University Of California Los Angeles

NSF Awards · FY 2026 · 2026-02

With the support of the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Louis Bouchard at the University of California Los Angeles will pioneer a measurement strategy poised to transform routine nuclear-magnetic-resonance (NMR) spectra into nanoscopic probes of molecular motion inside pores thousands of times narrower than a human hair. By analyzing the intrinsic shape of the NMR signal rather than relying on highly specialized and inaccessible hardware that generates extremely strong magnetic-field gradients, the project would allow researchers to quantify surface and bulk properties of porous materials, estimating pore dimensions, viscosity, and surface adhesion in catalysts, energy-storage devices, filtration media, and even human lungs. The effort would engage graduate, undergraduate, and high-school students in simulations, bench experiments, and open-source code development. Outreach activities, including hands-on demonstrations in local middle schools, would introduce K-12 learners to molecular imaging and highlight career pathways in chemical measurement science. Technically, the research team would integrate atomistic molecular-dynamics simulations with a generalized Langevin equation whose memory kernel is obtained from Green–Kubo time-correlation integrals, generating time-dependent diffusion coefficients and velocity-autocorrelation functions that preserve the molecular “memory” of prior collisions, surface encounters, and long-ranged hydrodynamic interactions in confined gases and liquids. These computed diffusion fingerprints would then be benchmarked against variable-temperature NMR spectra collected under carefully calibrated weak residual gradients in well-characterized porous media, including inverted-colloidal-crystal scaffolds, mesoporous silica, and single-wall carbon nanotubes, to ensure that the model reproduces experimental linewidths across a wide range of temperatures, pore diameters, and interaction strengths. By expressing the Green–Kubo response as separable functions of temperature, pore geometry, and intermolecular-potential depth, the investigators would feed those parameterized functions into an evolutionary optimization engine that iteratively adjusts the simulated spectrum until it converges on the measured lineshape, thereby solving the inverse problem of extracting nanometer-scale pore size, viscosity, and surface adhesion energies directly from a single NMR scan. Cross-validation against electron microscopy and gas-adsorption benchmarks would quantify uncertainties and refine the model. Once validated, the framework would be generalized to frequency-encoded pulse sequences and embedded in diffusion-weighted MRI protocols, furnishing clinicians, materials chemists, and energy researchers with a physics-based route to interpret non-Markovian transport in biological tissue, catalytic reactors, battery electrodes, and filtration membranes without relying on empirical calibrations, thus opening new avenues for quantitative molecular imaging and rational microstructural design. 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 2026 · 2026-02

The ovary is the fastest aging organ in humans. Some of the leading challenges to women’s health, including fertility decline, menopause, and aging-related diseases, are the inevitable consequences of this phenomenon. Identifying mechanisms that drive ovarian aging and developing strategies for their mitigation, therefore, have the potential to dramatically improve women’s health. Bats, which maintain reproductive health throughout their exceptionally long lifespans, offer a unique and powerful mammalian model for identifying mechanisms of ovarian resilience. Unlike traditional models such as mice, which experience rapid ovarian decline, many female bats sustain ovarian function and fertility into old age, despite being among the longest-lived mammals for their body size. However, the mechanisms underlying bats’ prolonged reproductive health remain poorly known, in part due to historical challenges in estimating age in wild bats. This project overcomes this limitation by utilizing newly developed methylation-based clocks that allow accurate chronological age estimation in wild bats. Long-term goals of this research are to develop bats as new models for ovarian aging and leverage their unique traits to develop bat-informed strategies to extend ovarian health-span in women. As a first step toward these goals, this project will pursue two aims. The first aim is to establish select bat species as models for ovarian aging in long-lived mammals, following three criteria. In preliminary fieldwork, eight bat species were identified that meet the first two criteria. This project will interrogate these species’ fit to the third criterion: they have long, sampleable lifespans over which they minimize ovarian aging. The hypothesis is that some of these bat species fulfill all criteria for adoption as long-lived, mammalian models for ovarian aging. To test this, for all sampled bats, chronological age will first be estimated with the new methylation-based clocks, and ovarian aging then characterized using markers of fibrosis, inflammation, and senescence. The second aim is to characterize primordial follicle dynamics in all sampled bats. In preliminary work, an abundance of primordial follicles was noted in the ovarian cortex of adult bats, far exceeding that typical in adult mice and humans. This is highly significant, as the primordial follicle pool is the ovarian reserve. Building on this, the hypothesis is that extended ovarian health in bats is associated with sustained maintenance of quiescent primordial follicles in the ovarian reserve with age. To test this, follicular quiescence, activation, and apoptosis and the proportion of primordial, transitioning, and primary follicles will be assayed over time in bat ovaries, using CB6F1 mouse ovaries as controls. This project is expected to establish select bats as transformative models for ovarian aging with the potential to uncover biological insights beyond the reach of traditional, short-lived models like mice. This project is also expected to characterize the dynamics of the primordial follicle pool in bats, identifying mechanisms that could sustain ovarian reserves and reproductive health. Ultimately, this project is expected to seed future NIH R01 proposals to develop bat-inspired therapies to extend women’s health and fertility.

NSF Awards · FY 2026 · 2026-02

Oaks dominate many ecosystems of California and the United States, making them crucial to biodiversity and economically valuable for timber products, such as lumber and furniture wood. Recent research has demonstrated that oaks are maladapted to current environmental conditions, being better adapted to cooler environments present 20,000 years ago. This unexpected maladaptation results in tree mortality, reduced growth, and increased susceptibility to pathogens. The project investigates the understudied concept of oak maladaptation by linking physiological traits, tree performance and fitness, and genomics. This project will investigate two widespread, ecologically important California tree oak species--Quercus lobata (valley oak) and Q. agrifolia (coast live oak)--which often grow together, but have different physiological responses to temperature and drought. Project goals are to understand how physiological traits determine tree growth, survival, and reproduction, whether these traits are genetically based, and how the underlying genetic gradients across the landscape can be used to predict which tree populations are most vulnerable and which are most resilient. Findings will both inform management strategies for oak restoration and conservation in areas where oaks have been removed by harvest or wildfire and also provide a case study for other forest tree species. The research will enhance the STEM workforce by educating students and postdoctoral scientists in cutting edge concepts and tools in integrative biology, ecology, evolution, and forestry. This project is an integrative study of the mechanistic and fitness response of trees to their environments using the understudied but ubiquitous phenomenon of maladaptation. Trees are particularly vulnerable to maladaptation due to their long generation time and long life span that can result in individuals being out of sync with current environments. Through these two contrasting California oaks, the project will first identify the physiological traits associated with response to high temperatures and drought through greenhouse and field experiments. Second, the studies will see whether traits contribute to the survival and growth across a tree’s life history of seedlings, saplings, and young adult trees, and determine how much key traits are genetically based. Third, a landscape genomic study will be conducted to identify geographic regions of maladaptation for each species based on genomic markers, and test whether these areas are the same as predicted to show maladaptation using empirical findings from physiological and fitness studies. This information can be used to identify seed sources for restoration and management of oak projects. This research will address the knowledge gap between selective processes and mechanisms affecting adaptation versus maladaptation. It will also demonstrate the innovative use of landscape genomics tools to detect maladaptation across a species range. By linking phenotypic mechanistic findings and relative fitness of trees with genomic information, this project will demonstrate how functional genomics can inform tree conservation. 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 2026 · 2026-02

