University Of Massachusetts Amherst

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY Cell division is a physical process when the genome - distributed across the replicated chromosomes - is equally separated prior to cleavage into two daughter cells. Given its central importance to life, integrated physical and biochemical fail-safe mechanisms have evolved to make cell division a high-fidelity process. However, natural selection does not yield perfection and failures in cell division result in aneuploidy, which causes a majority of miscarriages in the first trimester, birth defects, and has been implicated in tumorigenesis and cancer metastasis. Our long-term goal is to characterize fundamental and conserved mechano-molecular mechanisms of cell divi- sion. In the proposed work, we will combine advanced live-cell imaging approaches with molecular and biochem- ical methods to advance two areas of inquiry central to successful cell division. First, we will investigate the functional contributions of extremely large intrinsically disordered regions (ELIDRs) in proteins at the kinetochore and perichromosomal layer. Second, we will study how a phenomenon called branching microtubule nucleation is regulated to promote efficient and timely capture of chromosomes by spindle microtubules. A more compre- hensive understanding of how the complex biological processes of cell division are orchestrated by mechanical and biochemical pathways will be foundational to treating diseases stemming from improper cell division.

NIH Research Projects · FY 2026 · 2025-01

Project Summary Early intervention is crucial to improve academic and social outcomes for children at risk for language impairment. Intervening in the early stages of development is key to taking advantage of brain plasticity, which is more effective in early childhood. Coaching caregivers to use language facilitation strategies with their children has been documented as an effective approach to support children’s communication development. It has been shown that caregivers can be taught general and specific language support strategies, which appears to be associated with substantial language growth in young children. Hence, caregivers play a critical role in their children’s language development and are known as children’s first language teachers. However, the majority of this research has been conducted with White, non-Hispanic families, limiting equitable access to culturally and linguistically appropriate caregiver-implemented early language interventions for Latine children. We propose to conduct formative research to gather key information about current early intervention practices used by speech-language providers and early interactions between caregivers and their children with language delays in Puerto Rico. This study uses a mixed-methods approach to examine the use of coaching practices by speech-language providers and the use of naturalistic language facilitation strategies by caregivers in Puerto Rico, with the ultimate goal of developing or adapting a caregiver-implemented early language intervention that could benefit this population. We will characterize the use of naturalistic language facilitation strategies by caregivers and the factors that influence the use of these strategies (Aim 1), examine the use of language facilitation strategies and coaching practices by speech- language providers, and identify the factors that influence the use of coaching practices (Aim 2) and describe the perspectives of speech-language providers and caregivers about caregiver-implemented early language interventions (Aim 3). The long-term goal of this study is to use this formative research to inform the selection and adaptation of a caregiver-implemented early language intervention that responds to the cultural and linguistic needs of Puerto Rican families and speech-language providers. This innovative proposal brings a much-needed focus to the needs of underserved and historically marginalized families in Puerto Rico. This project will have a significant impact because this formative research will be the first step towards adapting and implementing the first culturally and linguistically responsive caregiver-implemented early language intervention for families in Puerto Rico.

NSF Awards · FY 2025 · 2025-01

The past few decades have seen tremendous advances in our understanding of galaxy formation and evolution using both observations and detailed computer simulations. However, many key questions are still not fully understood. For example, most massive elliptical galaxies, among the oldest and most massive structures in the universe and hosts of supermassive black holes (SMBH), cease forming stars and transition into passively evolving systems. While the precise mechanisms underpinning such "quenching" are unclear, SMBHs appear to play a key role by suppressing the infall of fresh gas - the raw material of star formation - from the circumgalactic medium (CGM). The PI will conduct a computer simulation of a massive galaxy's formation and evolution with spatial resolution sufficient to resolve the CGM's complex structure and the small-scale physical processes occurring there. This work will inform and complement large-scale cosmological simulations and will provide mentoring and training for three graduate students in cutting edge galaxy simulations. Undergraduate students will also be involved, and the investigator will engage in outreach to local high schools and public planetarium shows. To better understand the evolution of massive galaxies, the investigator and her group will conduct a very high- resolution numerical study using a cosmological-zoom simulation focusing on the formation of a single massive galaxy while following its evolution all the way down to the current epoch (z = 0). The major objectives of this work are to understand (1) how SMBHs form and grow in massive galaxies, (2) precisely how SMBH "feedback" leads to star formation quenching, (3) the structure and evolution the CGM, and (4) how the evolution of satellite galaxies is affected. The team will use the newly developed Enzo-E, which is a highly scalable redesign of the original Enzo code for the exascale era. They will develop a new state-of-the-art SMBH formation/feedback model, which will be added to the publicly available code and shared with the community. The proposed simulations will force enhanced refinement in the galaxy's CGM, providing unprecedented details of this major gas reservoir around massive galaxies, which likely plays a key role in regulating the fueling of both SMBHs and star formation. This research will provide insight into the complex interplay between SMBHs, the CGM, and star formation in a massive galaxy, and it will provide a framework for simulating the effects of (e.g.) cosmic rays, dust, and radiative transfer. 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 Awards · FY 2025 · 2025-01

In order to prevent the immediate conversion of a galaxy’s total gas supply into stars, a feedback mechanism must exist to slow down star formation. One promising candidate is the injection of turbulence into a galaxy’s supply of gas by a super-massive black hole. This research team has developed a technique to measure gas turbulence in systems hosting giant central black holes and will directly evaluate the viability of this mechanism using a large sample of galaxies. The team will work with artists/graphic designers to integrate their work into a series of freely available planetarium shows, mentor under-represented groups in STEM research, support a postdoc and a graduate student, and introduce undergraduates to astronomical research. This project is jointly funded by MPS/AST and the Established Program to Stimulate Competitive Research (EPSCoR). This project will deliver a more complete view of the “feedback” provided by accreting SMBHs, leading to a better understanding of the black hole-host galaxy relation. More specifically, it will directly probe the energetics of the intra-cluster, circum-galactic, and interstellar media of massive early-type galaxies. The origin of turbulence, the coupling between hot and cold gas, and the microscopic physics will be revealed through the gas kinematics of these systems at different mass scales using X-ray (Chandra), visible (VLT/MUSE), and sub-millimeter (ALMA) observations. The innovative technique of studying the velocity structure of multi-phase filaments has the potential to be a more direct and accurate way to probe turbulent gas motion, vis-a-vis X-ray surface brightness fluctuation analysis, resonant scattering analysis, and even the direct X-ray line width measurement with Hitomi. This project will be a pathfinder for the calorimeter-based X-ray observatories of the next generation such as XRISM, Athena-XIFU, and Lynx. 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 Awards · FY 2025 · 2025-01

