California Institute Of Technology

NSF Awards · FY 2026 · 2026-07

This research will develop new mathematical and computational tools that can significantly improve the speed, reliability, and realism of scientific simulations across a broad range of scientific and engineering applications. These capabilities are expected to support data-driven computational methods and AI-assisted engineering design and scientific discovery by enabling high-fidelity predictive models that reliably capture wave propagation, transport, and multiscale physical interactions, as well as producing simulation data that is sufficiently accurate and robust to serve as a foundation for training, validating, and benchmarking next-generation AI-based methods. The work is also relevant to wave and quantum phenomena arising in emerging sensing, photonic, and quantum-device technologies. Potential biomedical applications include improved computational approaches for medical imaging, ultrasound, diffusion and transport in biological systems, and related health technologies that depend on accurate wave and transport simulations. In manufacturing and materials engineering, the resulting methods may contribute to the design and optimization of next-generation materials, microelectronic and photonic components, and precision production processes requiring accurate modeling of complex geometries and wave interactions. Broadly, the project will provide foundational capabilities that enable faster innovation, improved predictive technologies, and efficient use of high-performance computing resources in areas of long-term national interest. This project develops new mathematical formulations and fast computational algorithms for accurate solution of partial differential equations and wave phenomena in geometrically complex, high-frequency regimes. The work includes methods for problems with geometric singularities such as edges and corners, approaches for fractional Laplacian equations on bounded domains via weakly singular integral formulations, and fast iterative eigensolvers for integral-equation models arising in electromagnetic, acoustic, quantum, and fractional-diffusion settings, enabling efficient solution of large-scale eigenproblems. The effort also extends Fourier-continuation methodologies to time-dependent and nonlocal systems, including fluid flow, reaction-diffusion dynamics, ultrasound propagation, and classical and relativistic Schrödinger equations. In addition, multidimensional Fourier continuation methods will be generalized to non-smooth domains in two and three spatial dimensions and beyond, addressing a longstanding limitation of existing Fourier extension techniques. Finally, new screened WKB methodologies will be developed for high-frequency wave propagation in heterogeneous media, including configurations with caustics, using Fourier-based decompositions on geometrically adapted screening surfaces. Collectively, these developments aim to provide scalable, highly accurate computational methods for challenging wave, transport, and nonlocal phenomena beyond the reach of many current numerical approaches. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

With support from the Environmental Chemical Sciences Program in the Division of Chemistry Nga Lee Ng and Rodney Weber at Georgia Tech and their students will study the formation of organic aerosols (tiny particles suspended in the air) from biomass burning emissions in the atmosphere. Understanding how these particles form is important for evaluating their impact on climate and human health. During fires, the decomposition of woody tissues in plants at high temperatures releases a significant level of volatile organic compounds (VOCs), including phenolic compounds. The team will investigate how these phenolic compounds react under atmospherically relevant conditions to form organic particles, as well as the ability of these particles to absorb light. The team will also develop and implement an instruction module on particles from biomass-burning smoke to expose middle school students in the greater Atlanta area to new scientific concepts related to wildfires and air pollution. Phenols and methoxyphenols are a significant part of non-methane organic compounds emitted from biomass burning. The oxidation and further chemical processing of these important biomass burning emissions has not been systematically studied under realistic ambient conditions. Through comprehensive laboratory chamber experiments, this project will investigate secondary organic aerosol (SOA) formation from gas-phase oxidation of a set of phenolic compounds under different reaction conditions and their subsequent aqueous-phase processing. The optical properties of aerosols will also be characterized. Results from this study will provide fundamental data to parameterize the gas-phase and aqueous-phase chemistries of phenolic compounds released into the environment and provide enhanced information on the aerosols that they form. These data will, in turn, be useful to scientist developing atmospheric models designed to account for such organic aerosols. 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 will support U.S.