With this award, the Chemistry of Life Processes Program in the Chemistry Division is funding Dr. Steven Clarke from the University of California, Los Angeles to investigate how organisms enhance their resilience and survival by modifying proteins inside cells. Proteins are long chains of amino acids that are folded in precise manners to carry out their functions inside cells. Organisms have also developed ways of modifying specific amino acids to expand “nature’s inventory” of chemical structures in proteins. These modifications are important for optimizing protein function and organismal survival when placed under stress. This project is focused on enzymes that catalyze the transfer of simple methyl groups to arginine amino acid residues, with the goal of defining how this chemical modification works in harmony with other modified amino acids to control how genes turn on and off in cells in non-physiologically cold or basic pH conditions. This project contributes substantially to the training of undergraduate students, graduate students, and postdoctoral scholars, preparing them for independent careers in defining chemical processes within cells and understanding cell survival strategies. Outreach activities target for training the young Americans who represent the next generations of researchers. This research project focuses on posttranslational modifications of proteins and specifically characterizes the role of mammalian protein arginine methylation and its relationship with other covalent modifications. The project is centered on the modifications catalyzed by the protein arginine methyltransferase 7 (PRMT7), a unique enzyme in the family PRMT that exclusively forms the monomethyl arginine (MMA) derivative, specifically in R-X-R sequences. PRMT7 is most active under conditions of physiological stress. However, there is currently a large knowledge gap in understanding the physiological function of MMA, particularly in relationship to other modifications (e.g. phosphorylation) of nearby amino acid residues. A central question is whether the MMA modification is stable and required for normal protein function or whether it represents a reversible modification that can regulate protein function. The biochemistry of the interactions of PRMT7 and protein kinases need to be studied to distinguish between regulatory roles and structural roles of protein methylation. Histones, the “poster children” in crosstalk among different protein modifications, undergo arginine and lysine methylation, lysine acetylation, lysine ubiquitylation, and serine and threonine phosphorylation. These modifications make up the “histone code” that determines which genes are activated. The interactions of protein arginine methylation with other types of histone modifications need to be defined. While there is clear evidence for regulatory mechanisms of many protein kinases and other types of modification enzymes, there is little understanding of how the activity of the PRMT enzymes are regulated. PRMT7’s in vitro activity is activated under cellular temperature stress (cold) and pH stress (alkaline) conditions, but nothing is known about PRMT7 regulation in vivo. Experiments are proposed to identify PRMT7 substrates in living cells and determine their stoichiometry in response to stress conditions. Particular attention is paid to identifying small molecule or protein activators of PRMT7. The results of these studies can distinguish regulatory versus functional roles for protein arginine methylation. 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 2026 · 2026-02

With no effective therapy to date, the ongoing Type 1 diabetes (T1D) epidemic continues to be a major health problem. While immune therapeutics hold great promise for the treatment of T1D, their inadequacy, serious toxicity, side effects, and morbidity have limited research efforts in the lifelong immunosuppression approach. This shortcoming has prompted investigators to search for alternative approaches. Targeted nanomedicine using polymeric nanoparticles (NPs) holds particular promise to enhance the delivery of immune therapeutics to treat T1D. This strategy can minimize the undesirable side effects of immune therapeutics by delivering them to diseased tissues, where they can undergo sustained release. In this multidisciplinary project, we aim to develop an innovative, targeted nanodelivery method for immune therapeutics for T1D. Although progress has been made in developing new formulations, a method of targeted delivery of NPs to specific tissue sites following systemic administration remains to be developed. The priming and activation of autoreactive T cells occurs in the pancreatic lymph nodes (PLNs), where naive T cells enter through lymph node (LN)-restricted vasculature known as high endothelial venules (HEVs) and encounter autoantigens from the pancreas presented by dendritic cells. Activated T cells traffic subsequently to the pancreas, causing insulitis and autoimmune diabetes. Notably, we have found that HEVs are also formed in the pancreas during the onset of diabetes in NOD mice. Here, for the first time, we have developed a nanodelivery of therapeutics to PLN and Pancreata of NOD mice targeting HEV with intra venous injection. We have generated a novel mAb and scFV against the peripheral node addressin (PNAd), a glycoprotein family expressed only by endothelial cells of the HEV. We also provide human data that supports the clinical applicability of our delivery platform. Moreover, our preliminary data shows that delivery of anti-CD3 antibody using our HEV targeted unprecedently increases the efficacy of anti CD3 in suppressing autoimmune diabetes in NOD mice. Our main hypothesis is that targeted delivery of anti-CD3 to the pancreatic lymph nodes (PLNs) and pancreata will increase its efficacy and decrease toxicity by reducing systemic dosing significantly. In Aim 1, we will examine and optimize the stability, binding efficacy, and biodistribution of anti HEV mAb-conjugated NPs in NOD mice. In Aim 2, we will assess the clinical efficacy and the mechanisms by which the delivery of anti-CD3 using anti HEV mAb- conjugated NPs reverse autoimmune diabetes in NOD mice. In Aim 3, we plan to test the binding capacity to the PLNs and pancreata of human T1D patients of our optimized anti HEV mAb-conjugated NPs. This multidisciplinary, collaborative approach will lay the groundwork for the introduction of an innovative, targeted delivery method of immune therapeutics for T1D.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY Iron is essential for a healthy pregnancy. During pregnancy, iron requirements increase substantially to support the development of the placenta and fetus as well as maternal blood cell volume expansion. Insufficient iron and ensuing iron deficiency anemia is linked to adverse pregnancy outcomes, including increased maternal mortality, perinatal death, and preterm birth. Hepcidin regulates systemic iron homeostasis by controlling dietary iron absorption, the release of iron from iron recycling macrophages, and the release of iron from hepatic stores. During pregnancy in humans and rodents, maternal hepcidin is profoundly suppressed, which is thought to maximize dietary iron absorption and mobilization of iron from stores for transfer to the developing fetus. Augmenting maternal hepcidin in mouse pregnancy by administration of hepcidin analogs led to severe embryo anemia or even death. Thus, maternal hepcidin suppression is essential for maternal and embryo iron homeostasis and health. Despite its importance, the mechanism(s) responsible for hepcidin suppression remain elusive. The goal of this project is to identify the pregnancy-related hepcidin suppressor. Specific Aim 1. Identify novel hepcidin regulators produced by the trophoblast – We identified over 600 proteins in our LC-MS/MS analysis of hepcidin suppressive fractions purified from human trophoblast supernatant and placental interstitial fluid. We will perform a high throughput cDNA overexpression screen of individual candidates to identify all proteins that modulate hepcidin expression in hepatocytes. Specific Aim 2. Investigate the role of HGF in hepcidin suppression during pregnancy – Our lab previously discovered that HGF suppresses hepcidin in vitro, but the physiological context for the role of HGF in iron metabolism was lacking. We now detected HGF in the placental LC-MS/MS screen and will characterize its role in hepcidin suppression in vitro and in vivo. In vitro, we will i) deplete HGF from suppressive trophoblast-derived conditioned media and placental interstitial fluid and ii) inhibit HGF signaling in hepatocytes treated with trophoblast media and placental interstitial fluid, and in each case assess their ability to suppress hepcidin. In vivo, we will i) measure HGF concentrations and the proportion of pro- HGF and mature-HGF in maternal circulation across gestation and ii) inhibit HGF receptor MET at the gestational age when maternal hepcidin suppression is initiated and assess hepcidin suppression and serum iron changes. When completed, these studies will provide fundamental insight into the regulation of iron homeostasis during and even outside of pregnancy, with broad translational potential for treatment of iron disorders.

