University Of California Santa Cruz

NSF Awards · FY 2026 · 2026-10

Large language models are increasingly used in healthcare applications such as virtual assistants and decision support tools, offering new opportunities to improve access to care and patient outcomes. However, these systems can introduce new kinds of risks that arise not from the model alone, but from how people interact with it. For example, patients may rely too heavily on automated advice, receive responses shaped by harmful preconceptions or be unintentionally influenced toward unsafe decisions. These risks are especially concerning in sensitive settings such as mental health and addiction recovery, where errors can have serious consequences. This project addresses these challenges by developing new methods to make interactions between people and artificial intelligence systems safer and more trustworthy. The work aims to improve the reliability of healthcare technologies, support safer patient experiences, and contribute to the broader goal of responsible artificial intelligence. Educational activities include developing interdisciplinary coursework and engaging students from diverse backgrounds in research at the intersection of artificial intelligence and health. This project develops a unified, safety-aware learning framework for identifying and mitigating risks in human-large language model interactions in healthcare. The research investigates three integrated thrusts. First, it develops predictive models to detect fundamental and emerging interaction risks, such as overreliance, stereotyping, manipulation, and privacy violations, using supervised and contrastive learning techniques with interpretable outputs. Second, it introduces robust learning methods to mitigate these risks by incorporating user intent, clinical context, and interaction dynamics, including adversarial training and personalized reinforcement learning algorithms. Third, it designs an adaptive, closed-loop method that jointly optimizes risk identification and mitigation through self-supervised and continual learning, enabling generalization to evolving risks over time. The framework is evaluated using realistic digital simulation environments for addiction recovery and mental health support. The expected outcomes include new machine learning methodologies for AI safety, insights into safe deployment of AI in healthcare, and generalizable techniques for trustworthy human-AI interaction in high-stakes domains. 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 · 2026-07

Information about predators is a primary driver in the development of animal signals communicating danger. Given that many species are social and share potential predators, many of these signals are shared amongst species that occur in multi-species groups. But do all species provide equally valuable information to others in their social community or are many species redundant in detecting and alerting others to danger? Does this make certain species in groups more important in providing information than others? This project aims to look at the underlying characteristics of species in different types of muti-species flocks to understand the underlying drivers of how animal communication is propagated and used across different types of groups. Given the ubiquity of different types of bird flocks across terrestrial ecosystems, this project will investigate the role of information in defining species interactions that maintain diverse groups. This project will also evaluate how information use may be an emergent property that underlies the cohesion of social groups. The conservation and management of ecosystems requires the identification of the roles that different species play and by collecting data and modelling the flow of information through groups, this project aims to identify the key role players that are central to these terrestrial ecosystems. Broader impacts include educational advancements, student training, and mentoring of community college students through research experiences. The proposed research addresses a central question about the diversification of vigilance against predators across social systems and the consequences for who uses such information within a community. How does variation in functional traits such as body size and foraging ecology influence the roles of anti-predator vigilance among multi-species groups? How does variation in vigilance and the detection of information translate into widespread use of information across other members of a group? Direct tests of the relative importance of different mechanisms in providing information transfer as a function of variation in species and functional diversity has been little explored theoretically or through comparative experimental approaches. This project will develop a model and then parameterize the model using data from a large-scale experimental evaluation of the relationship between species functional diversity and information transfer across multiple comparative social groups. The product will leverage the ubiquity of multi- species bird flocks (MSFs), found in nearly every terrestrial forested ecosystem , an ideal model system to conduct large-scale tests to understand the relationship between species diversity and information transfer across biological communities under natural conditions in the wild. 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 · 2026-06

This project supports the conference “Representations of Finite Groups and Related Structures,” to be held from August 3 to August 6, 2026, at the University of California, Santa Cruz. The conference will bring together researchers at different career stages, including graduate students, early-career mathematicians, and established experts, to share ideas and foster collaboration. Representation theory plays a central role in modern mathematics and has connections to areas such as number theory, geometry, and mathematical physics. By supporting the participation of early career researchers and individuals with limited access to travel funding, the conference will help build a more expansive and connected research community. The event will also provide valuable professional development opportunities for graduate students and postdoctoral researchers through direct interaction with leading experts. The conference will focus on recent developments in the representation theory of finite groups and related structures, including ordinary and modular representation theory, character theory, and connections with functorial and categorical approaches. Key topics include local-global conjectures, character correspondences, and the structure and behavior of representations of groups of Lie type. The program will consist of invited lectures and contributed talks, along with structured opportunities for discussion and collaboration. The conference will advance understanding of fundamental problems in the field, promote the exchange of new ideas and techniques, and stimulate further research by bringing together researchers working on closely related themes. 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 · 2026-05

