University Of Illinois At Urbana-Champaign

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Chronic subdural hematoma (CSDH) is one of the most common types of intracranial hemorrhage, affecting 3.4-5/100,000 per year in the general population and 60-80/100,000 aged 65+ and its incidence is rising in the older population. Treatment is performed by draining the hematoma, but as many as 30% of the cases reform hematomas with a mean timeline of about 1.5 months. Previous studies have worked to find clinical biomarkers to predict risk of hematoma and its recurrence so that the clinical care team can focus their efforts on these more complex cases. Previously identified biomarkers include age, hematoma density (as viewed on a CT scan), and blood markers of inflammation; however, another significant factor is the subdural space and lack of reexpansion of tissue after treatment. Previous literature using invasive methods has suggested that the elastance of the brain surface around the hematoma may provide a strong prediction of the ability of the brain to expand to fill the space of the evacuated hematoma. In this project, we will leverage recently developed magnetic resonance elastography (MRE) methods that quantitatively and non-invasively measure the brain mechanical properties of stiffness and damping ratio to correlate with characteristics of the hematoma (size and density) along with reexpansion of the brain 1 month after treatment. We will examine a set of 40 patients with a CSDH diagnosis, collecting standard clinical measures of hematoma size, density, clinical symptoms, and blood markers of inflammation. In addition, we will collect a high-resolution MRE data set 1 month after treatment that will provide spatial maps of stiffness and damping ratio of the patient’s brain. We will examine the mechanical properties of the brain in areas adjacent to the hematoma, along with a contralateral control area, to determine the correlative value of these measures for hematoma size and density along with brain tissue reexpansion. We will also examine the relationships between brain mechanical properties and the other clinical biomarkers of recurrence from previous literature through a factor analysis to determine the correlations with other measures and the uniqueness of the information provided through MRE for this condition. This line of research will have an important positive impact because it has the potential to provide a strong predictor of brain reexpansion after treatment for CSDH and hence risk of recurrence. The project will lay the foundation for more individualized treatments in complex cases that are likely to recur.

NSF Awards · FY 2024 · 2024-08

The E=mc2 project develops a community-driven cyberinfrastructure for simulating cosmic events such as binary neutron star and black hole-neutron star mergers. These cosmic collisions are incredibly energetic, emitting observable gravitational waves, electromagnetic waves, and neutrinos. Such "multi-messenger" observations provide critical insights into general relativity, nuclear physics, and plasma physics. The insights gained through these simulations promote the progress of science and enhance national scientific capabilities. By creating an open-source, high-performance simulation software framework, the project supports education and diversity, fostering a broad and inclusive scientific community. The cyberinfrastructure integrates into the Einstein Toolkit, making it widely accessible and facilitating interdisciplinary collaborations that benefit society by advancing knowledge in fundamental physics. The E=mc2 project aims to develop the first open-source, exascale-capable numerical relativity and general relativistic magnetohydrodynamics cyberinfrastructure with realistic neutrino transport capabilities. This involves creating new codes based on Monte Carlo and moment schemes for neutrino modeling, and high-performance software compatible with both CPU and GPU architectures. The project will leverage existing frameworks like AMReX and the Einstein Toolkit, ensuring that the developed cyberinfrastructure can fully exploit modern high-performance computing resources. Key deliverables include modules for GPU-enabled particle-based neutrino simulations, a fully tabulated equation of state module for relativistic hydrodynamics, and advanced code generation tools to optimize performance. These tools will be rigorously tested and validated to ensure reliability and efficiency. The project also includes extensive community outreach and education efforts, including summer schools and workshops to train new users and expand the user base, ensuring sustained impact and community engagement in computational astrophysics. This award supports research in relativity and relativistic astrophysics, and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the National Science Foundation's Big Idea activities in Windows on the Universe (WoU) and the Physics at the Information Frontier program in the Division of Physics in the Directorate of Mathematical and Physical Sciences. 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 · 2024-08

This grant supports travel for US participants in the program "Discrete integrable systems: difference equations, cluster algebras and probabilistic models," that will take place October 20 to November 1, 2024 at the International Center for Theoretical Sciences in Bangalore, India. The program includes a one-week introductory school, including three lecture series by experts in cluster algebras, integrability and probability, directed at graduate students and early career mathematicians. This will be followed by a one-week conference that will include presentations by senior researchers in the field. The grant will support the travel of early career mathematicians from the US to the conference, where they will interact and form research collaborations with researchers and leading experts from around the world. The program focuses on three interrelated aspects of discrete integrability at the interface of mathematics and theoretical physics, which have seen intense research activity of late: (1) The study of singularities of integrable difference equations and ultra-discretization of these equations, such as box-ball systems. (2) The interplay between discrete integrable systems and cluster algebras, with applications to discrete geometry and statistical mechanics. (3) Applications to integrable probability, solvable vertex models and the theory of symmetric functions. The program webpage is https://www.icts.res.in/program/DISDECAP2024. 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 · 2024-08

Large-scale networks are being generated in many scientific fields, including biological sciences, social sciences, and physical sciences. The project addresses the urgent need to develop scalable subsampling algorithms for statistical inference on such networks and provide theoretical guarantees on the performance. The proposed methods will be applied to biological and social network data, and will be used to study mindfulness-based therapies for disorders associated with hearing loss, such as tinnitus. The project offers opportunities for involvement of graduate and undergraduate students with diverse backgrounds and interests. The proposed methods will be incorporated into relevant courses. Research results will be disseminated to the scientific communities, and all software developed in this research will be freely distributed as open-source to the public. The project will develop subsampling based methods for inference problems, such as model selection and hypothesis testing, for large-scale networks, and investigate theoretical properties of these methods to provide statistical guarantees on performance. The subsampling strategies will be applied to a broad range of models for networks, including stochastic block models, random dot product graph models, latent space models, and other models for networks. The theoretical properties of subsampling methods investigated in this project include the consistency of model selection, hypothesis testing, and parameter estimation. The proposed subsampling methods will be applied to real network data from social and natural sciences. 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 · 2024-08