NSF Awards · FY 2026 · 2026-02

This award provides support for participants in the workshop "Bridging the gap between NISQ and FTQC", to be held February 17-20, 2026 at the Institute for Pure and Applied Mathematics, UCLA. Quantum computing is a rapidly developing fields that aims to utilize quantum effects to perform complex computations efficiently. Modern day quantum computers can manipulate thousands of qubits (quantum analogs of bits, the elementary units of data storage). Currently available quantum computers are noisy and errors occur during computation — this is the era of so-called “noisy intermediate-scale quantum” computers (NISQ). As noise suppression techniques and quantum error correction algorithms mature, we are entering the stage of so-called Fault Tolerant Quantum Computation (FTQC). The aim of the conference is to discuss this transition and plot a course towards useful quantum computation at scale. Increasingly sophisticated experimental demonstrations of the primitives associated FTQC have made it clear that the field is beginning to move out of the NISQ era first described by Preskill in 2018. The evident technical challenges of continuing to scale quantum computers are further complicated by the sometimes conflicting approaches associated with these two paradigms. Everything from software, to error suppression, to algorithm development, and prospective applications, seems to have distinct NISQ and FTQC perspectives. To continue towards the goal of utility-scale quantum computation, it will be essential to bridge this dichotomy. This workshop aims to understand the current state of the art in this period of transition and how to best prepare for early fault-tolerant quantum computers and beyond. Experts in a wide range of topics, including (but not limited to) quantum algorithms and applications, quantum architecture and error correction, and quantum hardware will be invited to contribute their perspective on the latest challenges facing the field. The worksop will focus particularly on the role that pure and applied mathematics can play in bridging the NISQ-FTQC gap, and better understanding when utility-scale quantum computation will be possible. More information on the workshop is available at https://www.ipam.ucla.edu/programs/workshops/bridging-the-gap-between-nisq-and-ftqc/ 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 2026 · 2026-02

This award provides support for participants in the workshop New Frontiers in Quantum Algorithms for Open Quantum Systems, to be held January 12-16, 2026 at the Institute for Pure and Applied Mathematics, UCLA. Quantum computing is a rapidly developing fields that aims to utilize quantum effects to perform complex computations efficiently. Modern day quantum computers can manipulate thousands of qubits (quantum analogs of bits, the elementary units of data storage). A quantum computer is then a quantum system that is controlled and regulated in a precise way to perform a computation. However, this system is not fully isolated from the rest of the world, and thus can be regarded as an open system — one that interacts with the environment. It turns out that this interaction can be leveraged and used to enable quantum computation. Conversely, quantum computers can be potentially used to simulate complex open quantum systems. The study of open quantum systems lies at the intersection of mathematics, computer science, physics, chemistry, and quantum engineering. From a more mathematical point of view, it also naturally brings together several branches of mathematics, including functional analysis (operator algebra), probability (as open quantum systems are quantum analogs of stochastic processes), applied mathematics, and mathematical physics. The primary goal of this workshop is to address the recent surge of interest in the quantum simulation of open systems, encompassing topics such as Gibbs sampling, ground state preparation, simulation algorithms, error corrections, and non-Markovian effects. This workshop aims to bring together experts from multiple backgrounds working at the forefront of open quantum systems, foster interdisciplinary collaboration, and create networking opportunities, particularly for young researchers. More information on the workshop is available at https://www.ipam.ucla.edu/programs/workshops/new-frontiers-in-quantum-algorithms-for-open-quantum-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.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY This application seeks partial support for the upcoming Society for Muscle Biology (SMB) conference entitled, "Skeletal Muscle Stem Cells in Development, Regeneration, and Adaptations", which will be held July 19-24, 20256 in Victoria, BC, Canada. This will be the 13th edition of this conference since 1998 focusing on skeletal muscle stem cells (MuSCs) and the 2nd sponsored by the SMB. This biennial meeting has traditionally been operated by FASEB, but shifted in 2024 to be organized under SMB to allow the organizers and participants more autonomy. This change facilitates a more robust and vibrant conference and a greater focus on supporting trainees. We expect approximately 125 attendees from around the world with at least ~60% junior researchers. No other scientific meeting has a primary focus on MuSCs. The need for a conference with this focus is demonstrated by the steady increase in attendance since the meeting's inception and the consistently excellent post-meeting evaluations provided by meeting attendees. This meeting attracts all leading MuSC researchers from around the world, further demonstrating its value for established and future leaders in the field. The overall objectives of this meeting include to: 1) provide a comprehensive analysis of recent discoveries in the field, with the goal of understanding the regulatory mechanisms controlling normal and abnormal functions of MuSCs in muscle development, homeostasis, regeneration, hypertrophy, aging, and myopathy; 2) create and foster an interactive environment for the exchange of ideas and unpublished data, so as to hasten discoveries and facilitate new and existing collaborations; 3) provide opportunities for junior investigators to present their work and network with senior investigators; and 4) facilitate career development of all career stages by ensuring representation in all aspects of the conference program. Our keynote speaker will be Dr. Peter Zandstra, a renowned engineer and stem cell biologist. Scientific sessions are planned, presenting 48 speakers (23 confirmed invited speakers and at least 25 selected from submitted abstracts) at all career levels. Session topics include: (i) Molecular regulation of MuSCs; (ii) Spatial and modeling innovation in MuSC analysis; (iii) MuSC epigenetics and transcriptional regulation; (iv) Engineering and translating muscle stem cells; (v) MuSC dynamics during development and muscle regeneration; (vi) MuSC niche biology; and (vii) MuSCs and other cell interactions in disease. Invited speakers have been selected for their scientific excellence, with particular attention to discipline, encompassing all trainee levels and geographical diversity. Speakers are explicitly required to present unpublished work, to ensure scientific discussion is at the forefront of the field. We will have poster sessions, each preceded by a “posters blitz” where poster presenters will give a one-minute talk to highlight their work, and career-oriented workshops and “meet-the-speaker” breakfast and lunch sessions. This meeting will provide a forum to foster discussion and cross-fertilization from diverse areas of research, to advance our understanding of muscle regeneration and aid in the development of therapeutics.