Halide ion-based electrochemical charge storage systems are attracting immense attention due to their high theoretical energy densities, low flammability and low risk of metal dendrite formation, the promise of local component sourcing, and their unique utility as biocompatible power sources. Polymer-based electrodes and electrolytes have the potential to yield high energy density halide ion batteries, however these polymer materials are in their infancy, and design guidelines to create optimal materials are currently unknown. The goal of this project is to apply the polymer growth and vapor-processing advancements made in investigator's lab to develop competitive polymer electrodes and electrolytes for halide ion batteries. This work will produce experimentally validated guidelines for optimal polymer electrode and electrolyte materials that will broadly inform materials and device development endeavors in the electrochemical systems community. This project will provide education and training to graduate students engaged in Ph.D. research, undergraduates gaining their first research experiences, and community college students participating in an internship program that increases opportunities in STEM fields.   In their present iteration, polymer-based electrodes and electrolytes have not yet afforded sufficiently high chloride storage densities and conductivities, and design rules for accessing optimal materials have not been established. The overarching hypothesis of this effort is that the real-time composition, morphology and porosity control afforded by polymer chemical vapor deposition (CVD) will yield conductive polymer electrodes with a high volumetric density of accessible chloride-storage sites and halide-conducting gel/membrane electrolytes with high ionic conductivities. Different facets of this hypothesis are explored in each aim: (1) the advantage of using oxidative chemical vapor deposition (oCVD) to create thick and conductive chloride-storing electrodes with high volumetric chloride storage capacities is explored; (2) the ability of the photoinitiated chemical vapor deposition (piCVD) process to create gel and solid-membrane electrolytes with high chloride conductivity via controlled mesh sizes will be experimentally proven; (3) the compatibility of the reaction trajectories/deposition chemistries used in oCVD and piCVD with fluoride anion salts will be explored to develop materials for fluoride-ion batteries. The project plans span process research, and thin-film and electrochemical characterization efforts, complemented and accelerated by collaborative efforts to apply machine learning algorithms to predict competitive electrode structures. 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 Awards · FY 2024 · 2024-11

Cuscuta (common name: dodder; Convolvulaceae) is a diverse genus of parasitic plants that causes major crop losses across the US and the globe. While dodder that are major pests attack a wide range of host species, many show host preferences, and dodder growth varies substantially across hosts. This apparent host preference suggests some dodder genotypes and populations are adapted to a particular set of hosts. Understanding the genetic mechanisms of these host preferences could help farmers in their battle against dodder. The central biological question of this project is: how do agriculturally relevant Cuscuta species successfully parasitize a wide range of hosts? The research team will study variation in DNA sequence and gene expression across diverse dodder populations across diverse host species to answer this question. Additionally, the team will pursue agronomic research, extension, and outreach as a part of broader impacts. In particular, the team will identify dodder-resistant cultivars of blueberries and determine the role of over-wintering of dodder in driving subsequent year infestations. Additionally, lessons in plant biology and research projects will be developed with blind and visually impaired and deaf and hard of hearing students. The research team will address the central question, how do agriculturally relevant Cuscuta species successfully parasitize a wide range of hosts, with three main aims: 1) profiling the diversity of trans-species miRNAs across Cuscuta populations and identifying their host mRNA targets, 2) resequencing Cuscuta genomes to characterize population genetic processes affecting miRNA diversity and host-specificity, and 3) determining how genetic variation in Cuscuta response to hosts is driven by gene expression and associated with success of attachments to hosts. The study will cover the two most common species of Cuscuta in the study region of the northeast US, C. campestris and C. gronovii. Small RNA sequencing will be used combined with host transcriptome assemblies to study the diversity of trans-species miRNAs and their targets across the region. Inference of the importance of miRNA variation will be tested using mutants in host mRNA targets. Population genetic inference will be applied to understand what evolutionary processes have shaped diversity in host-responsive genetic loci. Microscopy and RNAseq will be used to study how genetic variation in Cuscuta interacts with different host species to determine attachment success and gene expression. 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 Awards · FY 2024 · 2024-10

Mathematical problems involving matrices, such as systems of linear equations and eigenvalue problems, arise in a huge number of applications across machine learning, statistics, scientific computing, engineering, and beyond. More efficient algorithms for solving these problems can thus have widespread practical impact, significantly accelerating the pace of scientific discovery, decreasing the cost of data analysis, and facilitating simulations of large-scale, complex systems. Developing efficient algorithms for matrix problems is the central goal of the field of numerical linear algebra. Unfortunately, it can often be difficult to understand the limits of algorithmic research. How close are the current best algorithms to being optimal? Are there inherent barriers to developing faster algorithms? This project will tackle these questions by studying fast matrix algorithms through the lens of “query complexity”. Specifically, the goal is to develop algorithms that access, or “query”, the input matrix as few times as possible in order to solve a given problem. Algorithms with small query complexity are frequently efficient in practice, so focusing on query complexity will lead to the development of faster algorithms for applications. At the same time, unlike more general models of computation, it is often possible to prove impossibility results for query complexity, i.e., to show that no algorithm can solve a given problem with too small of query complexity. Thus, the project will improve understanding of the limits of algorithmic research and of the optimality or sub-optimality of existing methods. The work will produce general-purpose algorithmic tools and lower-bound techniques that can be applied to algorithms research at large. The project will also expand the dialogue between research communities, including theoretical computer science, numerical analysis, and quantum computing communities. Concretely, the research team will study linear algebraic computation through the lens of query complexity by 1) developing new, query-efficient algorithms for core matrix problems and 2) proving unconditional lower bounds on the number of queries required to solve these problems. The research will center on two important query models: entrywise matrix queries and matrix-vector product queries. Entrywise queries are perhaps the simplest query model for linear algebraic computation, although the complexity of many basic problems in the model, from eigenvalue approximation to matrix norm computation, remains unresolved. By studying the model, the project will explore broad themes, such as the importance of randomness and query adaptivity, and the power of “augmented” entrywise query models that rise in quantum-inspired numerical linear algebra, spectral graph theory, and beyond. The project’s second focus on matrix-vector product queries is motivated by the fact that matrix-vector products often dominate the runtime of linear algebraic methods in practice. As such, understanding the number of matrix-vector products required to solve central problems like structured matrix approximation, norm approximation, and spectral density estimation is a key goal. The matrix-vector product model generalizes the widely studied matrix sketching and Krylov subspace models, so proving lower bounds in this model will require new techniques. Overall, the project will strengthen the theoretical foundations of computational linear algebra, with the goal of establishing query complexity as a central framework for developing faster algorithms and computational lower bounds. 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 Awards · FY 2024 · 2024-10