Hawaii is home to some of the most active volcanoes on Earth, with recent volcanic eruptions at both Kīlauea and Mauna Loa. In 2018, the eruption of Kīlauea Volcano produced extensive lava flows covering over 35 square kilometers and damaged or destroyed over 1,800 structures, including many residential buildings. More recently, from November 27, 2022, to December 13, 2022, Mauna Loa experienced its first eruption in more than three decades. An improved understanding of the hazards associated with Hawaiian volcanism requires detailed knowledge of how magma is stored in the subsurface. However, gaps remain in our knowledge about the volume and geometry of subsurface magma reservoirs that feed volcanic eruptions. This project investigates the subsurface magma reservoirs below Hawaii using seismic data and modern subsurface imaging techniques that rely on recent advances in supercomputing. The results from the project will allow the researchers to gain new insight into the depth, dimensions, and melt fraction of magma reservoirs below Hawaii, and provide a clearer picture of how magma storage zones are related to earthquake activity. The project does not require any new installation of seismic instruments in Hawaii but relies on data from previous temporary instrument deployments and permanent monitoring stations. This project will support the education of undergraduate students in the classroom as well as the training of a PhD student in all aspects of this work. The Hawaiian Island chain is the classic example of hotspot volcanism. This chain was instrumental in the development of the plume hypothesis which proposes that intraplate volcanism and increased plate boundary volcanism is driven by upwelling thermal plumes from the deep mantle. Many aspects of Hawaiian volcanism are well explained by a steady supply of magma below a moving oceanic plate. However, key questions remain regarding the extent of melting in the source region, the depths of magma reservoir assembly and storage, and how magma is transported from its source region to feed both volcanic eruptions and those magmatic intrusions that do not reach the surface. Seismic tomography studies have been important for developing our current understanding of Hawaiian volcanism, but have been hindered by a variety of factors, including imperfect data coverage and limitations of ray-based imaging approaches. This project will provide a new high-resolution seismic tomography model of the seismic velocity structure of the Hawaiian volcanic system. This will be achieved by inverting local earthquake body wave and ambient-noise-derived surface-wave data with a full waveform imaging approach. The use of improved imaging theory, combined with diverse data sets from on-land and ocean-bottom seismic deployments, will provide a more complete picture of Hawaii’s magmatic system from the surface to uppermost mantle depths. In addition to providing new constraints on Hawaii’s magmatic plumbing system, including the geometry of magma reservoirs, melt fraction and organization, the project results have potential to improve catalogs of earthquake source parameters and gain insight into the relationship between seismicity and magma migration. A PhD student will engage in all parts of this research, and the results will be incorporated into undergraduate courses to strengthen learning of the scientific processes required to assess natural hazards. 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 · 2024-08

The University of Connecticut, the University of Wisconsin-Madison, and the University of Illinois at Urbana-Champaign are conducting a research study of the barriers and solutions that physics graduate students and faculty experience in non-traditional post-secondary training and workplace settings. The lack of full inclusion of people with disabilities in the STEM workforce is a missed opportunity to realize the full potential and talent of the entire U.S. population. Opportunities to advance knowledge about physics postsecondary training setting and workplace barriers and solutions for faculty and graduate students with disabilities will lead to increasing the engagement, academic career retention, and career advancement of faculty and students with disabilities in STEM. Such success is essential for building and advancing a robust U.S. STEM workforce. The research team is engaging with an expert advisory board, an objective evaluator, a postdoctoral research scholar, and graduate students to contribute to the project work. The research includes the collection, analyses, and interpretation of qualitative and quantitative data that are informed by robust theoretical frameworks and conceptual models. Findings will be share with the general public as well as researchers, educators, and administrators. This award has been made in response to the NSF solicitation “Workplace Equity for Persons with Disabilities in STEM and STEM Education” (NSF 23-593). The project is funded by the Directorate for Social, Behavioral and Economic Sciences’ Office of Multidisciplinary Activities, the Division of Equity for Excellence in STEM’s Education Core Research (ECR), the Division of Equity for Excellence in STEM’s Alliances for Graduate Education and the Professoriate (AGEP), the Division of Equity for Excellence in STEM’s Louis Stokes Alliances for Minority Participation program (LSAMP), the Division of Undergraduate Education’s Improving Undergraduate STEM Education (IUSE), and the Division of Equity for Excellence in STEM’s Eddie Bernice Johnson Inclusion across the Nation of Communities of Learners of Underrepresented Discoverers in Engineering and Science (INCLUDES). 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 · 2024-08

Understanding how aquatic plant canopies interact with flowing water is crucial for many aspects of environmental science, including river habitat restoration, flood management, and sustainable energy solutions. This project focuses on studying the forces that influence the movement and stability of these underwater and emergent canopies. By examining flexible and rigid plant structures in river environments, the researchers aim to uncover the complex interactions between water flow and plant life. The findings will enhance our ability to predict and manage water flow in natural and engineered environments, leading to more effective conservation strategies and improved designs for renewable energy systems. The project also promotes community engagement and education, with efforts to involve students from under-represented communities in STEM through university programs and public outreach activities, including interactive exhibits and public lectures. The research involves a combination of experiments and numerical simulations to investigate how different types of plant canopies affect water flow and drag forces. Experiments will measure the overall resistance of plant canopies and analyze the flow patterns around them. Advanced flow and object-tracking techniques will be used to capture detailed data on canopy movements and water currents. Numerical simulations will complement these experiments by better understanding local drag forces and drag distribution within canopies. Artificial neural networks will be developed to predict the drag of canopies in various configurations. This project will generate comprehensive datasets to train machine learning models, ultimately leading to a generalized formulation for predicting canopy drag in different environments. The results will be shared through publications, presentations, and a publicly accessible digital repository, contributing to the broader scientific knowledge and practical applications in the field. 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 · 2024-08