NIH Research Projects · FY 2026 · 2026-01

Project Abstract Motor impairments are one of the first signs of atypical development in infants who go on to have autism spectrum disorder (ASD) and are more closely tied to abnormal neurobiological processes in ASD. Motor behavior has high potential to serve as a measurable early behavioral difference to advance early detection of ASD—a necessary step for access to earlier and more effective interventions. However, standardized assessments of infant motor function have not been able to capture motor behaviors that are specific to ASD due to their focus on categorical ratings of milestone-attainment that are present across many neurodevelopmental conditions. There is a critical need for methods that objectively capture deeper aspects of underlying infant movements that are more sensitive and specific to ASD outcomes. The current study aims to use wearable sensor technology and advanced computational techniques to develop and validate objective, specific, reliable, and scalable measures of infant motor function that serve as predictive biomarkers of ASD, with the ultimate goal of advancing earlier detection and intervention that can improve long-term outcomes in ASD. The proposed study will use wearable sensors to measure motor development in the first year of life in infants at increased likelihood for ASD (ILA, defined as having an older sibling with ASD). Our team’s preliminary data strongly supports a theoretical model that lower sensor-based quantitative measures of infant movement symmetry and variability are specifically associated with later ASD outcomes, and that lower infant movement variability in the first year of life is associated with later forming repetitive motor behaviors (RMB) seen in ASD. We will enroll 120 ILA infants and examine an external validation cohort of infants. Infants will be assessed at 3, 6, 9, and 12 months of age with wearable sensors worn on bilateral upper and lower extremities and with standardized motor and behavioral assessments. Behavioral measures of ASD symptoms and developmental level will occur at 12 and 24 months. The assessments from 3-12 months will occur in the infant’s home, capture ecologically valid movement data, and remove barriers for participation for rural and underserved populations that cannot easily access major academic areas. We will apply sophisticated signal processing and machine learning techniques on the multidimensional quantitative infant movement data collected to: (1) validate our existing quantitative measures of infant movement variability (complexity and curvature) and symmetry; (2) create and validate new quantitative measures that improve detection of atypical movement characteristics across different developmental stages; and (3) advance early detection of ASD by creating prediction models that include the quantitative measures of infant movements and measures of other atypical behaviors associated with ASD. We will further examine the performance of our prediction models in briefer time subsets that mirror pediatric well child visits. This study has great potential to advance our understanding of motor impairments in ASD and drive a paradigm shift in scalable ASD identification in the first year of life.

NSF Awards · FY 2026 · 2026-01

Machine learning and artificial intelligence has been successful in the approximation and prediction of complex physical phenomena. A key aspect is the development of models capable of capturing dependencies on input parameters, domain configurations, boundary conditions, initial states, and spacetime coordinates within one neural network. One approach is operator learning, which encodes the solution operators of parametric partial differential equations into neural networks. However, the size of these neural networks often grows with the complexity of the task, that is, the accuracy of the methods can be limited by available computational resources and memory. This project aims to develop efficient algorithms with rigorous theoretical support for constructing accurate solution operators through the use of randomization techniques and numerical analysis. The tools developed will broaden the scope of machine learning applications in scientific modeling. In addition, the outcomes will contribute to curriculum development in both graduate and undergraduate mathematics as well as the training of graduate students in this research. The project is on the design, implementation, and analysis of randomized operator learning algorithms for enabling efficient and reliable scientific computation. In particular, this project will develop tractable training algorithms for solving parametric partial differential equations in Sobolev spaces with a focus on the noisy and limited data settings. The theoretical analysis will involve a comprehensive study of the algorithms' behavior in various settings, including quantitative bounds on the model's complexity, approximation accuracy, and generalization error. 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 2026 · 2026-01

The impact of (large) language models (LM) has been felt across a wide range of scientific domains. It is no exaggeration to state LMs have revolutionized several applications, including both commercial and scientific discovery. Among all the factors that contributed to the success of LMs, it is empirically clear that data is one of the most important. However, this relationship remains poorly understood and motivates foundational questions, given its importance to the success of LMs. Therefore, the future progress of LMs depends critically on the data, how they are obtained, selected, and utilized; this is the focus of this Mathematical Foundations of Artificial Intelligence (MFAI) project. If successful, this project could impact next-generation LMs, which, due to their increasing applications, may have far-reaching effects Traditional thinking views data primarily as an input for Language Models (LMs) during training. However, data can also be synthesized by a model, presenting it as an output. This dual role raises fundamental questions, including the role of synthetic data, the integration of exogenous data, and the impact of data quality on LM performance. Notably, the interaction between LMs and data is crucial, especially regarding in-context learning (ICL), which allows LMs to adapt to new tasks without altering their parameters. The project focuses on two tasks. Task 1 examines the interplay between synthetic and exogenous data in training, investigating the potential adverse effects of using synthetic data. It models this relationship as a dynamical system, employing control theory to optimize the use of diverse data types. Additionally, the project addresses the need for criteria to filter high-quality data for efficient learning and information retrieval. Task 2 aims to establish a mathematical foundation for in-context learning, enhancing its capabilities. This part develops an optimization perspective on ICL for various data types and reveals underlying structures through information theory, bridging domains of control, dynamical systems, and optimization. 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 2026 · 2026-01