-based students, postdoctoral researchers, and early-career faculty in attending a major international scientific meeting focused on how brains generate behavior. The 16th International Congress of Neuroethology (ICN), to be held in Vancouver, Canada in July 2026, will bring together researchers from around the world who study how animals sense, communicate, navigate, learn, and interact with their environments. Scientists at the meeting investigate a wide range of organisms—from insects and birds to mammals and marine animals—to better understand how nervous systems work. Because the conference will be held in North America, it provides an unusual opportunity for U.S. trainees and junior scientists to participate in an international research community. NSF travel support will reduce financial barriers for participants with limited resources. The meeting will provide opportunities for mentoring, networking, professional development, and scientific exchange, helping early-career researchers build collaborations and develop skills that contribute to the future scientific workforce. This project advances NSF’s priorities in Biotechnology and Artificial Intelligence. The ICN is the premier international meeting in neuroethology, an interdisciplinary field focused on understanding the neural mechanisms underlying natural behavior across diverse species. The scientific program will provide a comprehensive view of current advances in the field through plenary and keynote lectures, invited and participant-organized symposia, contributed talks, and poster sessions. Topics span multiple levels of analysis, from molecular and cellular mechanisms to neural circuits, behavior, and evolution, and integrate approaches from neuroscience, biology, physics, engineering, and computational science. A central theme of ICN 2026 is the convergence of traditional neuroethological model systems, such as bats, songbirds, and cephalopods, with established laboratory and genetic systems including rodents and Drosophila. The meeting will also highlight emerging research areas including multimodal sensory processing, decision-making, navigation, and social communication. NSF funds will support competitively awarded travel grants for U.S. participants with demonstrated need, including trainees, junior faculty, and selected speakers, thereby promoting interdisciplinary exchange and collaboration within the international neuroethology community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Roughly half of all stars in the Universe are members of binary or other multiple-star systems. Interactions between the stars in these systems can lead to a wealth of stellar evolutionary outcomes, including black hole and neutron star binaries, and eventually be the source of gravitational waves resulting from black hole and neutron star merger events. Gaia—a space telescope that has built the largest and most precise three-dimensional map of our Galaxy—has provided unprecedented data for studying binary stars. A researcher at the California Institute of Technology (Caltech) has developed software to analyze Gaia data in order to characterize and catalog binary systems in the Galaxy, identify systems containing compact objects (i.e., black holes, neutron stars, white dwarfs), and study the physics of interacting stars. This project will support a graduate student, who will be involved with all aspects of the research, and it includes course development at Caltech and a workshop that will provide hands-on experience analyzing Gaia data for undergraduate and graduate students, as well as the broader astronomical community. The project also includes STEM training for K-12 teachers and high school students through a summer research program. Binary star systems play a critical role in understanding stellar evolution, particularly related to mass transfer and compact object formation. Gaia is transforming binary star science by detecting binary stars with separations that are largely inaccessible to other detection methods, thus opening up a whole new regime of binary systems. The principal investigator has produced an open-source software package (gaiamock) that models Gaia’s binary selection function and will use it to constrain the Galactic population demographics of post-interaction binaries, constrain the mass distribution of black holes and neutron stars, test models for the formation of compact-object binaries, constrain the role of binary envelope stripping in producing RR Lyrae stars, and test whether long secondary periods in evolved giants are a result of binarity. The project will also result in an astrometric catalog that is more complete than the official catalog released by the Gaia collaboration, enabling a broad range of additional projects, such as detecting giant planet and brown dwarf companions to nearby stars, and studying massive stars in multiple-star systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Roots are essential for plant growth and agricultural productivity because they anchor plants and absorb the water and nutrients needed to sustain development. Root growth and development require neighboring tissues to divide, expand, and mature in a coordinated manner; if this