Young children thrive when strong relationships exist between their home and school environments. Often, schools and teachers struggle to establish these strong relationships. Math Partners will work with teachers and teaching assistants in classroom design teams to help teachers establish healthy, positive relationships with families that center families' knowledge and experiences. Early home and school experiences support the development of mathematical skills. Therefore, Math Partners will focus on helping teachers and families build strong partnerships in the context of mathematics. Math Partners will use teacher professional development (i.e., learning labs and small-group coaching) and classroom design teams to help teachers enhance their classroom teaching by centering families' perspectives and practices in the classroom and helping families have joyful experiences with mathematics at home. The Math Partners intervention is informed by the concepts of Funds of Knowledge and Community Cultural Wealth that inform a growing understanding of how to help teachers partner with families to support early math learning. This project will establish dual-capacity partnerships that recognize and honor families' knowledge and perspectives. The investigators will examine if and how teachers' and families' beliefs about family engagement and family math capacity shift as a result of this intervention and how the intervention impacts families' math engagement at home. Teachers will receive Practice-Based Coaching in small groups with other teachers and will form design teams in each Math Partners classroom. The design teams will inform how the teachers provide math activities, materials, and instruction. The design teams will help develop math-centered family engagement practices through home visits, classroom "stay-and-plays," and home activities. We will use quantitative and qualitative methods to assess program effectiveness and impact, including surveys, analysis of recorded meetings and activities, service logs, and focus groups and interviews with families and teachers. Math Partners includes four phases: (1) an initial design phase conducted in collaboration with four early childhood classrooms (PreK-2), (2) a field test conducted in collaboration with six early childhood classrooms, (3) a pilot randomized controlled design with 15 treatment and 15 control classrooms, and (3) a dissemination phase. Dissemination will include traditional outlets (research publications and conference presentations), as well as webinars and tip sheets for practitioner and family audiences. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models, and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Each year, the experiments at the Large Hadron Collider, LHC, at the European Laboratory for Nuclear and Particle Physics, CERN, in Geneva Switzerland collect data from high energy collisions of lead ions (Pb-Pb collisions). The colliding nuclei consist of protons and neutrons, which in turn are composite objects of quarks. For example, a proton contains two up-quarks and one down-quark. Quarks are bound together in protons and neutrons through the strong nuclear force, mathematically described by Quantum Chromodynamics (QCD). The force is mediated through the exchange of gluons as force carrier particles. In the overlap region of the nuclear collision, protons and neutrons are destroyed and leave behind the so-called quark-gluon plasma (QGP), a form of pure quark matter that existed in the microseconds immediately following the Big Bang at the beginning of the Universe. The reaction products of Pb-Pb collisions in the CERN detectors (ATLAS, CMS, ALICE and LHCb) often produce QGP. The observation of the final state particles makes it possible to characterize the properties of the QGP and in this way increases our understanding of the fundamental matter in the early universe. The research funded through this grant will result in radiation resistant materials and machine learning algorithms that are needed to use the ATLAS Zero Degree Calorimeter (ZDC) and the ATLAS Reaction Plane Detector (RPD) to determine the geometry of the Pb-Pb collision system. The researchers supported by this award will measure both, the magnitude of the overlap of the colliding nuclei as well as the orientation of the nuclear collision system with respect to the ATLAS detector systems. The RPD and ZDC are located in a radiation environment that inflicts severe radiation damage to materials. For example, most optical materials will lose their light transmission after just a few weeks of usage. This group will test different advanced fused silica materials for use as optical media in the active detection elements of RPDs and ZDCs. They will also study different radiation resistant photomultiplier tubes that will be used to readout the Cherenkov created in the active detection elements of the RPDs and ZDCs. Furthermore, the group will develop advanced machine learning algorithms that will be used to analyze the RPD data and to extract the orientation angle of the Pb-Pb collisions system with respect to the ATLAS detector. The advanced radiation resistant materials and the machine learning algorithms developed for ATLAS will be also used by CMS ZDCs and RPDs and for future experiments at the Electron Ion Collider, EIC, at Brookhaven National Laboratory, BNL, on Long Island, NY. In the laboratories at UIUC and Ben Gurion University more than 12 graduate and undergraduate students will work on the R&D for the RPDs and ZDCs. In addition, the laboratories host undergraduate summer students from 4-year colleges and high school student interns. Students will be trained in the Physics of the QGP, instrumentation for high radiation environments, advanced machine learning algorithms and collaborative research in large, international collaborations. The radiation-resistant detectors and materials developed in this project are expected to have applications in other fields, such as dose monitoring in radiation oncology, use for space-based instrumentation and radiation monitoring in response to nuclear accidents. The project will use unique irradiation facilities at the Soreq Nuclear Research Center in Israel and in the LHC tunnel at CERN that have been used to identify radiation-hard Cherenkov radiator materials for the present ATLAS and CMS ZDCs and RPDs. The project also will make use of state-of-the-art materials research facilities, such as MRL at UIUC and laboratories at Ben Gurion University. The work on Machine Learning Algorithms (MLAs) will start from the current ATLAS RPD MLAs and expand to include data from the ZDCs in order to characterize the event geometry of heavy ion collisions. The MLA applications will be developed and tested on DeltaAI at UIUC NCSA, the most performant GPU computing resource in NSF’s portfolio. The unique radiation facilities and the project expertise with MLAs will then be used to evaluate technology choices and materials for EIC forward detectors, for the ePIC electromagnetic calorimeter. 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 · 2024-08