This project aims to serve the national interest by creating a sustainable, adaptable framework for fostering critical thinking and inspiring diverse learners in STEM. By bringing together passionate experts with high social impact projects, advanced and introductory students, and students from both theoretical and applied disciplines, the project is designed to bring together several proven approaches to inspire interest, persistence, and success across diverse populations of STEM learners. Advanced students from statistics and applied sciences study advanced statistical methods (4 credits), while meeting weekly with introductory students, who study persuasive data visualization (1 credit). This novel two-course cross-level structure aims to promote sustained interest across the years of undergraduate study and foster critical statistical thinking. The project includes joint course development by faculty from several disciplines, who can then rotate teaching, making the course available to all disciplines and maximizing use of teaching resources. The modular course structure is adaptable and expandable. The project plans to include a public website and annual workshops to more broadly share the approach and teaching materials. The project plans to use substantive topics with social impact, near-peer mentoring, and relationship-rich cooperative learning to foster students' belonging, self-efficacy, and motivation for STEM: the mediators of STEM success and persistence, especially among underrepresented groups. Rich project work in a cooperative cross-disciplinary active learning environment is intended to foster learning of critical statistical thinking beyond rote thinking that is replaceable by generative AI. Faculty professional development and formative evaluation are designed to enhance the intervention and longitudinal mixed methods research will evaluate its effectiveness. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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 Awards · FY 2024 · 2024-10

Retrieval-Enhanced Machine Learning (REML) refers to a subset of machine learning models that make predictions by utilizing the results of one or more retrieval models from collections of documents. REML has recently attracted considerable attention due to its wide range of applications, including knowledge grounding for question answering and improving generalization in large language models. However, REML has mainly been studied from a machine learning perspective, without focusing on the retrieval aspects. Preliminary explorations have demonstrated the importance of retrieval on downstream REML performance. This observation has motivated this project in order to provide an alternative view to REML and study REML from an information retrieval (IR) perspective. In this perspective, the retrieval component in REML is framed as a search engine capable of supporting multiple, independent predictive models, as opposed to a single predictive model as is the case in the majority of existing work. This project consists of three major research thrusts. First, the project will develop novel architectures and optimization solutions that provide information access to multiple machine learning models conducting a wide variety of tasks. Next, the project will study training and inference efficiency in the context of REML by focusing on the utilization of retrieval results by downstream machine learning models and the feedback they provide. Third, the project will study approaches for responsible REML by examining data control for content providers in REML and fairness and robustness across multiple downstream models. Without loss of generality, the project will primarily focus on a number of real-world language tasks, such as open-domain question answering, fact verification, and open-domain dialogue systems. 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 Awards · FY 2024 · 2024-10

The ever-growing diversity of edge devices, from CPUs for basic tasks to graphics processing units (GPUs) for graphics and neural processing units (NPUs) for machine learning, presents a challenge for edge cloud computing. While advanced wireless communication seamlessly connects billions of edge devices to the edge cloud, traditional homogenous edge cloud platforms struggle to handle diverse workloads and computation models efficiently. This research proposes a pioneering approach using Field-Programmable Gate Arrays (FPGAs) within an edge overlay framework. FPGAs can be dynamically reconfigured to act as various processing units, efficiently handling these diverse computational needs. The main goal of this research is to develop novel techniques for offloading heterogeneous tasks, ensuring high overall throughput, uninterrupted service, and fault tolerance. To demonstrate the effectiveness of the proposed techniques, this research will focus on a drone network surveillance use case. The developed approach has the potential to significantly improve edge computing's energy efficiency, resiliency, and scalability. This research will make a significant contribution by making powerful edge cloud computing more accessible. To achieve this, the researchers will develop new course modules at UMass and WPI focused on heterogeneous edge computing, institute a research workshop for sharing research ideas and showcasing work, and leverage targeted programs to recruit underrepresented students to research programs. These initiatives will empower undergraduate and graduate students to leverage edge cloud FPGA resources for various hardware and software experiments. The annual research workshop, organized and executed by graduate students, will be open to the wider community, further expanding the project's reach and impact. All findings, innovations, and developed software from this research will be openly shared to ensure they are freely accessible and usable by the research community, industry partners, and the public, promoting collaboration, further development, and practical applications. 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 Awards · FY 2024 · 2024-10