Our proposed research focuses on defining the mechanism of action of flagellar accessory proteins and how they enhance bacterial fitness and colonization using the ulcer-causing bacterium Helicobacter pylori. Many bacteria decorate their flagella with additional proteins but it is mostly unknown how they augment flagellar function. We recently determined the identity of several of these accessories in H. pylori and found that they do not—as predicted—enhance motility but instead regulate it and are absolutely required for stomach infection. Surprisingly, these proteins were homologs of type 4 pili proteins called PilMNO but have evolved to work with the flagella. There remains a gap, however in our understanding of how these accessory proteins operate and benefit H. pylori. Continued existence of this gap prevents us from gaining a full understanding of H. pylori’s pathogenic mechanisms and, in the long term, creating new drugs to thwart these processes. Millions of peo- ple in the U.S. and worldwide are infected by H. pylori and suffer from its associated diseases—ulcers and gastric cancer, which impact millions of people annually. H. pylori is here to stay based on recent studies that show H. pylori incidence has stabilized in the developed world. Furthermore, current therapies to cure H. pylori infection fail with unacceptable frequency, e.g., recent estimates in the United States have found that 20-25% of infected individuals are not cured by the current therapeutic regime in part due to rising antibiotic resistance. New drug targets are desperately needed. The overall objective of this application is to provide insights into how flagellar accessory proteins confer new abilities on motility, cell responses, and infection. Our central hy- pothesis is that flagellar PilMNO act as a signaling hub that respond to signals that arise at the flagellar fila- ment and control a cellular response that includes aggregate formation and motility cessation. Our hypothesis has been formulated from robust multi-disciplinary preliminary data that includes multiple motility assays, mo- lecular microbiology, genetic screens, biochemistry, animal colonization studies, mathematical modeling, and high resolution microscopy. Our approach has three Aims, which use the same range of approaches. In Aim 1, we define the nature of flagella-based input signals sensed by flagellar PilMNO. In Aim 2 we determine the cell signaling pathway activated by flagellar PilMNO. In Aim 3, we identify how flagellar PilMNO stop flagellar motil- ity. The proposed research is innovative in its multidisciplinary nature, that it will create new knowledge about the functions of flagellar accessory proteins in general and specifically in H. pylori, and that it will pioneer new approaches to study flagellar function. The proposed research is significant because it will advance our under- standing of flagellar accessories and how H. pylori has improved its fitness using them.The long-term out- comes generated by this research are likely to provide insights that will enable creation of new drugs against H. pylori and other microbes that use flagella to cause disease.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY (ABSTRACT) Accurate detection of somatic variants in cancer genomes remains significantly more challenging than germline variant detection, with typical error rates an order of magnitude higher. Multiple factors contribute to this disparity, including tumor heterogeneity, aneuploidy, widespread structural variation, and cross-sample contamination. However, additional key factors impeding progress include insufficient benchmark data for training and testing methods, limited adoption of long-read sequencing technologies, and reliance on linear reference genomes that introduce reference bias. We propose to address these challenges through three complementary aims. First, we will expand our existing Cancer Standards Long-read Evaluation (CASTLE) collection to twelve tumor-normal cell line pairs, sequencing each with multiple technologies including Illumina, Oxford Nanopore, and PacBio HiFi. We will generate complete telomere-to-telomere germline genome assemblies for each line and create comprehensive benchmark variant sets validated across technologies. All data will be openly released without access restrictions. Second, we will create new versions of our DeepSomatic variant caller that incorporate pangenome information by: (1) using pangenome-based read mapping to reduce reference bias, (2) incorporating complete haplotype information from the Human Pangenome Reference Consortium into variant inference, and (3) utilizing personalized pangenome references imputed from sequencing data. Third, we will extend our Severus structural variant caller to work with both complete germline assemblies and pangenome references, exploring multiple approaches including direct mapping to diploid assemblies, mapping to merged diploid pangenome graphs, and using personalized pangenome references with imputed haplotypes. The successful completion of these aims will provide essential benchmark data enabling further method development, improved methods for detecting both small variants and structural variants in cancer genomes, and standardized variant call sets for major cancer genomics projects. Our team brings together leading expertise in pangenomics, machine learning, and cancer genomics, positioning us to successfully execute this ambitious program.

NIH Research Projects · FY 2026 · 2026-04

To successfully colonize a host, bacterial pathogens must precisely regulate virulence gene expression in response to the host 9ssue microenvironment. Key among these cues are iron availability and oxida9ve stress, which fluctuate across 9ssues and vary between individuals. Chronic condi9ons such as anemia or iron overload disrupt iron homeostasis and oxida9ve stress, altering the host terrain sensed by bacterial pathogens. Notably, iron overload increases suscep9bility to severe infec9on with Yersinia and several other pathogens. Yersinia, Salmonella enterica, and Vibrio vulnificus all use the iron-sulfur (Fe-S) cluster coordina9ng transcrip9on factor IscR to regulate virulence gene expression in response to cellu- lar Fe-S cluster demand, which is shaped by iron availability and oxida9ve stress. Although previous studies have shown that iron and oxida9ve stress affect IscR DNA-binding specificity, how IscR integrates these signals to regulate bacterial virulence and promote infec9on remains poorly understood. Preliminary studies indicate that IscR is essen9al for bacte- ria to express horizontally-acquired virulence genes that are silenced by the global repressor of foreign DNA, H-NS. More- over, our preliminary data suggest that IscR enables bacteria to overcome boPlenecks during 9ssue coloniza9on. We hy- pothesize that IscR strategically overrides H-NS–mediated silencing of key virulence factors in response to spaCotemporal fluctuaCons in host iron and oxidaCve stress during criCcal stages of Cssue colonizaCon, shaping the course of infecCon. To test this hypothesis, we will u9lize Yersinia pseudotuberculosis as an ideal model pathogen to carry out two independ- ent, complementary aims. In Aim 1, we will determine how IscR orchestrates regula9on of the Yersinia type III secre9on system (T3SS) virulence factor in response to cellular Fe-S cluster demand. To do this, we will determine how purified apo-IscR, [2Fe-2S]-IscR, H-NS, the H-NS binding partner YmoA, and RNA polymerase interact with the promoter of the Yersinia T3SS master regulator LcrF, as well as how IscR and YmoA/H-NS affect RNAP ac9vity. These experiments will guide the design of Yersinia mutants that will be used to test our in vitro findings in bacterial culture. In Aim 2, we will determine how bacterial sensing of Fe-S cluster demand controls the outcome of infec9on in heathy hosts as well as those suffering from anemia or hereditary hemochromatosis, a common iron overload disorder. To do this, we will assess infec9on kine9cs by monitoring luminescent bacteria in living mice over 9me, determine the spa9al distribu9on of bac- terial T3SS expression within 9ssues using fluorescence microscopy, assess Yersinia iron bioavailability and exposure to oxida9ve stress in infected 9ssues, and quan9fy coloniza9on boPlenecks and pathogen dissemina9on paPerns through lineage tracing of barcoded bacterial popula9ons. The conceptual framework for IscR virulence factor regula9on estab- lished in Aim 1 will inform interpreta9on of bacterial infec9on dynamics in Aim 2. Together, this work will provide mecha- nis9c understanding of how bacteria sense the host 9ssue microenvironment to express key virulence programs. In addi- 9on, this study will shed light on how infec9on dynamics are influenced by underlying host condi9ons.