This award is for research in the theory of numbers. Every positive whole number is uniquely expressible as a product of primes. Primes are fascinating to study theoretically, but they also feature prominently in cryptography (the secure transmission of information). The distribution of primes is analytically encoded in the Riemann zeta function, the simplest example of an L-function. L-functions are ubiquitous in modern number theory. Many widely studied number-theoretic problems are naturally phrased in terms of properties of more general L-functions. This project will focus on non-vanishing of L-functions, individually and in parametric families. This is one of the most important questions regarding L-functions. For example, the distribution of zeros of the Riemann zeta function influences the distribution of primes (the subject of the Riemann Hypothesis), and conjecturally, the Hasse-Weil L-function of an elliptic curve vanishes at the point s = 1/2 if and only if the elliptic curve has infinitely many rational points (the Birch and Swinnerton-Dyer conjecture). The project includes training of undergraduate and graduate students. This project has three components. Towards the first component, the PI aims to develop new techniques to establish strong t-aspect zero-free regions for all Rankin-Selberg L-functions. The goal is a t-aspect zero-free region as strong as what de la Vallée Poussin established for the Riemann zeta function. Towards the second component, the PI aims to find new large classes of Rankin-Selberg L-functions for which one can establish a “hybrid-aspect” zero-free region with good t-dependence and no Landau-Siegel zero. This new uniformity will improve our understanding of the distribution of primes in relation to joint Sato-Tate laws involving two non-CM twist-inequivalent modular elliptic curves over a totally real number field. Towards the third component, the PI will continue earlier work on zero density estimates, showing that all L-functions in a family apart from a small exceptional set have very strong zero-free regions. 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 · 2024-08

One of the first things you might notice while taking a walk in the woods is that different plant species make fundamentally different investments---in wood, in speed of growth, and in seeds. The biggest trees have been around for decades or even centuries, growing slowly, and only producing seeds later in their lifespan. Smaller, weedier plants tend to produce a lot of seeds, but perhaps at the expense of lower survival rates and shorter lifespans. Yet here they all are, together in the same location. These different strategies are collectively known as the “life history” of each species. Some of the defining characteristics of a plant---its lifespan, growth rate, and seed production---fall under the umbrella of life history. But we lack a definitive picture of how, when and why these very different strategies will coexist. This research project will integrate cutting edge mathematical theory with empirical life history data to shed light on the question of which plant life history strategies will be found together, and why. New environmental conditions can substantially change species’ life history strategies, with potential knock-on effects on biodiversity. This research will begin to quantify the role of life history differences in determining these outcomes. The broader impacts of the project include the integration of new, quantitative approaches for life history analysis into university curricula, as well providing a window into ecological dynamics in an exhibit designed for a public audience. The project activities will engage with both forest ecologists and mathematicians, building new bridges between existing intellectual communities. Finally, multiple trainees will collaborate on the project, contributing to their training and career development at the interface of plant ecology and mathematical biology. The project will develop a range of theoretical models analyzing the dynamics of communities of species, where each can have its own, idiosyncratic life history strategy, characterized by growth, mortality, and fecundity rates that vary with life stage. These models will integrate life history differences with competition for resources, across a range of scenarios for different levels of species diversity and resource partitioning, and will generate predictions for patterns of biodiversity and species abundances across space and through time. In parallel, the project will use publicly-available plant community censuses to infer life history strategies for multiple species in the same community, providing a way to test and guide the development of new theory. 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 · 2024-08

This project will investigate the Kardar-Parisi-Zhang (KPZ) equation and related stochastic processes from the perspective of Gibbs measures. The KPZ equation, which was introduced by M. Kardar, G. Parisi, and Y.-C. Zhang in 1986, quickly became the default model for random interface growth in physics. The KPZ equation (and its extension, the KPZ line ensemble) inherits a remarkable structure called the Gibbs property that represents an inner consistency. The main goal of the project is to leverage the Gibbs property to study the KPZ equation, with a focus on constructing the solution and understanding long-range correlation. Students will participate in the research and the awardee plans to co-organize workshops and seminars and to write survey articles. This project involves three related directions of research. The first investigates the relation between the KPZ equation and the KPZ line ensemble, and plans to establish connections based on Gibbs properties.. The second research direction is to establish the universality of the Airy line ensemble. The Airy line ensemble has been shown to converge for various models such as the PNG droplet, Dyson's Brownian motion, exponential/geometric last passage percolation, and lozenge tilings, primarily due to their integrable nature. This project seeks to demonstrate the universality of the Airy line ensemble under broader and milder assumptions. The third direction is to use Gibbsian line ensembles to study the Laguerre unitary ensemble (LUE), which lies outside the KPZ universality class, building on the successful construction of the Bessel line ensemble and exploring its many potential 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 · 2024-08

To maximize scientific contributions in the field of legislative studies, this project creates a new initiative with the mission to engage, support, and promote the study of legislative politics across gender and sub-disciplinary divides. The initiative hosts virtual events monthly throughout the year, a professional development seminar, a research seminar, and a writing group, an in-person annual conference. The project also maintains a website and listserve with over 550 members and promotes women’s research via social media. Additionally, the initiative collects/analyzes data on women in legislative studies. This project seeks to bring new research and perspectives to scholarship on legislative politics by promoting the study of legislative politics across gender and sub-disciplinary divides. The initiative focuses on the research being done by a diverse set of scholars studying legislatures around the world. One of the aims of the project is to bridge the gap across the study of individual legislatures and the study of legislatures in comparative perspective. Bringing together a diverse set of scholars of legislative politics will encourage intellectual contributions that bridge these subfields. The initiative hosts virtual events monthly throughout the year, a professional development seminar, a research seminar, and a writing group, an in-person annual conference. The project also maintains a website and listserve with over 550 members and promotes women’s research via social media. Additionally, the initiative collects/analyzes data on women in legislative studies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-08