Stretchable bioelectronic devices are essential for seamless integration with biological tissue, providing substantial benefits for biomedical applications. However, these devices face a fundamental challenge in maintaining their performance over time in the physiological environment. That is, ions from the surrounding environment gradually diffuse into the dielectric encapsulations, causing electrical leakage and degrading insulation over time. This EAGER project aims to develop a non-leaky, stretchable dielectric encapsulation. The research will focus on investigating heterogeneous materials and structures that mimic cell membranes, specifically emulating the bilayer that prevents ion transport. The outcomes of this research could lead to significant advancements in bioelectronic interfaces that are simultaneously stretchable and ion-impermeable, allowing reliable operation under physiological conditions for extended periods (e.g., lifetime). Additionally, this project offers a comprehensive interdisciplinary research and education program for graduate and undergraduate students, covering diverse fields such as interfacial science, mechanics, nanofabrication, materials science, and bioengineering. The fact that ion permeability and material stretchability are inextricably linked at the molecular level imposes a fundamental limit on soft materials’ ability to inhibit ion diffusion. This EAGER project addresses this fundamental limit by creating a bioinspired stretchable ion barrier that emulates the cell’s phospholipid bilayer so as to realize stretchable yet ion-impermeable dielectrics. Using only existing stretchable materials, the research focuses on engineering them to mimic the lipid bilayer and investigate (1) the underlying science mechanism, (2) the mechanical robustness, and (3) the electrical stability of the stretchable ion barrier through theoretical and experimental studies. This research is expected to provide new ideas for reconciling two mutually exclusive material properties through biomimetic engineering designs. In particular, it helps remove the major roadblock for bioelectronics to accurately record and stimulate biological signals in vivo over extremely long durations. 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 Awards · FY 2024 · 2024-10

Graduate students nationwide are often highly motivated to transform academic institutions to become more inclusive and to better foster the success of all students. However, graduate students may lack the knowledge and experience to understand how to effectively lead these changes, both in graduate school and at later points in their careers. In particular, science, technology, engineering, and math (STEM) students do not normally have the opportunity to formally learn these skills. This National Science Foundation Innovations of Graduate Education (IGE) award to the University of Massachusetts Amherst will test a program of inclusive leadership training aimed at providing graduate students with the skills needed to become leaders in fostering institutional change. Results will provide guidance for other institutions for how to better prepare graduate students to develop as effective leaders who emphasize participation, community, and respect across identities without sacrificing their own professional and personal goals. Two cohorts of graduate student Leadership Fellows will each participate for two years. In Year 1, Fellows will learn about academic leadership through a series of interactive workshops and panel discussions. In particular, they will be introduced to the structure of colleges and universities and how change occurs at different organizational levels. At the end of this year, Fellows are predicted to (1a) demonstrate increased knowledge about inclusive leadership in higher education; and (1b) demonstrate increased confidence in their skills as agents of change across their career stages compared to pre-testing and control groups. In Year 2, Leadership Fellows will work in teams on a Leadership Project led by a mentor. Projects will have measurable outcomes for the growth of the Fellows and for the impact on the institution, as each project has been designed to provide value to UMass Amherst. Fellows will also receive individualized sessions with a professional leadership coach. At the end of Year 2, Fellows are predicted to be able to (2a) describe how the lessons learned in Year 1 impacted both their Leadership Project and their work beyond the project and to apply their knowledge to novel case studies; (2b) demonstrate improved leadership skills through both self-evaluation and assessment by others that know them; and (2c) articulate a plan to continue to create institutional change throughout their careers. An additional prediction is that (2d) the Leadership Projects will have met their goals and provided value to the University. Finally, because the program incorporates many forms of support for the Fellows, Fellows should be able to demonstrate similar or improved measures of wellbeing compared to a control group. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to study, pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community. 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 Awards · FY 2024 · 2024-10

The ongoing global pandemic of COVID-19 has reminded us the importance of diagnostic testing technologies. Current point-of-care testing (POCT) technologies are inexpensive and easy to use, store, and transport. However, their simplicity causes a significant loss of sensitivity and specificity comparing to lab based diagnostic tests. The major goal of this NSF CAREER research program is to develop a new nanopore testing technology to enable ultrasensitive detection of infectious diseases at a reasonably low cost. Upon the successful completion of this project, the proposed nanopore test will meet the urgent need for a POCT technology with accuracy that exceeds current POCTs or even lab-based testing technologies. This platform technology presents great potential for changing the current paradigm of POCT for existing infectious diseases. Also, it can be readily modified with minimal optimization as soon as new diseases and biomarkers are identified for rapid deployment in clinics and at the point of care. This project also includes education and outreach activities, such as organizing a summer research workshop and internship for high school students on the fundamentals of Biomedical Engineering and Nanotechnology, broadly presenting our research to local communities, and starting an infectious disease research symposium that brings an additional synergy among local clinicians, biomedical engineers, and scientists. Special attention will be given to students from underrepresented groups and first-generation college students, who constitute a significant part of our student population. This CAREER program should result in achievement awards and authorships on research publications for undergraduate and high school students. In the era of personalized medicine, rapid and accurate quantification of multiple biomarkers at the point-of-care is fundamental to a successful control and management of infectious disease outbreaks. Classical point-of-care testing (POCT) technologies are inexpensive and easy to use, store, and transport. But their simplicity causes a significant loss of sensitivity and specificity comparing to lab based in vitro diagnostics. The nanopore technology is a promising alternative because of its single-molecule analysis capacity, portability, and low cost. However, existing nanopore technologies are not suitable for detecting analytes in complex human samples, can only analyze charged biomarkers of certain sizes, and are not device compatible. The major goal of this CAREER program is to develop a next generation nanopore biosensor to enable ultrasensitive detection and quantification of multiple infectious disease biomarkers from human blood at the point of care. The program has three research objectives: (1) Develop and optimize a nanopore biosensor with unprecedented sensitivity, robustness, and reproducibility for various purposes; (2) Enable sensitive and specific biomarker detection by automated immunoprecipitation and novel signal transduction mechanisms; (3) Demonstrate ultrasensitive multiplex quantification of circulating proteomics biomarkers and its applications in infectious disease diagnosis and prognosis. Proposed approach has several innovative elements: several amplification methods are employed to achieve the sub-femtomolar level detection limit; special single strand DNA structures are used as detection surrogates for biomarkers, so that the readout signal can be easily differentiated from other biomolecules to prevent false-positives; an integrated microfluidic automatic immunoprecipitation module can reduce sample-to-answer time and human errors in the assay protocol. 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.