PROJECT SUMMARY / ABSTRACT Malignant pleural mesothelioma (MPM) is a highly aggressive asbestos-related malignancy of the pleura for which effective therapy is highly limited. Recent clinical trials have shown that immune checkpoint inhibition (ICI) result in durable clinical benefit in ~50% of patients with MPM. Reciprocally, only ~50% of patients benefit at all from therapy and objective responses occur in less than 20% of patients. There are currently no reliable biomarkers (including PD-L1 expression) that identify individuals with MPM who are likely to respond to ICI and identification of such a pre-treatment biomarker prior could avoid unnecessary toxicity, triage non-responders to other treatment modalities, and extend long-term survival. We constructed a single cell atlas of immune organization in human MPM using time-of-flight mass cytometry (CyTOF) and identified two dominant cellular networks within its tumor immune microenvironment (TiME) that discriminated response and resistance to ICI. Based on the frequencies of key cell types from these opposing networks, we designed a “real-TiME” score for predicting the likelihood of response to ICI in MPM. To accelerate clinical translation, we developed and validated a bioinformatics platform to abstract this score from clinical tissue sections using imaging mass cytometry (IMC) and show robust prediction of response in a sample cohort of ICI-treated MPM patients. Further, mechanisms of response to ICI in MPM are unknown and their understanding will advance the care of patients with this disease. In some tumor types, neoantigen burden is predictive of response to ICI, however these findings are inconsistent and clinical studies have relied exclusively on in silico prediction methods to derive neoantigen burden. We used mass spectrometry (MS) to quantify the amounts of neoantigens within MPM tumors and our recent studies were the first to evaluate the relationship between the actual presence of tumor neoantigens within tumors and responses to immunotherapy. In Aim 1, we will test our hypothesis that response and resistance to ICI can be predicted by a novel single cell immunoproteomic score that can be translated to clinical tissue sections, using prospectively collected pre-treatment tumors from MPM patients treated with ICI. In Aim 2, we will test our hypothesis that response to ICI is more accurately predicted by neoantigen abundance than computationally- derived estimates of neoantigen burden, and is dependent on concordant expression of neoantigens and the MHC proteins specific for those neoantigens. In Aim 3, we will test our hypothesis that that a balance between the repertoire of HLA-presented peptides of MPM, its immunopeptidome, and its TiME regulate response and resistance to ICI. Our results will define core elements of the immunoproteomic structure of MPM that may improve treatment and potentially redirect efforts in the expanding field of immuno-oncology.

NSF Awards · FY 2026 · 2026-01

This project develops an innovative radio frequency (RF) signal propagation modeling framework to advance next-generation wireless technologies, including Wi-Fi 7 and sixth-generation cellular networks. These technologies enable critical applications in smart cities, precision agriculture, and smart healthcare by supporting efficient wireless communication and sensing tasks such as human activity recognition and environmental monitoring. A key challenge in data-driven wireless systems is the labor-intensive process of collecting large-scale RF signal datasets for training deep learning models. This project addresses that challenge by generating high-fidelity synthetic RF datasets using advanced propagation modeling. These synthetic datasets support improved network planning, resource allocation, and sensing accuracy, ultimately leading to more efficient and scalable wireless infrastructures. The outcomes of this research contribute to societal benefits such as economic development, cost-effective network deployment, and enhanced connectivity in dynamic and infrastructure-limited environments. The project also integrates educational activities at the University of California, Merced and the University of California, Los Angeles, incorporating wireless communications and generative artificial intelligence into undergraduate and graduate curricula. In addition, the research team trains graduate students and postdoctoral scholars to support workforce development in RF modeling and next-generation wireless systems. The research investigates the use of Neural Radiance Fields for RF signal propagation modeling, with the goal of synthesizing received signals at arbitrary transmitter and receiver positions in complex three-dimensional environments. The scientific problem centers on overcoming key limitations of existing models, including high data requirements, computational inefficiencies, and poor adaptability to dynamic scenes and spatial variations. To address these issues, the research team develops a scalable approach that combines Gaussian-distribution-based representations, Graph Neural Network-guided scene modeling, and accelerated neural ray tracing to reduce data needs, training duration, and inference latency. Temporal adaptability is introduced through the use of deformation fields that capture dynamic environmental changes, while spatial generalization allows for applications across varying receiver positions and new environments without extensive retraining. The approach integrates techniques such as multi-head deformation decoders, neural-driven ray tracing, and contextual scene embeddings. The resulting models are evaluated using both simulation and experimental testbeds at UC Merced and UCLA. Evaluation metrics include fidelity of signal reconstruction, computational efficiency, and task performance in key applications such as device localization, activity recognition, network design, and resource allocation for diverse wireless technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Surveys of distant galaxies have challenged our understanding of the early phases of star and galaxy formation. These galaxies are brighter and more abundant than previously expected. Detailed studies have found further surprises: growing black holes, runaway star formation, quenched star formation, and even unusual stellar populations. A researcher at the University of California, Los Angeles, will develop computational tools and models that can help explain these surprises in a rigorous way. As part of the project, research tutorials for local high school and undergraduate students will be developed alongside outreach tools for small planetariums. The materials will focus on cutting-edge investigations of early galaxies and the Cosmic Dawn. The goal of project is to improve the physical modeling of galaxies in the early universe in several ways. First, an easily parameterized analytic model of the “burstiness” seen at early times will be developed. It will include an exploration of how the burstiness affects the chemical evolution of these galaxies. The framework will incorporate accreting black holes to constrain the processes governing the origin and growth of the early galaxies. In parallel, a new inference pipeline that can robustly compare observations to theoretical models will be built to stringently test the models. This is an important contribution, as most existing software is calibrated to galaxies at later times and cannot be directly compared to theoretical models. Using a Bayesian hierarchical approach, the novel inference framework will compare the data on an object-by-object basis. The resulting framework will directly compare a fast, flexible set of galaxy models to the data to constrain the key parameters of galaxy formation. The new inference pipeline will be released publicly to maximize the science return of observations and plan to for future surveys. 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 2026 · 2026-01

