for private nearest-neighbor search in high-dimensional vector spaces, and a new cryptographic primitive, distributional private information retrieval, which allows a client to privately fetch data from a remote database server at relatively low cost. 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.