NIH Research Projects · FY 2024 · 2024-09

Project Summary/Abstract People with aphasia often experience challenges conveying their thoughts to unfamiliar communication partners, which is critical for living independently and building new social connections. This line of research has two long-term objectives: to improve unfamiliar communication partners’ comprehension of people with aphasia, and to support people with aphasia in advocating for their communication needs. This project addresses these goals with a focus on service workers, who interact directly with customers to provide goods, services, or information. Being understood by service workers is often necessary to complete essential tasks such as purchasing food and picking up medication. This project investigates whether service workers comprehend speakers with aphasia more accurately when they have first read an aphasia identification (ID) card. Aphasia ID cards contain written self-advocacy statements that disclose the speaker’s aphasia, define aphasia, and provide guidance on how to communicate successfully. These statements have been shown to improve unfamiliar communication partners’ attitudes (knowledge about aphasia, emotions, and behavior), which improves communication experiences for people with aphasia. This project tests the central hypothesis that, by improving unfamiliar communication partners’ attitudes, aphasia ID cards facilitate key language processes that improve their comprehension of people with aphasia. This randomized controlled trial will enroll 160 service workers who vary with respect to age, gender, race/ethnicity, and occupation. Half of the service workers will view an aphasia ID card for a speaker with aphasia, and half will not. Then, all service workers will complete tasks measuring their attitudes, language processing, and comprehension of service requests (i.e., requests for goods, services, or information) produced by speakers with aphasia. Eye-tracking will be used to measure key language processes that take place while service workers listen to the service requests. The Specific Aims are to investigate how viewing an aphasia ID card affects service workers’ attitudes, language processing, and comprehension when people with aphasia produce long pauses (Aim 1) and paraphasias (word-retrieval errors) (Aim 2). By rigorously investigating the effects of aphasia ID cards on service workers’ attitudes, language processing, and comprehension, this project will contribute to the long-term goals of improving unfamiliar communication partners’ comprehension and helping people with aphasia self-advocate effectively.

NSF Awards · FY 2024 · 2024-09

Internet suppression continues to be a significant global threat to freedom of speech and open access to information for average citizens in many countries or regions. While there exists an arsenal of tools to defeat Internet suppression, they often fall short of helping users effectively and reliably. This project is based on the key observation that existing technologies are largely reactive to the Internet suppressions: they only adjust their counter mechanisms based on the actions taken by the suppressors. The project team believes that in order to defeat Internet suppression, novel and more proactive approaches are required. Therefore, the overarching goal of this project is to investigate, design, analysis, and deploy proactive techniques to fight against Internet suppression, with the objective of making these new techniques more dynamic and adaptive. This project will pursue three complementary thrusts on creating proactive techniques to defeat Internet suppression. First, the project aims to build a generic, extensible Internet suppression emulation framework capable of emulating past, present, and future suppression strategies to help researcher to gain insights into the problem. The project aims for such an emulation framework to become an integral part of future research and development on proactive anti-suppression techniques. Second, the project plans to explore the extent of capabilities that AI may be exploited to assist Internet suppression and then develop effective countermeasures. Finally, the project plans to facilitate the adoption of the created anti-suppression techniques through Internet-scale testing and bring them out to practitioners and users. 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 Awards · FY 2024 · 2024-09

Hazardous weather early warning systems disseminate timely and meaningful information about flash floods, tornadoes, and other weather hazards so that individuals, communities and organizations can prepare for and protect themselves against harm or loss. Early warning systems involve sensors, model forecasts, federal and local public safety organizations, private companies, and communications technologies that disseminate warnings to the public. Hazardous weather warning messages are general rather than tailored to the risks faced by the people who receive them. The same warning message goes out to everyone in an affected region, regardless of individual circumstances. Each person is responsible for ensuring they receive and understand the warning, figuring out if they or loved ones are at risk, and then deciding if they have the capability or interest in taking protective actions. While warning systems have been effective for segments of the population, there is great potential to improve individual-level decision-making and community/societal outcomes, especially in the face of rapidly intensifying weather events. This planning grant takes a human-centered approach to hazardous weather warning to: 1) develop a deeper understanding of how individuals assess their risk and take action as weather hazards evolve, and 2) apply this expanded knowledge to new ways of tailoring warnings to individual or group circumstances. In this planning grant, a multidisciplinary group of researchers and practitioners address how multiple factors – rain intensity, quality of the stormwater infrastructure, individual daily routines of travel, advanced preparation, risk perception, warnings, social and environmental cues, and socioeconomic vulnerability – interact to influence people’s perception and response to floods. The team establish a common knowledge base and language through sharing research, methods, and datasets. A focus group is held with residents of vulnerable communities in collaboration with a local nonprofit to investigate how different individuals process information from early warning systems. The planning project includes exploratory projects that contribute to an innovative plan for convergent human-centered research. This work identifies new relationships among risk perception, mobility, weather, and built infrastructure that can point to new directions for convergent warning research. In addition, the planning grant allows early work on developing the concept of personalized warnings. Broader impacts include outreach to vulnerable populations to learn about this group’s perceptions and actions during floods. 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.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Cardiac diseases remain the leading cause of human morbidity and mortality. Cardiac microtissues/organoids built from human induced pluripotent stem cell–derived cardiomyocytes (hiPSC-CMs) provide promising platforms for disease modeling and long-term treatment (e.g., through transplantation). However, recapitulating the intricate environment of the human heart, especially the global cell maturity, has proven challenging in vitro. One primary hurdle is the lack of consistent delivery of oxygen and nutrients to the deep-layer tissue, leading to unbalanced tissue development that eventually impairs the full functional maturation for precision studies. Existing artificial vascular systems are either confined to planar substrate or limited in resolution, falling short of the spatial coverage and resolution in the capillary network of a living organ for efficient media delivery. Moreover, no concurrent sensing network and bioelectronic data analytics are available to provide real-time and comprehensive assessment (e.g., link to molecular cell mechanisms) of the delivery effect across the 3D tissue. To fill in the gaps, we aim to develop an ultra-flexible and stretchable bioelectronic microarchitecture that integrates a capillary-like network for efficient media delivery and a tissue-like sensing network for tissue-state feedback. The system will be seamlessly integrated with cardiac microtissues to improve tissue development. We will also translate the real-time physiological feedback to molecular cell mechanisms to enable comprehensive quantification of the delivery effect. To realize the goals, Aim 1 will focus on constructing the flexible bioelectronic microarchitecture, optimizing the converged delivery and sensing functions, integrating the system in cardiac microtissues, and evaluating the interfacing intimacy and media delivery efficiency. Aim 2 will focus on the comprehensive analysis and optimization of delivery effect, through the multiple efforts of performing long-term electrical recording, employing in situ electro-sequencing to combine 3D spatial transcriptomics with electrical recording, and using analytics to connect functional phenotypes to molecular cell mechanisms. The success of this work will provide a transformative platform for improving cardiac tissue engineering. This platform can be readily translated to other tissue systems for improving function and development.