Given the potential motivational benefits of digital learning games, games might provide a pathway for reducing students' math anxiety and increasing their self-efficacy and interest in math. This project will explore whether and how digital learning games can lead to less math anxiety and better learning in students. It will study learning with two existing digital learning games: Decimal Point, which teaches foundational math concepts (decimal numbers and operations) to 5th and 6th grade students; and Angle Jungle, which targets a similar age range (4th and 5th graders) and has a similar thematic design (i.e., a game map, cartoon characters), but with different game mechanics, content (angles), and instructional approach. The study will explore how and why Decimal Point has, over the course of several experiments spanning multiple years, consistently produced a learning advantage for players. In doing so, investigators will identify principles regarding the relationship between student learning and game features that can be shared with game developers and used in other games, starting with Angle Jungle. The study will investigate two pathways hypothesized to lead to learning differences among students: first, that the playful features of the games reduce the saliency of the math content, making it less likely to prompt math anxiety; and second, that the games' thematic details are more appealing and engaging to some learners based on their interests and videogame preferences. In Year 1, educational data mining will be used to infer students' cognitive and affective processes while playing Decimal Point and compare data to the distinct processes predicted by these two pathways. In Year 2, investigators will assess whether the hypothesized pathways and learner differences replicate in the context of Angle Jungle. In Year 3, hypotheses will be further tested by manipulating Decimal Point's emphasis on math content in one version of the game and enjoyment and playful features in another. The project will compare learning outcomes between the two versions to more deeply explore the competing hypotheses. The ultimate aim of this work is to provide insights into how different students learn from digital games, providing principles and guidance for other researchers and game designers in developing and revising digital learning games. Thus, the project has the potential to transfer Decimal Point's success with learning outcomes to other digital learning games and advance knowledge on the game features that best support students' learning outcomes. Furthermore, findings will allow investigators to revise both games and make them available to thousands of late elementary and middle school students across the country. Even during this project, approximately 1,950 students--including many from districts with low math proficiency--will benefit from learning with Decimal Point and Angle Jungle. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. 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 2026 · 2026-01

This collaborative research project aims to synergize advancements in artificial intelligence (AI) and mathematics to enhance computational methods for mathematical reasoning and expedite mathematical discovery. The project brings together a team of experts from the mathematical sciences, computer science, and AI, leveraging their complementary skills to tackle complex problems in these intersecting fields. The research will focus on developing AI models that can reason constructively about complex mathematical problems, improving formal proof systems, and creating new AI tools that integrate mathematical intuition and creativity. Additionally, the project seeks to advance AI with mathematical foundations, aiming for more interpretable, controllable, and trustworthy AI models. By addressing both the advancement of mathematical research through AI and the enhancement of AI with mathematical insights, the project aims to create significant breakthroughs in both areas, ultimately contributing to broader societal impacts and scientific knowledge. More specifically, this project investigates how to endow AI systems with the ability to reason constructively and intuitively about complex mathematical problems, using techniques from reinforcement learning, generative modeling, and formal proof verification. Central to the research is the modeling of theorem proving as a sequential decision-making process, where formal proofs are framed as trajectories through combinatorially structured state and action spaces. The team will develop scalable task embeddings to quantify the complexity and diversity of reasoning tasks, enabling curriculum learning strategies and improved training data generation. Ideas from intrinsic motivation such as novelty and surprise will guide proof-space exploration in settings where reward signals are sparse or delayed. The project also aims to construct interpretable and elegant proofs by identifying efficient trajectories through the reasoning space, aligned with human-interpretable landmarks, and to develop alignment metrics for selecting models suited to specific problem types. In parallel, the team will investigate the mathematical foundations of neural architectures, analyzing the representational power and optimization of transformer-based models in formal reasoning contexts. Generative models will be applied to construct counterexamples and structured mathematical objects, providing tools for discovery in mathematical domains such as knot theory, group theory, and algebraic geometry. Through these integrated efforts, the project seeks to advance both the development of mathematically grounded AI systems and the use of AI as a tool for mathematical 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.

NIH Research Projects · FY 2026 · 2025-12

PROJECT SUMMARY Coronary artery disease (CAD) remains the leading cause of death in the Western world despite significant advances in early detection and extensive use of lipid-lowering and anti-hypertensive drugs. To date, no single drug has been developed to target the primary disease process in the vessel wall. A complete understanding of the disease susceptibility is urgently needed to develop additional therapies. Common forms of atherosclerosis involve environmental factors, hundreds of genetic variations, and their interactions, each of which exert a relatively small effect on disease susceptibility. The most recent genome-wide association study (GWAS) in nearly six hundred fifty thousand individuals identified 175 independent variants associated with increased risk for CAD. However, most of the underlying genes and the related mechanisms of how these variants contribute to the disease process remain unknown. This proposal outlines an integrative genetics study in a unique resource of human aortic smooth muscle cells (SMCs) isolated from 151 genotyped multi- ethnic heart transplant donors to discover the CAD-associated variants that perturb SMC gene expression and their downstream functional consequences. In recent studies, we measured gene expression in quiescent and proliferative culture conditions representing the transdifferentiation of SMCs from a healthy to an atherogenic phenotype. We identified 84 genes whose expression was associated with CAD variants in GWAS loci. However, the causal genetic variants in these loci remain to be elucidated. Therefore, as part of the proposed studies, we will first perform massively parallel reporter assays to identify the variants that modulate gene expression in SMCs. We will also take advantage of the natural variation in gene expression to construct co- expression and Bayesian networks to understand how the predicted candidate causal genes function in SMCs. We will refine these networks by mapping regulatory elements to nascent RNA transcripts in response to pro- inflammatory cytokines. We will then validate our predictions in gain and loss-of-function experiments in cultured SMCs. We will also validate our predictions in well-phenotyped coronary artery specimens from cases of unexpected sudden death by performing immunohistochemical analysis of proteins encoded by genes that are predicted to play a key role in atherosclerosis-relevant SMC phenotypes. The overall goal of the proposed studies is to integrate systems genetics and computational biology leading to mechanistic predictions of the gene networks that are perturbed by CAD. Besides understanding CAD loci, these integrative genetics studies will provide a useful window into the flow of biological information from genetic variants to SMC gene expression and atherosclerosis-relevant phenotypes.