NIH Research Projects · FY 2026 · 2026-04

The purpose of this project is to understand how polyadenylation of RNA transcripts contributes to antimicrobial resistance and bacterial pathogenesis. Polyadenylation in bacteria represents a critically understudied process that accelerates decay of target transcripts. The major bacterial polyadenylase PAP I is widely conserved among b- and g- proteobacteria but has never been studied in mammalian pathogens. In non-pathogenic E. coli, PAP I promotes plasmid replication and plasmid-encoded antimicrobial resistance. However, the role of PAP I in maintaining clinically-significant plasmids in bacterial pathogens is unknown. Our preliminary data suggest that PAP I promotes the maintenance of both native virulence plasmids and engineered antimicrobial resistance plasmids in the human pathogens Yersinia pseudotuberculosis and Shigella flexneri. The Yersinia and Shigella native virulence plasmids each encode a type III secretion system (T3SS) used to subvert host defenses. Yersinia and Shigella require PAP I for sufficient T3SS expression as well as for plasmid-encoded antimicrobial resistance. This underscores PAP I as a promising target for antimicrobial drug development. However, the polyadenylation landscape outside of lab-strain E. coli is completely unexplored, despite preliminary data suggesting species-specific roles for PAP I in resistance to cellular stress. We hypothesize that PAP I stabilizes diverse plasmids in pathogenic Enterobacteriaceae by polyadenylating transcripts that regulate plasmid replication and stability, promoting plasmid-mediated virulence and antimicrobial resistance. To address these gaps in knowledge and to test this hypothesis, we will carry out the following three aims. In Aim 1, we will assess the role of PAP I in antimicrobial resistance and cellular stress resistance in Klebsiella pneumoniae and Salmonella enterica, clinically relevant pathogens often associated with antimicrobial resistance plasmids in humans. We will extend our study to include a collection of antibiotic resistance plasmids isolated from human blood samples. In Aim 2, we will identify transcripts polyadenylated by PAP I in Y. pseudotuberculosis, K. pneumoniae, and S. enterica as well as those encoded by clinically-isolated antimicrobial resistance plasmids, using parallel transcriptomic approaches. We will then validate polyadenylation of prioritized PAP I targets predicted to be involved in plasmid maintenance or stress resistance. In Aim 3, we will determine how PAP I inactivation impacts bacterial pathogenicity when it depends on plasmid replication, using Y. pseudotuberculosis mouse infection as a model. This exploratory investigation sets the stage for comprehensive mechanistic studies of polyadenylation in pathogenic bacteria and lays a foundation for a drug discovery initiative targeting PAP I.

NSF Awards · FY 2026 · 2026-04

Large machine learning models are advancing healthcare, scientific discovery, economic innovation, and national security. As these models increase in scale and capability, they require unprecedented memory capacity, creating a critical bottleneck for future Artificial Intelligence (AI) infrastructure. Emerging technologies such as Compute Express Link (CXL) enable large-scale memory resources to be shared across multiple servers, expanding capacity beyond the limits of a single server. However, sharing memory among multiple users introduces significant challenges to data privacy and system security. Without effective safeguards, cloud providers and data centers may be unable to safely deploy next-generation AI infrastructures based on CXL-enabled shared memory. This project addresses this challenge by developing secure and scalable abstractions and architectural designs for AI infrastructure using CXL-enabled shared memory, enabling continued technological advancement while strengthening economic competitiveness and national security. Research findings and simulation artifacts will be disseminated through publications, professional meetings, and a public website, and will be integrated into undergraduate and graduate curricula as well as K–12 outreach activities to advance education and workforce development in emerging computing technologies. This project establishes a secure and efficient AI infrastructure based on CXL-enabled disaggregated shared memory by addressing two research questions: how to secure disaggregated shared memory in multi-host and multi-tenant environments, and how to reduce security overhead to fully realize system performance. The project develops a new enclave abstraction tailored to disaggregated AI infrastructures that defines protected memory regions spanning multiple hosts and enables secure multi-tenant data allocation and management with strong confidentiality and integrity guarantees. The research investigates challenges unique to disaggregated settings, including cross-host isolation, secure resource coordination, and protection mechanisms against both software and hardware attacks, extending enclave-based protection beyond traditional single-server deployments. To reduce security overhead, the project introduces performance–security co-design techniques that incorporate workload data access characteristics, particularly those of machine learning models, into memory protection strategies. These techniques include workload-aware page prefetching and secure page migration mechanisms that improve efficiency while preserving strong security guarantees. The project conducts rigorous architectural and system-level evaluation of the proposed hardware and software designs using large-scale machine learning and memory-intensive workloads to assess scalability, security strength, and performance trade-offs. The resulting framework provides broadly applicable foundations for secure, scalable, and high-performance AI infrastructure built on CXL-enabled disaggregated shared memory. 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 Astroviruses are a major cause of pediatric diarrhea worldwide. Despite causing one of the most common early childhood infections, astroviruses are one of the least studied enteric RNA viruses. We previously discovered that the virus infects small intestinal goblet cells, specialized epithelial cells that secrete mucus. Few studies have investigated viral infection in goblet cells due to the lack of cell-specific models. Because the mechanisms by which viruses replicate inside of goblet cells are completely unknown, my lab is interested in addressing 1) how do astroviruses enter cells with highly dynamic apical membranes? and 2) what role does mucus secretion play in viral egress? We have established new in vitro models and tools to address these questions and have built a strong and collaborative investigative team with complementary expertise that will ensure the success of these projects. To evaluate receptor-mediated and fluid-phase endocytosis entry pathways into goblet cells, we will use a combination of CRISPR-Cas9 engineering, biochemical analysis, and high-resolution microscopy. We will use a similar suite of techniques as well as cryo-electron microscopy to define the egress pathway of astrovirus from goblet cells via mucus secretion. In addition to murine and human astroviruses, other respiratory and enteric viruses have also been shown to target goblet cells for infection. Thus, our work aims to initially provide foundational knowledge on the basic biology of astroviruses before shedding light on key host pathways in goblet cells that are co-opted by viruses from other families, including influenza and SARS-CoV2. Completion of these studies will provide the first major insights into the virus-host interactions at the apical membrane surface of intestinal goblet cells, which will pave the way for the future development of targeted drug treatments for the numerous viruses that target this unique cell population.