NIH Research Projects · FY 2024 · 2024-09

Demystifying the interaction of UGGT, the ER folding gatekeeper, with its substrates and co- chaperone Project Abstract Proteins must fold into specific 3-dimensional structures to attain their function, but this process can be error prone. Worse yet, accumulation of misfolded proteins can lead to diseases such as Alzheimer’s and Parkinson’s. To mitigate this risk, nature has evolved sophisticated mechanisms to assist protein folding and assess their folding status. One such example is the endoplasmic reticulum protein quality control (ERQC) cycle, where the enzyme UDP-glucose: glycoprotein glucosyltransferase (UGGT) serves as the master regulator. UGGT can sense the folding status of its clients and selectively adds a glucose residue to the N-glycans of non-natively folded proteins. Substrates modified by UGGT are retained within the ERQC cycle for further attempts at productive folding. Prior studies have determined that UGGT prefers “near-native” substrates that present molten-globule conformations. Nonetheless, a structural description of the folding-sensing mechanism of UGGT remains unknown. UGGT can act alone, but also forms a complex with a co-chaperone, the 15-kDa selenoprotein (SEP15). This protein contains a redox-active selenocysteine residue and is thought to aid UGGT in QC of disulfide-rich substrates, but few structural and functional studies have been performed. This project will utilize a combination of cellular and biophysical experiments to investigate the interactions of UGGT with its substrates and co-chaperones. First, structural studies of UGGT and the UGGT/SEP15 complex are underway. These results will provide insights into the concerted action of UGGT and SEP15. In parallel, endogenous substrates of UGGT/Sep15 will be identified in mammalian cell culture using a glycoproteomics assay and their maturation characterized. Second, UGGT-substrate interactions will be investigated using the disease- causing null Hong Kong variant of ɑ1-antitrypsin as a model substrate. Specific UGGT residues involved in client recognition will be identified using photo-crosslinking mass spectrometry (XL-MS) experiments. This work will be performed under the guidance of two leaders in the field of protein homeostasis. Dr. Lila Gierasch is a leader in the field of protein chaperone biophysics, while Dr. Daniel Hebert is an expert in the cell biology of glycoprotein quality control. This project will leverage my background in analytical chemistry and provide critical training in biochemistry and cell biology. Additionally, I will become well- versed in the broader field of protein homeostasis (proteostasis). After completing the training described in this proposal, I will be ready to launch my independent academic career studying the structural biology of ER proteostasis.

NSF Awards · FY 2024 · 2024-09

Computer science education is increasingly critical for preparing well-trained professionals for the national economy and building a competitive workforce of the future. The emergence of generative AI provides an opportunity to improve computer science education by adapting the learning process to the needs and knowledge of individual learners. University of Pittsburgh, Carnegie-Mellon University, University of Massachusetts, and North Carolina State University will develop and evaluate a comprehensive personalized programming practice environment (C-3PE) that utilizes artificial intelligence (AI ) to enhance learning experiences. This project capitalizes on the power of generative AI and progress in learning science research to provide personalized learning experiences for computer science students. C-3PE recommends the most suitable learning activities for each student according to their current knowledge level and offers personalized feedback to support their progress. By conducting long-term classroom studies, the project team will assess the impact of AI-based personalization approaches and identify the most effective types of learning activities and feedback messages for students with different competency levels. Leveraging advances in AI-driven learning technologies and theoretical frameworks in learning sciences, C-3PE will deliver engaging computer science learning experiences. It will provide personalized practice support and detailed feedback for individual learners based on their practice history and current knowledge state. C-3PE will dynamically model the state of learner knowledge using context-aware deep-learning knowledge tracing models. Furthermore, the project team will develop a nested personalization approach with an outer loop and an inner loop. For the outer loop, the project will develop new, large language model (LLM)-powered adaptive testing algorithms that select the most informative next practice question/worked example for each student. For the inner loop, they will use preference optimization to align LLM-driven feedback generation with student learning outcomes. A sequence of experiments will lead to a better understanding of the kinds of practice opportunities (i.e., worked examples vs problems) and types of feedback messages that are most effective to each student. Utilizing an iterative design process to integrate insights from studies into the learning environment, the project will evaluate C-3PE in various introductory programming classrooms across diverse institutions. The project will enhance education through personalized recommendations and feedback, disseminating findings and tools through academic conferences and platforms, and sharing C-3PE via a GitHub repository for computer science instructors. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. 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 Awards · FY 2024 · 2024-09