PROJECT SUMMARY/ABSTRACT Extensive studies have observed impaired mitochondria in patients and animal models of Alzheimer’s disease (AD). Specifically, a large body of research has made the connection between altered mitochondria functions and accumulation of amyloid-β (Aβ) peptides in various experimental systems. However, given the complex functions of mitochondria and Aβ, further research remains overwhelmingly needed to better understand mitochondrial dysfunction in AD in order to reveal promising therapeutic targets. To approach this question, we have collected a large amount of preliminary data to show that a E3 ubiquitin ligase, named neural precursor cell-expressed developmentally down-regulated gene 4-like (Nedd4L), is associated with mitochondria and regulates mitochondrial membrane potential. We further showed that Nedd4L is dephosphorylated in a familial AD mouse model, APP/PS1 mice, and dephosphorylated Nedd4L ubiquitinates a mitochondrial outer membrane protein mitofusion-2 (MFN2). Previous studies have observed downregulation of MFN2 in post-mortem patient samples and animal models of AD and suggest a contribution of MFN2 downregulation in AD pathogenesis. Based on these data and prior research, we hypothesize that dephosphorylation of Nedd4L in APP/PS1 mice contributes to impaired mitochondrial function and neurodegeneration in part through ubiquitinating MFN2. We will test this hypothesis with two aims. Aim 1 will employ multiple unique mouse models to determine how the two major isoforms of Nedd4L differentially regulate mitochondrial functions and how dephosphorylation of Nedd4L affects those functions. Aim 2 will employ a cell-permeable peptide to promote phosphorylation of endogenous Nedd4L in vivo to assess the impacts of Nedd4L dephosphorylation on neuronal functions and health in APP/PS1 mice. Our goals are to establish Nedd4L as an upstream regulator contributing to the dysfunctions of MFN2 and mitochondria in APP/PS1 mice and to introduce approaches that can rectify or ameliorate the impairment.

NSF Awards · FY 2024 · 2024-08

Some languages use a part of speech called an article. For example, the definite article “the” and indefinite article “a/an” are the first and fifth most used words in the English language, respectively. Speakers of languages that do not have articles have great difficulty acquiring them in languages that do have articles, despite how frequently they may be exposed to articles in the second language input. As some of the most spoken first languages in the world do not have articles, this is an important puzzle worth addressing from both theoretical and pedagogical perspectives. This doctoral dissertation project combines application of language acquisition theory, psycholinguistic methods, and pedagogically-informed instruction and feedback to investigate a well-known, but unsolved, problem in second language acquisition research. Other benefits to society include an educational opportunity for training in second language acquisition research and the public distribution of resources for second language instruction. The definite article “the” can only be used if a noun names an entity which is unique and known to both the hearer and speaker; otherwise, “a” is used (with singular nouns). However, there is evidence that learners incorrectly use “the” when they instead intend to convey that the entity named by the noun is known to the speaker. This doctoral dissertation study builds on these prior findings by examining how article learning is influenced by the linguistic focus of instruction (about different properties of articles), as well as by the pedagogical focus (on language comprehension versus language production). Learners of English complete a six-week treatment that teaches them to pay attention to speaker versus hearer knowledge, or to uniqueness versus lack thereof. The two groups are further subdivided into subgroups whose instruction emphasizes language comprehension versus language production. The learners take a battery of tests that measure their explicit and implicit knowledge of English articles before and after the instructional intervention, as well as five months later (to measure long-term retention). The results of these experiments contribute to and expand the use of psycholinguistic methods in theoretical and applied second language acquisition 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.

NSF Awards · FY 2024 · 2024-08

The Industry-University Cooperative Research Center for Earth Greenhouse Gas Reduction and Utilization (CEGRU) proposes an integrated collaboration between two sites, the University of Illinois Urbana-Champaign and the Oklahoma State University, to conduct science and engineering research to reduce greenhouse gas concentration in the atmosphere through prevention, mitigation, utilization, and storage. The CEGRU core is composed of faculty and students from two universities working with diverse interested entities including private sector companies, government agencies, and national research labs. Community organizations and stakeholders will also be engaged with a purpose to minimize the effect of methane and other greenhouse gases on climate change and its consequent impacts. The Center will seek to accomplish this goal by performing transdisciplinary basic research to advance greenhouse gas subsurface reservoir resource assessment and security via improved understanding, the development of novel methods of energy and greenhouse gas storage in geologic media, and carbon dioxide storage in manufactured materials. Recognizing that the energy transition from petroleum to other sources requires tradeoffs that could potentially have different negative impacts, this Center will pursue integrative research to evaluate and track greenhouse impacts to air quality, water, climate, and society. Broader impacts of the Center include community engagement which is integral to all activities. This approach effectively identifies key stakeholders for success of projects, with efforts that focus on understanding community and stakeholder sentiment working to include their collaboration on problem resolution and the development of and relationship to greenhouse gas management strategies. The Center’s engagement of undergraduates, graduate students, and postdocs in research activities will contribute to the development of the workforce of the future which is required to support net-zero greenhouse gas goals by 2050. Active collaborations with Minority-Serving Institutions will promote equitable training opportunities for a diverse future workforce. Center efforts will result in much needed increased diversity and equitable improvements in the research community as well as engagement with communities in areas that will be impacted communities where technologies will be implemented. The Center Lead Site at the University of Illinois will lead research to refine greenhouse gas (methane, hydrogen, etc.) storage resource assessment by building on its extensive expertise in carbon storage and oil and gas exploration. The research will leverage the Illinois State Geological Survey’s well-established relationships with local operators, the energy industry, and the US Geological Survey. Its work will integrate University of Illinois expertise in rock mechanics and earthquake characterization and prediction to provide risk assessment methodologies. It will also employ machine learning methods to its and its partner Site in Oklahoma's extensive library of oil and gas wells to develop risk assessments that can be used for prediction of leakage and induced/triggered seismicity. The wellbore completion technology research and seismic reflection data processing by its Oklahoma partner Site will be integrated into estimates of the storage volume of various geologic formations, a critical component of greenhouse gas resource/storage/and leak assessment. The Illinois Site of the proposed Center will work with the Oklahoma Site to collect, integrate, and analyze the potential utilization of legacy and marginal wells for greenhouse gas mitigation and to improve understanding of the role of completions in leakage development. An additional Center focus will be the drone-based detection and measurement of methane gas emissions from existing and orphan oil and gas wells. 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 · 2024-08