NSF Awards · FY 2026 · 2026-01

Rock deformation is a sub-discipline of Earth science that employs experimental techniques from geology and engineering to measure the strength of rocks. Observations and data from rock deformation are essential to a wide range of research in geoengineering, geologic hazards, geophysics, and planetary geology. However, most institutions do not have active research programs in rock deformation, due to the scale, cost, and technical needs of an experimental rock deformation lab. The Research Opportunities in Rock Deformation (RORD) REU site provides training in experimental rock deformation and generates a robust pipeline of students and industry professionals from all backgrounds. The RORD REU site provides research and mentorship opportunities for undergraduate students in the field of experimental rock deformation. The long-term objective is to expand the pipeline of students pursuing research or industry careers in rock deformation or related fields. Student participants receive training in research methods and professional development topics that provide a stable foundation for graduate school or related career paths. A large team of PIs and senior participants, composed of academic researchers in rock deformation, ensures that students who participate in the program have a deep professional network to support their future endeavors. Students are drawn from the full spectrum of higher education institutions. Strong emphases are placed on recruiting students from smaller colleges and universities that do not have research programs in rock deformation. The REU site includes three integrated sessions: a field session to introduce students to the geological study of deformed rocks, a laboratory session where students conduct experiments on specimens collected during the field session, and a conference session where students have the opportunity to present the results of their research projects. The REU site uses an innovated distributed model, leveraging the combined lab capacity of the PIs and other senior participants to support 10 students per year. 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

An offspring’s traits, such as appearance or behavior, are shaped by both its genes and its environment. Scientists now know that parents' experiences, well before offspring exist, also influence offspring traits. This transgenerational parental impact allows parents to help offspring prepare for survival in risky or stressful environments, even if the parents and offspring never physically meet, by altering how genes are expressed (turned on or off) in their offspring. Most research has focused on how mothers pass along information or cues about their environment to their young, but in reality, both mothers’ and fathers’ experiences are important. For example, if both parents experienced similar environments, their combined cues to offspring might make transgenerational plasticity more beneficial compared to the mother’s cue alone. On the other hand, if mothers and fathers experience different environments, the information they provide their offspring might be contradictory and not beneficial. This research uses both theoretical models and laboratory experiments to ask how offspring respond to maternal and paternal information that differs and whether parents develop behaviors to avoid or manage these mismatches, including choosing mates with similar experiences or changing how they care for their young. By exploring how both parents’ experiences collectively affect their offspring, this work will help us predict when offspring respond to parental cues and when they ignore them. The project will also train undergraduate and graduate students by offering long-term internships that provide students with independent research opportunities, coding workshops to train students in mathematical modeling, and seminars to prepare students for careers in science after they graduate. Biologists are particularly interested in understanding why plasticity occurs, when it is adaptive, and how it influences biological patterns. While transgenerational plasticity (TGP) can benefit offspring beyond what is possible for within-generational plasticity alone, mismatches in parental experiences (e.g., maternal cues of low predation and paternal cues of high predation) can result in traits that are maladaptive for the offspring. We hypothesize that parents can gain and respond to environmental information from mates in ways that may rescue offspring from the detrimental effects of mismatching. We will use mathematical models to understand whether TGP is more likely to arise when maternal and paternal cues match and when differential allocation of care in response to mismatching cues is possible. We will then use experiments with threespined sticklebacks (Gasterosteus aculeatus) to evaluate whether females use mate choice to reduce the frequency of mismatching maternal and paternal cues. Finally, we will empirically test whether males differentially allocate paternal care in response to maternal experience, if this differential allocation reduces the fitness costs of mismatching maternal and paternal experiences, and if it alters the ways in which TGP persists across generations. We predict that mate choice and differential allocation in response to parental cues may allow for the evolution of TGP in environments that would otherwise not select for TGP. This could explain the ubiquity of transgenerational plasticity across taxonomic groups, despite existing theory predicting it should evolve only under limited conditions. 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 United States has high population densities and strong economic interests in coastal cities such as New York City, Miami, and those surrounding the Gulf Coast. However, flooding is becoming an increasingly pressing issue for coastal communities and infrastructure nationwide. The melting of the polar ice caps contributes to rising sea levels and enhances the rate and frequency of coastal flooding. This project will use deep-sea sediment cores to investigate how changes in Earth’s orbit and shifts in climate influenced iceberg discharge events and ocean circulation in the past. The results will provide key insights into the future of the nation’s coastlines in relation to flooding and rising sea levels. The project supports an early career scientist as principal investigator and supports undergraduates who will carry out independent research projects. A marine sediment core drilling expedition was conducted in the North Atlantic in 2023, near Iceland and Greenland, which collected a substantial amount of data on ocean and ice cap variability. Over 100 meters of sediments from that highly successful scientific ocean drilling expedition will be utilized for this project which aims to answer outstanding questions regarding ocean circulation and ice sheet changes during a critical period of mean-state changes in the climate system in the early Pleistocene (2.5 to 1.7 Ma). An important focus is how the high latitudes changed from a largely ice-free northern hemisphere into the most recent ice-dominant regime. The three main goals of this project are to 1) Build a highly resolved timeline of the sediment drill core by matching chemical signals in the cores to well-known global patterns of the ice ages, 2) Characterize the nature of ocean circulation changes throughout this period, and 3) Quantify the timing and amplitude of iceberg discharge events to understand their correlation to ocean circulation changes. To achieve these goals, approximately 800 newly collected drill core samples will be measured using stable isotope mass spectrometry and microscopic techniques. This multi-faceted project will provide a high-resolution and long-term record of North Atlantic Ocean and iceberg variability during a critical time in Earth’s history, bridging a gap in the current understanding of the natural processes that have driven past changes in the Earth’s ice and ocean. 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 · 2025-10