coordination fails, because plants are surrounded by interconnected cell walls, organs tear themselves apart as they grow and normal root growth breaks down. More broadly, understanding how distinct tissues communicate during organ growth remains a major unresolved question in developmental biology. This project will determine how plants coordinate growth and differentiation across tissues, using Arabidopsis thaliana roots as a model. Recent work has shown that disrupting hormone signaling in a single cell layer of the root can dramatically reshape the growth of neighboring layers and halt root growth, confirming that developing tissues must communicate with each other to remain coordinated. To examine this communication, broad manipulation of plant hormone pathways in the whole plant often produces undesirable side effects, therefore understanding tissue-specific signaling, proposed here, is crucial for the precise engineering of crops. By uncovering the signals that maintain this coordination, the project will advance fundamental understanding of how multicellular plant organs grow and lay the foundation for precision plant engineering strategies to improve crop productivity, essential for food security. The project will also expand training in plant biology through a new undergraduate research course, a high school outreach project in plant genome engineering, and a training workshop on single-cell and spatial methods in plant biology. The project will test the hypothesis that organ development depends on molecular and mechanical feedback between neighboring tissues, such that perturbing hormone signaling in one cell layer dynamically reprograms both local and adjacent cell behaviors. Roots provide a tractable system for these studies because concentric tissue layers and rigid cell walls allow chemical and mechanical signals to be tracked across layers. The project focuses on brassinosteroid signaling; tissue-specific disruption of a hormone receptor leads to exaggerated expansion in neighboring cells and loss of inter-tissue coordination. Combining tissue-specific perturbations with single-cell, spatial, and live-imaging approaches, the project will identify coordination-responsive cell states, track how signaling outputs propagate across neighboring cell layers, and discover candidate factors that mediate developmental integration between tissues. Quantitative imaging, mechanical assays, and artificial intelligence-enabled mechano-biochemical modeling will link these molecular changes to root cell geometry, growth dynamics, and tissue mechanics. Finally, inducible perturbation systems will be used to define spatial and temporal windows in which coordination is established and maintained, testing when and where signaling is required, whether coordination can be restored after it has been disrupted, and how many cells must be affected to trigger breakdown. These studies will generate a multiscale framework for how molecular and mechanical signals integrate across tissues to build a multicellular organ. Combining AI with single-cell and spatial approaches, the project advances plant biotechnology while producing publicly available datasets, code, computational models, and molecular resources for the larger research community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-05

Project Summary Symbioses span the tree of life and impact human health in myriad ways—both beneficial and harmful. Understanding how these interactions originate during evolution is invaluable to how we approach them biomedically. Yet, knowledge of how symbiotic lifestyles evolve at the molecular and cellular levels is scarce. Our work has harnessed a virtuoso group of organisms to break open basic questions on the origin and evolution of interspecies relationships. Rove beetles (Staphylinidae) are a species-rich clade in which numerous of lineages have evolved from free-living organisms into highly specialized symbionts that obligately depend on ant colonies. The high frequency of transitions from free living to symbiotic creates a unique and powerful system to retrace key evolutionary steps leading to a complex but stereotyped form of symbiosis. The symbiotic transition involves dramatic changes in chemical biosynthesis and behavior that assimilate the beetle into the ant society. Through comparisons between model free-living and symbiotic species, this proposal will uncover how changes in these beetle's genomes, biosynthetic pathways, cell types and nervous systems can transform a free-living organism into an obligate symbiont that is entrenched within its host's biology. Our research falls into four connected areas: 1) the evolution of cellular and biochemical innovations that have enabled rove beetles to interact chemically with ants—defensively in free living species, and though chemical manipulation of ant behavior in symbiotic species; 2) the cellular basis for the symbiotic integration of rove beetles inside host colonies through evolutionary changes in mechanisms of pheromonal biosynthesis, and via chemosensory mechanisms of host finding and recognition; 3) the genetic basis for the irreversibility of symbiotic evolution via epistatic interactions between symbiotic traits that entrench the symbiosis; 4) reconstituting symbiotic evolution via genetically engineering the free-living rove beetle phenotype, to test how critical changes unlock the ant colony niche and facilitate the transition to host dependence. This proposal will be foundational for understanding the evolutionary trajectory of symbiotic lineages, from the conditions that predispose them to form nascent interactions with other species, to the evolutionary path of specialization leading to obligate host dependence.