Researchers at NewHaptics Corporation and the University of Illinois at Urbana-Champaign are developing and testing software and hardware tools for faculty, staff, postdoctoral research fellows, and graduate students who are blind or have low vision, and who are working in college and university Chemistry, Mathematics, and Computer Science and Engineering and Information Sciences employment settings. These tools, which use a combination of hearing and touch technologies, are making it possible for people who are blind or have low vision to generate, identify, and manipulate digital data patterns and trends. Given the scarcity of STEM scientists, researchers, and educators in our country, opportunities to increase knowledge about better data use technologies is essential to the retention and advancement of students and professionals who are blind or have low vision. The research being conducted aims to: 1) Develop new hardware interaction components and firmware/drivers; 2) Develop new interaction software, and 3) Evaluate the system and conduct workshops for users. The project activities are addressing the following key research questions: 1) How can the addition of spatial information, realized through multiline braille and large array tactile graphics hardware, coupled with interactive software tools, break down barriers to data use? 2) What design considerations contribute to multi-modal data, that is, verbal, sonification, and tactile data representations that go beyond a single perceptual modality? Answers to these questions have the potential to contribute to research about STEM postsecondary workplace solutions for people with disabilities. This award has been made in response to the NSF solicitation “Workplace Equity for Persons with Disabilities in STEM and STEM Education” (NSF 23-593). The project is funded by the Division of Equity for Excellence in STEM’s Alliances for Graduate Education and the Professoriate program. 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 · 2024-08

Computers and computational methods are an increasingly important part of the scientific endeavor, and they are changing the ways in which science progresses. One new and important such methodology is that of validated numerics. Traditional numerical methods produce approximate solutions to the equation of interest. While these methods usually produce very good approximations to the exact solutions, they typically do not have explicit bounds on the error. In validated numerics the goal is to produce an approximate solution along with an explicit guarantee that the error is no larger than some prescribed tolerance. In practice realizing such a validated numerical method requires both new mathematical analysis and new computational techniques. On the computational side, for instance, rather than doing the standard floating point arithmetic one must instead do interval arithmetic, where the result of a calculation is not a single number but an interval in which the result is guaranteed to lie. While these validated numerical calculations are much more difficult to carry out than standard numerics, the advantage is that one has a mathematical proof of the correctness of the solution. This means that there are many questions about the behavior of solutions to equations to which one can give a mathematically rigorous numerical proof. The investigators study some equations that govern nonlinear wave phenomenon, such as the propagation of a wave in the ocean or light in an optical fiber. Often one can find an exact special solution to these equations, such as a wave that propagates without changing its shape. One would like to know if this solution is stable: if solutions that begin close to this known solution remain close. Stability is an important question from the point of view of applications, as it determines whether these solutions are likely to be observed in practice. The investigators study stability via validated numerics. An important part of this proposal is training graduate students in these increasingly important techniques. The investigators address the stability of periodic traveling waves in Hamiltonian PDEs. One project establishes that the essential spectrum of the associated linearized operator to solutions of the generalized KdV and nonlinear Schrödinger equations is purely imaginary. This represents the first time that the essential spectrum has been calculated rigorously for such operators arising from non-integrable equations away from the small amplitude limit. This approach will extend to encompass other equations, including but not limited to regularized long wave type, the Benjamin-Ono and Camassa-Holm type, and two-dimensional equations. Furthermore, we aim to advance from spectral to linear stability, revealing the long-term dynamics of the solutions of the associated linearized equation. 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 · 2024-08

This collaborative project aims to develop new distributionally robust quadratic optimization models and methodologies to seamlessly integrate highly uncertain renewable energy into power systems, thereby providing society with cleaner, more reliable, and cost-effective energy solutions. Distributionally robust optimization (DRO) has emerged as a leading framework for optimizing under uncertainty, which can ensure the satisfaction of specified requirements even under adverse distributions of random parameters. Despite its advantages, applying DRO to nonlinear optimization problems under uncertainty remains challenging due to their inherent complexity. Nonlinearity is prevalent across various critical real-world applications in the US economy, beyond just power systems. This collaborative project will bridge this pressing gap by formulating new DRO models tailored to the unique characteristics of power systems and developing computationally efficient approaches to solve DRO problems with quadratic constraints. The project aims to deliver practical solutions that benefit all stakeholders and end-user communities. Its broader impacts include: (i) integrating research and education at the University of Arizona (UArizona) and the University of Illinois Urbana-Champaign (UIUC) by involving undergraduate students in hands-on research, connecting results with practical applications to inspire STEM careers; (ii) enhancing graduate-level education at UArizona and UIUC with contemporary case studies; and (iii) facilitating technology transfer to other societally important industries in the US economy, such as finance and transportation, where effective management of high uncertainty is crucial. This project will utilize distributionally robust optimization to address two critical decision-making challenges in uncertain power systems: (1) alternating current optimal power flow, and (2) power-system generation planning and operations, both of which are inherently formulated as quadratically constrained quadratic programming (QCQP) problems. The research objectives and tasks include: (1) developing new conic reformulations for distributionally robust QCQP problems; (2) designing efficient algorithms that exploit the special structure of these conic reformulations; and (3) adapting and applying these solutions to effectively address key optimization challenges in uncertain, large-scale power systems. These advancements aim to significantly enhance the computational efficiency and practical applicability of DRO solutions in real-world operational settings. 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 · 2024-08