This project aims to generate new evidence-based practices for using virtual reality (VR) to teach complex, invisible physics concepts (for example, wind flow and force). VR is a promising technology to improve conceptual understanding of invisible physics, as VR can provide visualizable, interactive learning opportunities. The practices generated from this Level 2 Engaged Student Learning project are intended to provide instructors and administrators in STEM education with a concrete guide on when, why, and how to use VR to effectively teach invisible physics. Accordingly, this project plans to significantly improve student learning of invisible physics and, more generally, advance understanding of principles of how humans learn in VR environments. This project is positioned to help shape the careers of both undergraduate and graduate students who work on this research project in a multidisciplinary setting. The research training planned for the project’s student personnel is designed to inspire future engineering educators to teach undergraduate courses using VR. It also aims to develop computer science professionals who can enhance VR learning systems and learning scientists who will advance fundamental research on how we learn in virtual environments. The goal of this project involves (a) advancing scientific models of human learning in VR environments, (b) providing reliable, systematic evidence on the effectiveness of VR-aided undergraduate engineering education, and (c) generating new evidence-based best practice of VR applications to complement traditional teaching pedagogy. Cognitive science research predicts that some features of VR, such as enabling learners to explore causal relationships, will enhance learning, while other features might harm learning. There have been few rigorous, systematic investigations into the specific features of VR interactions that improve or impair learning. This project plans to use rigorous experiments to generate new knowledge on the promise and perils of VR in aiding teaching invisible physics concepts compared to high-quality traditional teaching. A workshop for undergraduate instructors is proposed to deliver best practices and provide a concrete, actionable guide on why, when, and how to use VR. Plans also include dissemination to broad audiences through seminars, conferences, workshops, and a project website. A planned external advisory board comprises domain experts for conducting formative and summative evaluation of the project. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the project supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The widespread adoption of the Internet of Things (IoT) forms a critical foundation for enabling applications in healthcare, transportation, and industrial automation. However, the ultra-dense deployment of IoT devices and their need to transmit sensitive data raise significant challenges for efficient and secure communication. Conventional cryptographic methods are often too computationally intensive for resource-constrained IoT devices. This project explores a lightweight and non-cryptographic framework that secures wireless communication by leveraging the randomness of physical wireless channels, grounded in information-theoretic principles of Physical Layer Security (PLS). To address challenges in dense networks where channel correlation among users is high, the project integrates Intelligent Reflecting Surfaces (IRS), passive devices capable of reconfiguring wireless signal paths, into the system design to improve both security and energy efficiency. In addition to its technical contributions, the project supports national workforce development by providing interdisciplinary research training, enhancing cybersecurity education, and engaging students across multiple institutions. This project investigates a learning-based framework to enhance Physical Layer Security (PLS) and energy efficiency in ultra-dense IoT networks using Intelligent Reflecting Surfaces (IRS). By dynamically adjusting the IRS configuration based on relational information among legitimate users and potential eavesdroppers, the system aims to increase channel disparity and mitigate eavesdropping risk. The research introduces three core innovations: (1) an IRS control strategy guided by inter-device relational states to improve secure communication channels; (2) a friendly jamming mechanism enabled by traffic pattern analysis of inactive users to further suppress adversarial interception; and (3) a secure energy efficiency optimization framework that incorporates long-term fairness across users during resource allocation. The project combines algorithm design, theoretical analysis, and real-world wireless experiments to validate system performance. Its outcomes will provide critical insights into designing adaptive, secure, and scalable communication systems for next-generation IoT environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The goal of CS4NorthCal is to scale up an effective, evidence-based model for preparation, PD, and ongoing support of high school CS teachers. CS4NorthCal will prepare and support high school teachers committed to CS education excellence throughout Northern California. This project will increase rigorous and engaging CS course offerings to all high school students. The retention of students in high school CS courses will increase, and these students will gain in-demand skills that allow them to enter the CS workforce. The strategies identified and tested in this project for improving teacher preparation and support will be of broad interest, as school districts throughout the country are facing, or will face, the same problems, as they ramp up their own CS offerings. Over the course of this project, the RPP will serve 200 teachers and support 25,000+ high school students. San Francisco State University, WestEd, and 20+ school districts will collaborate and launch CS4NorthCal to address the critical national need to broaden the availability of high-quality computer science (CS) education to all American students by creating a scalable and effective model for teacher preparation. The project has three main activities: (1) scaling a certification program for CS teacher preparation and professional development to increase the number of authorized high school CS teachers; (2) expanding Professional Learning Communities (PLCs) for newly certified high school CS teachers to provide continuous professional learning on CS curricula and effective pedagogical strategies; and (3) establishing and scaling a high school CS teacher mentorship program to offer ongoing, rigorous support to newly certified teachers to improve their retention and skills. The RPP has jointly developed research questions to investigate how best to design certification courses, PLCs, and teacher mentorship programs to prepare teachers of varying disciplinary and geographic backgrounds to teach high school CS: Research questions include: (1) How can online certification courses be designed to effectively prepare teachers with CS content knowledge and skills? (2) How can certification courses and ongoing support be designed to address the needs and readiness levels of teachers with different disciplinary backgrounds? (3) How do teachers engage in ongoing support (e.g., PLCs, mentoring) focused on the continuing development of their content knowledge and teaching skills, particularly in emerging areas such as artificial intelligence for workforce readiness? By focusing on building teacher capacity in high schools, this project creates new learning opportunities for all students, enabling them to pursue higher education and careers in high-demand STEM and AI-integrated fields. 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 · 2025-10