NSF Awards · FY 2025 · 2025-12

Coral reefs nurture fisheries, protect coastlines, and support rich ecological communities. Reef-building corals depend on microbes living in their tissues to keep them healthy and thriving. This community of microbes – the coral's microbiome – includes algae that provide food and bacteria that promote coral health. Changes in reef conditions can affect the makeup of this microbiome. In coastal regions near coral reefs, environmental stresses have intensified, especially over the past several decades. These changes have impacted coral health. Some corals can live for decades or centuries and have survived these changes. The chemistry of a coral's skeleton reflects the conditions under which it grew. Researchers will measure the chemistry of the coral skeleton from its earliest growth to today. These data will provide a history of warm and cool extremes, periods of fast and slow growth, and changes in seawater salinity, nutrients, pollution, and clarity. Coral skeletons also preserve the DNA of the coral and its associated microbiome. Researchers will use the DNA preserved in the skeleton to describe the composition of the entire community. By pairing DNA analysis with histories of reef conditions, this research represents a major breakthrough to understand how corals and their microbes are surviving in a changing ocean. This study will reconstruct the recent environmental and ecological history of massive corals in the Caribbean (Siderastrea siderea) and the Great Barrier Reef (Porites lobata). Using paleoenvironmental and paleometagenomic reconstructions, this research will elucidate how the coral holobiont - the coral and its associated microorganisms - responds to specific changes in the reef environment. The use of corals from different reef locations will highlight how different species and different ecosystems may show distinctive types of responses to environmental stressors. Historical reconstructions that compare holobiont communities from the pre-industrial era (1600-1850’s) to present day will be used to assess and identify microbial taxa that are robust to environmental stressors, as well as gene functions that appear to be adaptive over time to specific environmental conditions. This will be the first research to document how the dynamics of coral holobionts respond to disturbance events at fine temporal scales, applying new ancient DNA techniques to long-lived coral skeletons over decades to centuries. Broader Impacts of this research will facilitate the creation of a Global Coral ancient DNA Paleobiology Network that will include several teams already in possession of coral cores from different oceans, thus expanding the reach of our efforts to planetary scales. 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 · 2025-12

The convergence of sensing and communication technologies is becoming increasingly critical in emerging multi-agent systems, such as robot swarms, autonomous vehicles and virtual reality. These systems require both precise spatial awareness for coordination and real-time data exchange for collaborative decision making. However, existing solutions face fundamental limitations. Optical tracking systems are expensive and sensitive to occlusion, while Radio Frequency-based systems struggle to achieve comparable accuracy. Moreover, existing wireless solutions cannot simultaneously deliver the high-precision spatial tracking and high-throughput data exchange needed for real-time coordination in dynamic environments. A unified solution that enables both high-precision tracking and efficient communication remains an open challenge. This project tackles the above problem. Developing such new wireless technology is strategically important for setting the US as the leader in 6G and its applications. The proposed research is interdisciplinary in nature and will have a significant impact on different aspects of the society such as education, smart environment, health, robotics, warehouse automation and emergency response. The goal of this project is to enable precise spatial awareness and real-time sensor data communication using millimeter-wave (mmWave) backscatter technology. To achieve this, it first designs and fabricates novel backscatter tags that overcomes environmental interference and achieves submillimeter position accuracy and sub-degree orientation tracking. Then, to enable communication, it incorporates ultra-low power designs that directly encode analog sensor data into backscatter signals and develops an add-on receiver module for radars that enables data streaming for digital sensors. Finally, the system will be integrated into multi-robot systems performing collaborative perception tasks like 3D object detection and mapping. By combining hardware innovations, advanced algorithms, and system integration, this project provides a robust and energy efficient platform that enables new possibilities in networked multi-agent applications where both precise spatial coordination and real-time data sharing are essential. 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 2026 · 2025-12

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of mortality worldwide and is associated with symptoms of dyspnea, cough, and reduced functional capacity. While numerous scientific advances have been made to improve the care of patients with COPD, considerable uncertainty remains about optimal management. For some questions, clinical trial data have been conflicting; for others, clinical trials have not been feasible. Uncertainty in the management of COPD has been further compounded by questions of generalizability in randomized controlled trials. Studies have demonstrated that the majority of patients with COPD would not qualify for such trials because of their strict inclusion criteria based on characteristics such as age, comorbidities, smoking status, and spirometry. Conflicting or absent clinical trial data and questions of generalizability prompt the need for “real-world” studies of patients treated for COPD in routine clinical practice. Given poor adherence to inhaler therapy outside of clinical trials, studies are also needed to understand why patients discontinue therapy. Improving the care of patients with COPD requires identifying which therapies are most likely to be safe and effective in routine clinical practice and developing interventions to target those least likely to be adherent. The ultimate goal of the proposed research is to supplement existing data from randomized controlled trials with pharmacoepidemiologic studies to refine treatment strategies in COPD. The proposed research will accomplish this goal by using large, longitudinal healthcare databases to pursue three specific aims: (1) To validate claims-based definitions of COPD exacerbations; (2) To compare the effectiveness and safety of therapies in the management of COPD, focusing on four areas of ongoing clinical uncertainty; and (3) To develop a clinical prediction rule of inhaler adherence that incorporates key variables across several domains, from out-of-pocket costs and insurance benefit design to therapy-related features (e.g., frequency of dosing) and COPD disease severity. By addressing treatment effectiveness, safety, and adherence among patients treated in routine clinical practice, the proposed research will glean novel insights into the management of COPD, particularly for patients who are underrepresented in clinical trials, including older adults, racial and ethnic minorities, women, and those with complex co-morbidities. Dr. Feldman has a unique background as a practicing pulmonologist with public health experience. This K08 proposes an education plan that will help him build new skills in pharmacoepidemiology. He will receive mentorship from Dr. Sebastian Schneeweiss, a pioneer in pharmacoepidemiology, and Dr. Aaron Kesselheim, a leading authority on pharmaceutical policy and use, and will rely on a team of scientific advisors with expertise in machine learning (Dr. Joshua Lin), data management (Dr. Shirley Wang), biostatistics (Dr. Robert Glynn), geriatric prescribing (Dr. Jerry Avorn), and COPD epidemiology (Dr. Edwin Silverman). This award will provide Dr. Feldman with the tools needed to become an independent investigator.