Per-and polyfluoroalkyl substances (PFAS) have been a key part of semiconductor manufacturing due to their unique physical and chemical properties. PFAS are currently essential for processes including photolithography and etching, and are used as auxiliary fluids in pumps and lubricants. While there are efforts to find alternatives for fluorochemicals, their unique physicochemical properties make replacement difficult. As such, PFAS use in semiconductor manufacturing currently poses a challenge for balancing supply-chain security and environmental sustainability. The current workshop is proposed to provide a platform for experts to identify, discuss, and prioritize topics of urgent concern for the detection, removal, and safe destruction of PFAS from semiconductor manufacturing streams. A key goal of this workshop will be to identify the research needs for PFAS treatment that are unique to the semiconductor industry and provide a roadmap for future PFAS treatment technologies to ensure environmental sustainability. By bringing experts from academia, industry, and national agencies, the workshop aims to cover three critical areas for PFAS abatement: (i) discuss and review analytical methods to characterize the PFAS-containing materials used in the semiconductor industry and detect the releases of PFAS in the waste streams, (ii) brainstorm and create roadmaps to guide the cost-effective and low-energy technologies for PFAS removal and separation in semiconductor waste streams, especially short-chain PFAS, and (iii) provide insights into the future opportunities and challenges for energy-efficient and cost-effective PFAS destruction methods. The proposed workshop will support education and awareness on PFAS as well as provide insights into PFAS related issues in semiconductor manufacturing. Products from the workshop will have a clear impact on environmental sustainability and water quality through addressing an urgent topic of PFAS abatement in a critical manufacturing industry. 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 · 2024-08

With the support of the DMREF Program and the Division of Chemistry, Professor David J. Masiello from the University of Washington, Professor Stephan Link from Rice University, and Professor Katherine A. Willets from Temple University are developing methods to theoretically design and experimentally realize a new class of periodic 1D and 2D thermal metamaterials. Thermal energy, or heat, flows naturally from hot to cold, making it difficult to create localized thermal “hot spots” even when heat is applied to a single location. Said differently, the degree of spatial correlation between the heat power supplied and the temperature change that it induces is likely to be small. Touching a hot pan’s lid provides a simple and all too familiar example of this effect. As a material’s size is reduced to 10-100s of nanometers, or about 1,000 times smaller than the width of a human hair, depositing and maintaining thermal energy within a small region of space becomes even more challenging. Yet, the ability to control heat flow and thus temperature at both nanoscale (<100 nm) and micron-scale (~1-100 μm) dimensions has important implications for applications ranging from big data to nanomedicine. This research project aims to overcome thermal diffusion and achieve long-range global control of spatially-nonuniform heating, using only light to actively control the thermal profile of the materials. Beyond impacting a wide variety of applications, the project will facilitate the interdisciplinary training of students and postdoctoral researchers through student exchange between the three research groups, organization of two new scientific meetings, and the design of a nanotechnology summer camp for middle school students with focus on photothermal materials. The goal of this project is to overcome thermal diffusion through the theoretical design and experimental realization of a new class of periodic 1D and 2D thermal metamaterials. Plasmonic nanoparticle unit cells that are individually capable of hosting spatially-controllable nanoscale thermal profiles will be integrated into periodic lattices, which introduces the possibility for long-range global control of spatially-nonuniform heating upon optical excitation. To achieve this goal, the research team will (i) expand the design and thermal characterization capabilities for multi-particle unit cells that exploit near-field coupling; (ii) engineer photonic band structure to sculpt long-range thermal profiles in 1D and 2D Bravais lattices using light; and (iii) integrate multiple sub-lattices to realize 1D and 2D non-Bravais lattices to actively control both nanoscale and micron-scale thermal profiles using light. Realization of such thermally-active materials will require the coordinated and iterative efforts of a highly-skilled team capable of integrating new theoretical methods for predicting how light energy is transduced into modified thermal profiles with experimental fabrication and characterization techniques to design and quantify temperature across decades of length scales, spanning from below the diffraction limit to millimeters. This project will leverage the iterative theory-experiment-theory feedback loop to expand the genome of actively-controllable photothermal materials. 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 · 2024-08

The design of heterogeneous catalysts for specific chemical reactions, along with ideal conditions for carrying out targeted reactions, is aided by a detailed understanding of the specific steps by which the reaction occurs on the catalyst surface. Such steps control the overall rate (i.e. kinetics) of the reaction, as well as the product selectivity in cases where multiple products are produced. Accurate kinetic models are crucial for research and development in heterogeneous catalysis. In recent years, there has been growing appreciation of the complexity of surface catalyzed reactions, thus prompting the evolution of microkinetic models (MKMs) as a tool to predict ideal combinations of catalyst design and reactor operating conditions. Two prevailing types of MKMs have emerged representing different levels of complexity and accuracy: 1) Mean Field (MF-MKMs) - built on a one-site basis, and 2) kinetic Monte Carlo (kMC) simulations with hundreds of sites. The project investigates a novel periodic tiling framework that retains the convenience of closed-form MF-MKM expressions, while accurately including complexities that have previously required kMC simulation. More broadly, the project will advance computational modeling across the full spectrum of heterogeneous catalysis, linking theory to experiment in a more robust catalyst and reaction engineering process, potentially scalable from the molecular to the process level. Tremendous progress has been made with simple mean-field microkinetic models (MF-MKMs) that ignore correlations between molecules on the catalyst surface. The other fruitful direction has been kinetic Monte Carlo (kMC) simulations that track every interaction and every detail of the catalytic process. MF-MKMs yield simple closed-form rate expressions. They are convenient for reactor design, process optimization, and for understanding trends in temperature, concentration, and barriers via derivative-based quantities like activation energies, reaction order, and degree of rate control. However, MF-MKMs cannot accurately describe reactions where adsorbates interact with each other and where surface diffusion is important. The current alternative kMC framework remains accurate for highly complex surface reactions but its statistical rate estimates are less convenient than a closed-form MF-MKM rate expressions for all of the aforementioned tasks. The project develops a generalizable way to partition the surface into linear periodic tiles, to automate the formulation of the master equation including adsorption, reaction, diffusion, and desorption steps (with adsorbate interactions of any strength), and to exactly solve the master equation. The new approach can yield exact solutions and closed-form rate expressions for complex surface reactions that are notoriously difficult for MF-MKMs. Preliminary results show that the tiling approach also yields rates that are essentially indistinguishable from numerically exact kMC results. The project applies sparse matrix tools and an automatic differentiation strategy to facilitate activation energies, reaction orders, degree of rate control, and data fitting / parameter estimation tasks. Maps will be created linking the linear tiles to conventional periodic surface models so that the new tiling models can be parameterized with ab initio calculations. This new framework should become a powerful alternative to kMC models in the frequently encountered situations where MF-MKMs are inadequate. 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 · 2024-08