With the support of the Macromolecular, Supramolecular, and Nanochemistry (MSN) program in the Division of Chemistry, Professor Jin Zhang of University of California-Santa Cruz and Professor Yuan Ping of University of Wisconsin-Madison are using advanced computational and laser techniques to study the behavior of electron spin in two-dimensional (2D) metal halide double perovskites. Electron spin is a fundamental quantum mechanical property useful for applications such as information encoding and storage. Many emerging applications require long spin state lifetime and precise control. However, spin lifetime is usually short, a fraction of a second, and challenging to manipulate and use in devices. The research will create new 2D perovskites with long spin lifetimes and use ultrafast lasers to probe and control spin. Their discoveries could impact technologies from nanoelectronics to quantum information technologies including quantum computing and communication. The project will also provide training opportunities for future scientists, and through their “open lab” with a theme on “Spectroscopy and Sunny Santa Cruz” (SSSC)”, it will introduce research to local high school students and communities to enhance public awareness about science. With the support of the Macromolecular, Supramolecular, and Nanochemistry (MSN) program in the Division of Chemistry, Professor Jin Zhang of University of California-Santa Cruz and Professor Yuan Ping of University of Wisconsin-Madison are using combined advanced computational and experimental efforts to study spin and carrier relaxation in novel 2D lead-free metal halide double perovskites. This is motivated by the novel spin-orbit physics hosted in this class of material, which is highly tunable through crystal symmetry, morphology, and chemical composition in these systems. Meanwhile, electron-phonon and electron-electron scatterings lead to spin relaxation through spin-orbit coupling and lead to finite spin lifetime, which can be determined using ultrafast laser experiments with circularly polarized light. The project will systematically study the fundamental factors, such as structure, composition, surface, and chiral component, which affect the electron spin lifetime by studying the impact of each factor on spin lifetime as well as associated processes such as electron-electron, electron-surface, and electron-phonon scatterings. The materials will be synthesized with rational structural and compositional control, and carefully characterized using a combination of time-resolved photoluminescence, transmission electron microscopy, X-ray diffraction and spectroscopy, Raman and infrared spectroscopy, as well as ultrafast pump-probe methods. Computational studies based on state-of-the-art first-principles open quantum dynamics method are exploring the scattering processes affecting electron spin lifetime to guide and corroborate experimental studies, which is critical for addressing the complex and challenging issues proposed. 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 · 2025-10

Over a century ago, the Research Vessel Albatross collected fishes from the Philippines, now stored at the Smithsonian Institution. The archive provides the potential for rare insights into how fish have evolved in response to fishing, habitat loss, and other challenges. The research will compare historical and modern fish and will focus on blue sprat, a small coastal species important for food. The research findings can help understand adaptation across many species facing similar challenges. The project will also support paid research internships for students with limited access to careers in science. The project will host workshops to build international exchange with the Philippines. Finally, this research can inform fisheries by identifying fishing zones and where seafood was caught. This project will help to understand the architecture and genomic origins of rapid adaptation, in part by testing the hypothesis that local adaptation provides the raw material for rapid evolution through time. Species objectives include to 1) assemble and annotate high-quality genomes to understand genetic architecture in blue sprat (Spratelloides delicatulus); 2) resequence the genomes of ~1000 individuals across at least five sites in historical and modern eras to identify loci targeted by spatially divergent or temporal selection, and 3) measure morphology and growth to test for the functional importance of genomic variation. The project will focus on historical (1907-1909) samples held by the Smithsonian Institution and modern samples collected in collaboration with Silliman University. The ethanol preservation by the R/V Albatross is a unique scientific accident that provides excellent DNA preservation over the last century. 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 · 2025-10