Catalysis is a key technology that enables the production of fuels and chemicals from carbon-containing resources such as petroleum, biomass, or waste polymers. In an ideal scenario, a catalyst has an infinite lifetime: it assists a chemical transformation while remaining unaltered itself. However, the real-world conditions in industrial processes lead to catalyst deactivation, often by deposits on the surface of the catalyst. As a result, the processes become less efficient, and catalysts need to be regenerated or replaced, all of which adds to energy consumption, waste generation, and cost. Researchers at the University of Massachusetts Amherst (US), the L.V. Pisarzhevsky Institute of Physical Chemistry of the National Academy of Sciences of Ukraine in Kyiv (Ukraine), and the National Institute of Chemical Physics and Biophysics in Tallinn (Estonia) combine their expertise and devise new approaches to catalyst regeneration that will prolong the service life of catalysts. The objective of the project is to test the concept of a “companion catalyst” that will be integrated into a reactor and make the regeneration process itself catalytic. It is hypothesized that catalytic regeneration is milder and more efficient than non-catalytic regeneration. Three target reactions with a variety of reactants (hydrocarbon or oxygenate) and a range of operating temperatures will be explored to generate deposits of different composition. Companion catalysts will be designed to oxidize or hydrogenate the deposits to volatile compounds. To avoid interference of a companion catalyst with the target catalyst, it will have low intrinsic reactivity, be passivated, or be inaccessible to the reactants. Experiments will be conducted to assess the role of transport of the activated regenerant across the surface or grains. The group in Kyiv will synthesize and characterize catalysts; the group in Tallinn will apply NMR to characterize catalysts and carbon-containing deposits; and the group in Amherst will deactivate catalysts and monitor their regeneration by operando spectroscopy and thermal analysis. The concept of a companion catalyst, if proven to be viable, has potential for the many processes in which deactivation is caused by deposits. The project will provide basic and advanced training in catalysis and kinetics, cutting-edge methods of materials synthesis and characterization, and in situ and operando techniques for all participants, including students in all three locations. Efforts to enhance the participation of underrepresented groups will be made when recruiting students and through contributions to the UMass College of Engineering RISE program. The results of this project will be widely disseminated, within the scientific community through publication in peer-reviewed international journals or presentation at conferences, and to a broader audience via news items on institutional webpages. This IMPRESS-U project is jointly funded by NSF, Estonian Research Council (ETAG), US National Academy of Sciences, and Office of Naval Research Global (DoD). The research will be performed in a multilateral international partnership that unites University of Massachusetts Amherst (US), National Institute of Chemical Physics and Biophysics, Tallinn (Estonia), and Institute of Physical Chemistry, National Academy of Sciences of Ukraine, Kyiv (Ukraine). US portion of the collaborative effort will be co-funded by NSF OISE/OD and ENG/CBET programs. 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 Awards · FY 2024 · 2024-09

Cardiac diseases are the leading cause of human morbidity and mortality. Drugs also need to be verified to not cause side effect in heart before they can be used clinically. It is inconvenient to study cardiac diseases or test drug effects by using living animals for multiple reasons, including that an animal heart is not that close to a human heart. A convenient strategy is to use heart-mimic tissue outside of the living body made from human-derived cells, which bypasses the use of living life but retains human information for relevant studies. Since the heart beating is coordinated by an electrical signal, it is often desirable to monitor both the mechanical beating and electrical signal together for better assessment of the tissue state involved in various studies. However, current sensing technologies are limited in achieving that—particularly deep inside the tissue—without causing perturbation to the tissue function or health. This project aims to tackle this challenge by developing cell-sized electronic sensors that can simultaneously detect the electrical and mechanical activities in cardiac tissue, incorporating these sensors on small ribbon threads having the lateral width still smaller than a cell, and embedding these threaded sensors into the cardiac tissue for real-time monitoring. For the small form factors in both the sensors and ribbon threads, the system is expected to introduce minimal invasiveness or perturbation to tissue function. Therefore, it will not only acquire enriched signals from both mechanical and electrical activities but also long-term stable monitoring, fundamentally improving the assessment of tissue state involved in cardiac disease studies and drug tests. Eventually, the research contributes to the improvement of cardiac disease remedy and alleviation of healthcare burden. To meet the overall goal, this project will accomplish the following key objectives: 1) A scalable assembly strategy will be developed to fabricate an array of three-dimensional sensor structures using planar semiconducting graphene material. The sensor structure is designed to be able to detect both electrical and mechanical stimuli. 2) These sensor devices will be evaluated for their multifunctional sensing capabilities by culturing planar cardiac tissue on them. 3) A scalable integration method will be developed to integrate these three-dimensional sensors on an ultra-flexible and porous mesh scaffold consisting of individual thin ribbons. 4) The sensor-integrated mesh system will be embedded into three-dimensional cardiac organoids for functional validation and tissue-state evaluation. Success in the research can lead to transformative biomedical devices and cardiac tissue models that have much improved feedback quantifications for various studies. It can also have the long-term potential to translate to implantable biomedical devices for heart monitoring and early disease prediction. The research will also be integrated into education for broadening the participation in science and engineering. 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 Awards · FY 2024 · 2024-09

Vortices are persistent circulating flow patterns that arise in diverse physical contexts, ranging from classical hydrodynamics and superfluids to condensed matter physics and nonlinear optics. They are ubiquitous physical phenomena in the world around us and can be observed at very different scales, from microscopic vortex lines in superfluid liquid helium, to dust devils and tornadoes, and even to Jupiter's Great Red Spot. Bose-Einstein condensates of ultracold atoms (BECs) provide a pristine and controllable environment where numerous aspects of the fascinating realm of nonlinear vortex dynamics can be explored not just in theory but also through direct experiments. In addition to their intrinsic fundamental interest, these systems also exhibit localized solutions with potential practical applications: for example, it has been suggested that solitary waves could be used for unprecedented, improved sensitivity in interferometric and force-sensing devices. On the other hand, vortical structures, which are the focus of this proposal, also hold promise for other intriguing applications. For instance, they can provide an instance of 'analogue gravity' as a proxy to study the behavior of spinning black holes. It has also been proposed that BEC vortices could collapse in a manner akin to supermassive black holes and that supersonic expansion in BECs can replicate properties of an expanding universe in laboratory settings. Through a bijective collaboration with experiments, this proposal aims to advance the current understanding of topological structures in BECs. Being based on universal models of modulated waves in nonlinear media, the underlying physical setting represents a fundamental playground to study topologically charged excitations that are, in turn, at the heart of an extremely wide variety of physical contexts in atomic, optical, wave physics, and beyond. The project will address the existence, stability, manipulation, and dynamics of vortex configurations in 2D and 3D settings from a novel and broad perspective. The PIs' plan is to develop effective lower dimensional, reduced evolution equations to gain novel insights on the properties of these coherent structures in the original, high-dimensional, models and to compare the theoretical results therefrom with numerical computations and circling all the way back to direct observations from atomic and polariton BEC experiments. The main goals of this proposal are multi-fold and include the following themes: the creation, removal, and interactions of vortices and soliton filaments and experimentally tailored external potentials by leveraging effective lower-dimensional dynamical models for the evolution of soliton filaments coupled with point-vortex models including the relevant case of open quantum systems in the presence of driving and damping for polariton condensates. Also, in close synergy with experimental collaborators, the study of the timely theme of synthetic magnetic monopoles and the elusive so-called Alice ring in spinor (chiefly F=2) BECs. The project aims to shed light on this highly complex, topological pattern forming system and, in particular, on the recent collaborator experiments where they observed that monopole instabilities give rise to topological patterns reminiscent of Alice rings. 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 Awards · FY 2024 · 2024-09