The Louis Stokes Alliances for Minority Participation (LSAMP) program assists universities and colleges in their efforts to significantly increase the numbers of students matriculating into and successfully completing high quality degree programs in science, technology, engineering and mathematics (STEM) disciplines to diversify the STEM workforce. Particular emphasis is placed on transforming undergraduate STEM education through innovative, evidence-based recruitment and retention strategies, and relevant educational experiences in support of all students no matter their background. These strategies facilitate the production of well-prepared, highly qualified, and motivated undergraduate student graduates who pursue graduate education and/or careers in STEM. For the United States to remain globally competitive, it is vital that it includes the talent of all its citizens and provides exceptional educational preparedness in STEM areas that underpin the knowledge-based economy. The Southern Central Illinois Louis Stokes Alliance for Minority Participation (SCI-LSAMP) program has formed an alliance in response to the need for a more diverse and skilled technical workforce. That need still exists and is particularly acute in Illinois. The institutions that make up the Alliance include the University of Illinois Urbana-Champaign (the lead institution), Bradley University, Illinois State University, Illinois Wesleyan University, Southern Illinois University-Carbondale, and Western Illinois University. Through this project, the Alliance will implement activities to improve academic performance, build community, ease transitions from undergraduate to graduate education or careers in STEM. By the end of the project, SCI-LSAMP will have made a transformational impact on numerous students in the central and southern part of Illinois, providing valuable cross-institutional knowledge on effective and sustainable strategies to facilitate seamless transitions and strengthen the persistence and success of all students in STEM, thereby increasing retention and graduation rates. 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 · 2024-08

The University of Illinois at Urbana-Champaign ADVANCE Adaptation project, I-ADVANCE, will induce positive, sustainable, systemic change for STEM faculty by implementing evidence-based practices that will foster a climate of equity in UIUC’s STEM departments. The project will develop a sustainable, data-driven, systemic change model that removes informational barriers and builds equitable workspaces. Guided by rich institutional data, I-ADVANCE seeks to reduce disparities in recognition and rewards for faculty work; to increase capacity, resources, and accountability for equity-minded decision making by department leaders; and empowering majority faculty to foster an equitable and inclusive department climate. The project seeks to adapt evidence-based strategies to (1) create a centralized Faculty Workload Dashboard for better transparency and accountability of unrecognized work; (2) educate and develop Faculty Fellows to conduct systemic equity work directly in their home units; and (3) launch an Advocates and Allies network to shift the institutional culture toward one in which everyone contributes to equity and inclusivity. Formative and summative assessment will be conducted by internal and external evaluators, and additional guidance will be provided by internal and external advisory groups. The project can serve as a model for other research-intensive land grant institutions. The NSF ADVANCE program is designed to foster gender equity through a focus on the identification and elimination of organizational barriers that impede the full participation and advancement of diverse faculty in academic institutions. Organizational barriers that inhibit equity may exist in policies, processes, practices, and the organizational culture and climate. ADVANCE “Adaptation” awards provide support for the adaptation and adoption of evidence-based strategies to academic, non-profit institutions of higher education as well as non-academic, non-profit organizations. 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 · 2024-08

This Capacity Building project aims to serve the national need of identifying, developing, and supporting highly qualified physics teachers to teach in high-need secondary schools, addressing a physics teacher shortage that has resulted in inequities in student preparation for post-secondary STEM coursework. This project builds physics teaching workforce capacity by developing holistic recruitment mechanisms, prospective teacher training, and support for physics teachers in high-need secondary schools. This secondary physics teaching capacity building project aims to pursue a Noyce Track 1 pathway for a more diverse group of students to enter and succeed in post-secondary STEM. This University of Illinois Urbana-Champaign project includes partnerships with the following high-need Illinois high schools: Champaign Central, Armstrong, Rantoul, Champaign, Danville, and Urbana. Project goals include building capacity to (a) recruit and train undergraduate STEM majors to become physics teachers and (b) support them during in-service teaching to maximize long-term retention. First, to aid recruitment, this project aims to double the active undergraduate membership in the aspiring physics teacher group (Future Leaders in Illinois Physics Teaching – FLIPT) and expose members to physics teacher presentations and classroom observations at high-need schools. Second, to aid preservice training, this project will solidify partnerships with high-need schools, including a prospective physics teacher scholarship program for student teaching. Finally, to establish clear pathways for aspiring physics teachers between waypoints of interest, induction, preservice training, and in-service support, this project will develop explicit connections between three existing departmental infrastructures: (a) FLIPT, (b) the Learning Assistant (LA) Program in which undergraduates co-instruct physics labs, and (c) the NSF-supported Illinois Physics and Secondary Schools (IPaSS) in-service physics teacher community. Opportunities for shifts in student attitudes toward physics teaching careers are particularly ripe at large universities serving over 1,000 STEM majors in their introductory physics courses each semester. This project will survey students in introductory physics courses, the LA program, and FLIPT about their beliefs surrounding physics teaching careers and use this information to design a recruiting strategy. If successful, the strategy will be shared with other physics departments. This project models effectively aligned collaboration between departments of physics and education that can make secondary physics teaching a more visible and viable career choice within large research universities. This Capacity Building project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. 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.