This project will combine large flow-cytometry datasets with novel machine learning models to reveal the geographical distribution of phytoplankton and show how the environment shapes these patterns. Neural network methods for flow-cytometry data analysis will be applied to data from over 100 cruises across the Pacific and Atlantic Oceans. The project will develop computationally efficient mixture of neural network models, a generative model framework for changepoint detection, and spatially dependent convolutional neural networks. These methods will make oceanographic data analysis more automatic and efficient while also allowing for model-based rediscovery of ocean provinces as well as predictive mapping of ocean microbe populations and traits. The proposed methodology will advance AI and statistics, data science, and oceanography while also being useful across a broad range of disciplines that deal with complex high-dimensional dependent data such as environmental science, ecology, agriculture, epidemiology, and econometrics. The methodology will also be useful for various data science industries that handle high-dimensional mixture data or flow cytometry. Public-use software packages will be created. The project will develop computationally efficient neural network models that automatically classify cell level data with environmental covariates. This will streamline the analysis and reveal biological responses to changing environments. Generative neural networks will be used for changepoint detection. Latent variables will identify shifts in phytoplankton communities and help redefine ecological ocean provinces. Finally, convolutional neural networks will be applied to density regression and spatial interpolation of flow cytometry data. This predicts complete cytogram “images” extending data value beyond cruise tracks, to help create global phytoplankton biogeographies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY / ABSTRACT We propose to advance comparative genomics through three interconnected aims. In Aim 1, we will scale genome alignment and analysis capabilities to accommodate the rapidly growing number of available vertebrate genomes, projected to reach ~10,000 species within four years. We propose developing a highly scalable, automated pipeline for species-tree construction from raw genome assemblies, reworking the genome-based HAL format, and introducing a new column-based alignment format (TAF) to facilitate efficient representation and analysis of large-scale alignments. To demonstrate these improvements, we will create and share the deepest ever vertebrate alignments, focusing on predictions of evolutionary selection. In Aim 2, we address the challenge of aligning telomere-to-telomere (T2T) human genome assemblies. We will develop novel repeat-aware alignment algorithms and graph models to enable a more complete alignment, starting with human centromeres and extending to other heterochromatic sequence. We will integrate these methods into our pangenome construction process to facilitate T2T alignment within the human pangenome. Aim 3 focuses on disentangling the genetics of the 1q21.1 region, which is associated with various neurodevelopmental disorders, as an exemplar of a difficult segmental duplication. We will analyze T2T ape and human genomes to create a comprehensive 1q21 pangenome, develop efficient methods for high-resolution genome reconstruction of patient-derived cell lines with 1q21.1 copy number variations, and test the functional consequences of identified genetic alterations using hiPSC-derived cerebral cortex organoids. By combining advanced genomic analysis, efficient sequencing protocols, and functional studies, we aim to significantly advance our understanding of genome evolution, improve our ability to analyze complex genomic regions, and potentially lead to improved diagnostics and therapeutic strategies for neurodevelopmental disorders associated with the 1q21.1 region. Together, these advancements will empower the community to conduct more comprehensive comparative genomic analyses, leading to a deeper understanding of genome evolution across vertebrates and humans and its implications for human health.

NIH Research Projects · FY 2025 · 2025-09

Abstract Hibernation involves complex metabolic and physiological shifts that enable diverse mammalian species to survive prolonged periods of resource scarcity. These species are able to cope with prolonged periods of fasting, rapid weight gain and loss, periods of insulin resistance, and other physiological stresses that are associated with metabolic disease or dysfunction in non-hibernating mammals, including humans. This project investigates the mechanisms underlying metabolic innovation in mammals. To complete its objectives, this research project will use comparative and functional genomic approaches to investigate the mechanisms and evolution of hibernation across the mammalian tree, reveal detailed regulatory mechanisms underlying hibernation gene regulation in the brown bear, which is a particularly unique and valuable hibernation model system, and directly test the translational potential of bear serum factors for the modulation of human adipocyte metabolism. This work will advance our understanding of the mechanisms governing hibernation in mammals and identify specific genes, transcription factors, and signaling pathways with conserved or divergent function in hibernators compared to humans. This research will identify specific genes and regulatory mechanisms that can be targeted in the treatment of human metabolic diseases.

NSF Awards · FY 2025 · 2025-09

With the support of the Macromolecular, Supramolecular, and Nanochemistry Program in the Division of Chemistry, Dr. Alexander Ayzner of the University of California, Santa Cruz will determine the molecular characteristics that govern the structure and electronic properties of liquids that have both viscous and elastic properties, called coacervates, composed of conjugated (semiconducting) polyelectrolytes (CPEs). Such liquids are promising candidates to serve as aqueous photochemical reactors and compartments in artificial photosystems that mimic natural photosynthesis. To develop a foundational understanding of such systems, the influence of ions on the nature of the electronic states and their movement within the system will be interrogated across a series of CPE structures using a combination of thermodynamic and optical techniques. Once the fundamental characteristics that govern such states of soft matter are determined, artificial reaction centers will be incorporated into the CPE coacervates to improve their light harvesting characteristics. The lifetime of the charged carriers and the efficiency of their generation will be elucidated using a combination of optical characterization techniques. This research will develop the next generation of STEM professionals by training graduate and undergraduate students, as well as promising high school students via the state-wide California science summer school program. CPEs exhibit fascinating aqueous phase behavior that spans the viscoelastic continuum from solids to complex fluids and, as shown more recently, coacervates. The relatively large CPE concentration within the coacervate leads to significant excitonic connectivity, and the liquid nature of this macrostate allows for diffusion of small molecules. These characteristics are highly promising for aqueous light-harvesting systems. However, there exists no fundamental physical-chemical understanding of how spatially extended π-stacking interactions and the coupling between electronic and ionic degrees of freedom conspire to determine the stability and properties of intrinsically electronic coacervates – viscoelastic liquid phases that are highly enriched in CPE chains. In this project the strength of ion-π interactions and the ion hydration free energy will be correlated with the nature of emergent electronic states and the exciton diffusion within the coacervate. The influence of extended π-stacking interactions on the stability, structure, and electronic structure of the coacervate phase will be determined by systematically varying the CPE backbone topology. Having characterized the fundamental structure/property relationships that underpin electronic coacervates, the ability of such phases to support the formation of long-lived photoinduced electron/hole pairs will be studied. Doing so will generate new fundamental knowledge regarding the mechanistic aspects of liquid-liquid phase separation of CPEs for the formation of coacervates and lead to the ability to rationally design coacervate droplets that serve as light-harvesting compartments in complex aqueous photosystems. 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 · 2025-09