NSF Awards · FY 2025 · 2025-12

Slime molds have inspired and enabled a wide range of biotechnology and bioengineered applications. They are unusual organisms since as a single-celled organism they multinucleate, are capable of movement, memory-like behavior, decision-making, and efficient network formation. Biological highways built by the slime mold Physarum polycephalum experience similar goals and tradeoffs to human-built networks, including needing to efficiently allocate the matter the highways are built from, handling fluctuating levels of traffic, and resisting damage or outages. In building highways and its other behaviors, Physarum shows itself capable of intelligence and memory, while having no central organizing body and only limited ability to transmit information between different parts of the organism. Here, collaborating researchers at UCLA (USA) and TUM (Germany) will work to build experimental and theoretical models of Physarum’s ‘behavioral space’. The central hypothesis of this work is that slime mold highways can be reduced to finitely many modes. These modes are derived from physical constraints upon the long-range flows that can be created within the network as well as feedback between flows on the slime mold highways and the bio-motor driven pulsations of the network that create and shape them. The research team will map out Physarum’s behavioral space, relating the modeled flow modes to the biophysical processes that the slime mold uses to piece together its highways, including tube sprouting and pruning, and to the migration of the organism. Because of its size and the speed of its internal flows, Physarum contends with an extreme version of the physical challenges encountered by all single-celled organisms: how to allocate resources and coordinate behaviors across the entire cell. Work to understand its repertoire of intelligent behaviors will have expansive impacts on understanding of the extents to which cell behaviors emerge from biophysical constraints or from choices. Combined with the peculiar charisma of intelligent slime molds, the project creates a rich trove of broader impact activities. These include a workshop to fertilize discussions of multinucleate cell organization across organisms and ecosystems, and a joint US-German Research Experience for Undergraduates (REU) in which each year, three undergraduates from each country collaborate, make integral contributions to the project, and learn key physics skills. Additionally, the team will design and disseminate standards aligned lesson plans for K-12 students on maze-solving, to foster inquiry and curiosity about algorithms and information-use. This collaborative U.S.-German project is supported by the U.S. National Science Foundation (NSF) and the German Research Foundation (DFG), where NSF funds the U.S. investigator and DFG funds the partners in Germany. 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 · 2025-12

Developing the US engineering doctoral workforce is a significant opportunity to build and improve the US economy and global leadership. The long-term vitality of the US workforce relies on the full range of engineering career pathways being available to all Americans. Note all students who start doctoral programs finish them and retention varies by the various social strata of the students. The problem is not students’ inability to complete the Ph.D. degree requirements, but rather that talented students leave engineering doctoral programs before completing their doctorates. Student attrition results in a loss of human talent to the national endeavor of research and discovery at universities fueling US economic growth. This project aims to examine the organizational climate of engineering doctoral programs and their impact on promoting or hindering the persistence and retention of doctoral students in engineering. The goal of this mixed-methods project is to examine doctoral students’ perceptions of the factors that impact their persistence in degree completion and the differences in experiencing those factors based on intersecting social categories. Drawing on organizational climate research and other theories, the project’s multidisciplinary team aims to use a student-centered approach to shed light on multiple climate factors by engaging with a wide range of students. To achieve a comprehensive picture of departmental climate and persistence, iterative and complementary cycles of project implementation are planned over the project period. The project will develop, refine, and validate a survey instrument, including a climate scale that will be sensitive enough to assess phenomena unique toa range of students. The scale will be grounded in measurement invariance, in that factors will be measured in the same way across different groups to reveal similarities and differences between engineering doctoral student populations. The project is intended to result in a national survey and interviews with a subsample of survey respondents. This project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad, and enduring: STEM learning and STEM learning environments, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest interventions and innovations to address persistent. 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 · 2025-12

The mission of the Institute for Pure and Applied Mathematics (IPAM) is to build new interdisciplinary research communities, to foster the interaction of mathematics with a broad range of science and technology, to promote mathematical innovation, and to engage and transform the world through mathematics. Mathematics is becoming increasingly central to today's science and technology, with applications as broad as material science, cryptography, medical imaging, artificial intelligence, and many others. Future developments, from biotechnology to quantum computers, require further mathematical innovation and application of existing mathematics. IPAM's primary objective is to facilitate collaboration between mathematicians and practitioners from fields such as medicine, engineering, physical sciences, and social sciences, with the aim of promoting technological and social advancements that benefit the national interest. IPAM fulfills its mission through workshops and long programs that connect mathematics and other disciplines or multiple areas of mathematics. These activities bring in thousands of visitors annually from academia, government, and industry, at all career stages. IPAM also has programs that serve specific needs of government agencies, and that inform the public about the excitement of modern mathematics and the important contributions that have come to society through mathematics. Student-focused programs highlight the value of mathematics degree and the many career paths available to mathematics majors. Through these activities, IPAM serves the national interest. IPAM promotes the progress of science by stimulating the mathematical developments that are needed for this progress; advances the national health, prosperity, and welfare through programs that address current scientific and societal challenges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-12

Ice sheets lose ice mass through gravity-driven flow to the ocean where ice breaks into icebergs and melts, contributing to global sea level rise. Water commonly found at the base of ice sheets facilitates this process by lubricating the ice-rock interface. The recent discovery of vast, kilometer-thick groundwater reservoirs beneath the Antarctic Ice Sheet thus raises important questions about the potential impact of groundwater on ice flow. It has been hypothesized that groundwater flow to the ice-sheet bed may accelerate ice flow as the ice sheet shrinks in response to global warming. Evaluating this hypothesis is challenging due to poorly understood interactions between water, ice, and rock, but is crucial for anticipating the response of ice sheets and sea level to climate change. Understanding how groundwater responds to a changing ice sheet also has important implications for the heat, chemical elements, and microorganisms it stores and transports. To assess the impact of groundwater processes on ice dynamics, a new idealized modeling framework will be developed, incorporating several novel hydromechanical couplings between ice sheets, subglacial drainage systems, and groundwater aquifers. This framework will enable testing the hypotheses that (1) aquifers decelerate ice mass loss in the absence of a well-developed subglacial drainage system, but that (2) an efficient, channelized drainage system can reduce and even reverse this decelerating effect, and that (3) the impact of these phenomena is most pronounced for steep ice flowing rapidly over thick sedimentary basins and depends in a complex way on aquifer permeability. Existing geodetic, seismic, and other geophysical datasets at well-studied Thwaites Glacier and Whillans Ice Stream will be used to constrain model parameters and investigate the impact of groundwater processes in contrasting glaciologic settings. This work will help rule out or highlight subglacial groundwater as one of the next major challenges for efforts to predict the future of the Antarctic Ice Sheet and sea-level rise on decadal to millennial timescales. The project will contribute to educating the next generation of scientists by supporting an early-career PI and a graduate student, as well as participation in a field and research educational program in Alaska and the production of chapters for an online, open-source, free interactive textbook. 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.