This award supports research that enables a new dynamic model for fluid-structure interaction (FSI) systems with a focus on large wind turbine blades, thereby promoting the progress of science, and advancing prosperity and welfare. The project will promote safe design of next-generation offshore wind turbine structures by enabling slender and lighter blade designs. The research will provide indications about various wind turbine blade aeroelastic instability thresholds along with the most appropriate simulation and analysis approaches for novel designs of longer and therefore more efficient wind turbine blades. Although flow-induced instabilities have been predicted to occur for such new wind turbine blade designs, predictions are often based on deterministic models without the influence of flow turbulence and load variability. This project will address this critical gap by combining experimental measurements and theoretical modeling to derive a novel model that accounts for the influence of turbulence on the onset of instability and post-critical behaviors. This research is timely since the current energy plan of the United States strongly emphasizes the need for alternative and sustainable energy production by offshore wind energy. Integrated research and educational initiatives will complement the research. The findings of this research will be disseminated, at different levels, by integrating the research into the outreach programs for K-12 students and teachers, creating new modules for different courses, hosting high school classes, and broadening research opportunities for students from under-represented minority groups. This research aims to make fundamental contributions to accelerate the use of stochastic and probabilistic structural dynamics to examine pre- and post-critical behavior of fully-coupled FSI systems with asymmetric structures and subjected to three-dimensional flows that can undergo nonlinear dynamic instabilities. It will achieve this goal by producing models that describe turbulence effects and more accurately considering the aeroelastic loads, which are relevant to highly flexible and asymmetric structures, such as a wind turbine blade. The existing aeroelastic load models are mainly for basic-shape airfoils (symmetric, small thickness idealized surfaces), and their flow parameters are based on experiments conducted on airfoil cross sections that represent long structures, under two-dimensional flow conditions. The researched modeling will include the asymmetries, twists and variable thicknesses that are typical of modern wind turbine blades but also applicable to a wider range of structures similar to wind turbine blades. These asymmetries and twists result in a highly three-dimensional turbulent flow. Several series of experiments will be conducted both at small and large scales. Information on flow forces and turbulence intensities for each case will be collected to inform a semi-empirical stochastic model for the onset and post-critical instability of the structure. The model will account for three-dimensional “rotationally sampled” flow features and will be validated at two separate experimental scales. These two sets of aeroelastic experiments will enable model verification for larger and full-scale structures. 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 Awards · FY 2024 · 2024-09

The resilience and robustness of autonomous robotic systems in dynamic, unpredictable, and ever-changing environments are central concerns of the robotics community. To address these challenges, this research project introduces a novel "introspective counterfactual reasoning" capability to empower robots with lifelong autonomy. While counterfactual thinking—considering the implications of changes in the world that could have happened, but didn’t—is a foundational cognitive function in human beings, its application in robotics remains largely underexplored. This project aims to bridge this knowledge gap by enabling robots to answer and learn from "what if" questions regarding both their surroundings and themselves, better preparing them for unforeseen events, potential hazards, and evolving contexts. This project introduces two different yet interleaved forms of counterfactual reasoning: Contextual Physical Rehearsal and Introspection Adaptation. Contextual Physical Rehearsal allows the robot to model the physical world and forecast the outcomes of actions without actual execution. Introspection Adaptation focuses on predicting and enhancing the robot's capacity to perform tasks in unfamiliar environments and unexpected situations. The strategy involves designing these capabilities, integrating them into diverse autonomy platforms as interconnected modules, and validating their efficacy in real-world tasks. The framework will be validated in a rigorous procedure from modular simulation testing to integration and deployment on real ground vehicles under challenging conditions. The project will create new interfaces that allow developing courses on field robotics and simulation and provide immersive and engaging programming activities for K-12 students. 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 Awards · FY 2024 · 2024-09

This project is a collaboration amongst the University of Florida (Gainesville), the University of Georgia (Athens), and the University of Massachusetts (Amherst). Schistosomiasis, a disease caused by parasitic worms and transmitted through contact with contaminated freshwater, poses a significant public health threat in many developing regions. Hence, designing effective intervention strategies to mitigate the disease’s impact is timely and critical. This project aims to advance our understanding of schistosomiasis transmission dynamics and control. Specifically focusing on schistosomiasis in Zanzibar and Ethiopia, this research will create and use innovative mathematical modeling tools to understand how various factors like human movement and environmental changes influence disease transmission. This will help to identify the best strategies to control and eventually eliminate schistosomiasis as a public health problem. The project is not only scientifically important but also has significant public health, educational, and societal implications. The educational and societal impacts include training a diverse group of students (including students from underrepresented groups) and fostering interdisciplinary and collaborative research skills. The project will provide novel analytic tools for efficient resource management and inform evidence-based policies for sustainable elimination of schistosomiasis, thereby significantly impacting global health. A workshop in Zanzibar will further help building workforce in quantitative public health and disseminating scientific knowledge. The proposed project seeks to develop advanced mathematical models to improve our understanding and management of schistosomiasis transmission dynamics, especially during the transition from high to low transmission phases towards elimination. Current models often fail to adequately capture low transmission environments, where random events, spatial and temporal heterogeneities, as well as environmental factors significantly impact transmission persistence. The project aims to develop a novel and robust hybrid deterministic-agent-based modeling framework integrating snail population dynamics (an aspect hither to overlooked in many schistosomiases transmission models) and environmental factors, using data for Schistosoma haematobium from Zanzibar and Schistosoma mansoni from Ethiopia. This innovative dual-phase framework will capture complexities in both high and low transmission settings, incorporating human movement, hydrological networks, and parasite gene flow. The project will assess persistence drivers under low-level transmission, identify transmission breakpoints, and optimize intervention strategies, offering new insights into transmission dynamics and control strategies that lead to impactful public health policies.  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.