With the ever-widening use of software in safety-critical applications such as autonomous vehicles, design defects are becoming increasingly catastrophic in their consequences. Formal, mathematical techniques to prove the correctness of software provide a promising approach to ensure the safety of such systems. However, formal verification of complex systems often requires an impractical level of human effort: automated theorem provers (ATPs) typically do not scale to real-world applications, forcing correctness proofs to be written largely by hand in interactive theorem provers (ITPs). A similar challenge has arisen in mathematics, where there is growing use of ITPs to formalize (and sometimes find mistakes in) proofs: the lack of scalable automation puts formalization beyond the reach of most working mathematicians. This project aims to address these challenges by developing new techniques allowing ATPs to scale to complex theorems, as well as tools usable by mathematicians for proof formalization. Enhancing the scalability and usability of ATPs will reduce the barrier to entry for safety-critical system designers and mathematicians to verify their systems and proofs, helping to make these safer and more trustworthy. The project has three primary research thrusts. The first two thrusts tackle several obstacles to using Large Language Models (LLMs) to automate proof construction, turning an ITP into an ATP: data scarcity, sparse rewards, and lack of self-play. Thrust 1 will address the data scarcity problem by generating synthetic theorems and proofs: the project will develop LLM-based techniques to generate human-like theorem statements and proofs, as well as techniques for translating between formal theorems/proofs and informal, more easily-interpretable versions. Thrust 2 will address the self-play and sparse reward problems by exploiting high-level structure in proof search: the project will develop techniques to synthesize lemmas providing easier-to-prove intermediate steps on the way to a desired theorem, as well as techniques to guide proof search using human feedback. Finally, the last thrust seeks to ensure that the project's advancements in ATPs transfer to advances in mathematics, and that the developed tools will be useful for working mathematicians. Towards this end, Thrust 3 will apply the project's tools to study important conjectures in the theory of linear groups. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This award supports the renewal of an REU Site in computational astrophysics at the University of California-Santa Cruz (UCSC). Students will principally be recruited from community colleges, where the main selection criteria will be outstanding academic accomplishments and promise of future achievement. Each year, ten selected interns will work closely with mentors at UCSC and will take part in an intensive eight-week introduction to astrophysical research methods and tools with an emphasis on computational astrophysics and astronomical instrumentation. The overall goal of the program is not to solely train the next generation scientists but to use astrophysical simulations and astronomical instrumentation as an exciting medium for imparting a broad array of technical skills to the participants. The REU Site will increase the retention and graduation rates for students enrolled in two-year colleges through inquiry-based learning of concepts related to astronomy and planetary sciences, using state-of-art astrophysical simulations and astronomical instrumentation as a common pillar. Through a comprehensive program of research instruction, mentoring, workshops, and support programs at UCSC, students will develop additional skills to advance their education and careers. As high-performance computing becomes a routine tool, industry and government will seek graduates with expertise in these fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Spectroscopic analysis based on a wavelength-dependent sample response is an invaluable and ubiquitous tool in diagnostics and fundamental biomedical research. It allows for multiplex analysis, either using continuous spectra (e.g. Raman spectroscopy) or using distinct and discrete labels. These principles are increasingly being implemented in compact lab-on-chip settings, including optofluidic devices in which optical and fluidic functions are integrated on the same chip. However, miniaturization of spectrometers has been a major challenge as conventional dispersion-based instruments scale poorly with size. Only recently, new approaches based on photonic elements and reconstruction algorithms have started to emerge, but they have not been used in conjunction with highly sensitive multiplex bioparticle detection. The goal of this project is to overcome this challenge by introducing and validating a new paradigm for multiplexed spectral bioparticle analysis on a chip. The specific objectives of this application are to incorporate a high-performance spectrometer on an optofluidic chip to enable spectral classification of fluorescence signals from single biological nanoparticles. Our central hypothesis is that this can be accomplished by leveraging emerging photonic design methodologies to build a multi-mode waveguide whose light propagation patterns can be imaged from the top. The addition of a nanostructured metasurface will allow for guiding specific wavelengths to spatially well separated spots for easy observation with a camera. The objectives of this application will be accomplished by the following Specific Aims: (1) Demonstration of detection of individual nanoparticles on integrated MMI waveguide spectrometer that is optimized by end-to-end photonic design; (2) Design and demonstration of a nanopatterned metasurface for spectral particle classification; and (3) Multiplex direct detection of individual exosomes from cerebral organoid cultures. The main innovative contributions of the proposed work are: (i) a novel waveguide-based spectrometer that can be seamlessly integrated on a fluidic chip; (ii) end-to-end inverse photonic design of a nanostructured metasurface for efficient and easy classification of different wavelengths; and (iii) validation of the novel device with a multiplex fluorescence assay of individual exosomes. The proposed work is significant because it will introduce the first high- performance integrated spectrometer for both discrete and continuous spectral analysis in a lab- on-chip format. Thus, this approach is suitable for both fundamental research and numerous applications, such as Raman and fluorescence spectroscopy.

NSF Awards · FY 2025 · 2025-09

This project involves interviewing researchers and research administrators who have worked with embedded ethicists and social scientists in the fields of genomics, neuroscience, and artificial intelligence to understand their perspectives on the value of such integration efforts. The project provides a comprehensive view of how interdisciplinary collaborations in three rapidly advancing research fields function in practice. Projects findings contribute to the development of evidence-based best practices for integrating ethical and social science expertise into scientific research. The project also informs the training of the next generation of researchers in responsible scientific practices. The project employs qualitative research methods to investigate the experiences that researchers have working with embedded ethicists and social scientists. The project team will conduct semi-structured interviews with researchers and research administrators across three domains: genomics, neuroscience, and artificial intelligence. The primary goal is to develop a comprehensive, multiperspectival analysis examining the motivations, experiences, and evolving understandings of responsibility that researchers have. The project will identify epistemological, organizational, and other factors that shape interdisciplinary scientific collaborations. 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.