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY Maintaining sodium balance is critical for survival, as sodium is essential for fluid homeostasis, cardiovascular function, and cellular processes. However, excessive salt intake poses a significant public health challenge, contributing to hypertension, cardiovascular disease, and renal dysfunction. Thus, identifying strategies to regulate salt intake is of urgent importance. Salt taste perception is not fixed but is dynamically modulated by internal physiological states. Low concentrations of sodium are typically appetitive, whereas high concentrations are aversive. During sodium deficiency, animals exhibit a robust salt appetite, tolerating concentrations that would otherwise be rejected. This shift in taste preference suggests that the brain centrally modulates salt taste pathways to align behavior with homeostatic needs. Despite progress in identifying peripheral taste mechanisms, how central circuits reshape salt taste perception in response to physiological signals remains poorly understood. Our findings identified two interoceptive neural circuits that modulate salt taste: hindbrain circuits that promote salt appetite and forebrain neurons in the lamina terminalis (LT) that regulate salt tolerance by suppressing aversive responses. Our preliminary data further show show that sodium depletion elevates levels of prostaglandin E2 (PGE2), which activates PTGER3-expressing LT neurons, suggesting an inflammation-like state mediates this central modulation. We hypothesize that PGE2 signaling in the brain reconfigures salt taste pathways to increase salt tolerance and drive salt-seeking behavior under sodium-deficient conditions. To test this hypothesis, we propose three specific aims: (1) characterize prostaglandin signaling in LT neurons using a genetically encoded PGE2 sensor and calcium imaging to measure real-time dynamics in vivo; (2) determine how sodium imbalance and inflammatory signals modulate central and peripheral salt taste pathways through genetic and pharmacological manipulation, as well as state-dependent single-cell transcriptomics in taste ganglia and taste bud cells; and (3) define the downstream circuits where forebrain and hindbrain interoceptive pathways converge to regulate salt ingestion, using transcriptional mapping of integrative brain regions. This project integrates molecular, neural circuit, and behavioral approaches to uncover how physiological need modulates taste perception and ingestion. By elucidating the brain mechanisms that regulate salt taste, our findings may inform therapeutic strategies to reduce dietary salt intake and address widespread health issues associated with sodium overconsumption.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY Antibiotic tolerance hinders the treatment of chronic infections of diverse types, contributing to poor patient outcomes and burdening the healthcare system. Underpinning the failure of many conventional drugs to eradicate opportunistic pathogens in these contexts is that they target machinery needed to support rapid growth, yet cells in chronic infections typically exist as slow- or non-growing multicellular aggregates (biofilms). Cells in the biofilm interior experience anoxia yet maintain a low level of metabolic activity. How these cells conserve energy and how they spend energy economically is poorly understood, in part because we have lacked a good experimental system through which to quantify and study maintenance physiology. But thanks to recent technological advances, we now have one. Pseudomonas aeruginosa is an opportunistic pathogen found in chronic infections of the foot (diabetic ulcers), ear and lung (cystic fibrosis) and is a model biofilm-forming bacterium that is notoriously difficult to eradicate with standard antibiotics. A defining aspect of P. aeruginosa is its ability to make phenazines, redox-active pigments that promote biofilm development and energy conservation under anoxia, shifting it into a physiological state that is highly antibiotic tolerant. Phenazines are extracellular electron shuttles that transfer electrons from the cell interior (thereby promoting intracellular redox-balance and permitting ATP to be generated) to the exterior (where they pass electrons to an extracellular oxidant). When the extracellular oxidant is an electrode, the current measured at the electrode reflects the cellular metabolic rate. Using a novel 96-well electrode system, we have demonstrated that we can study phenazine maintenance metabolism under anoxic conditions in high-throughput. The mass-specific power output of this non-growth metabolism is amongst the lowest ever recorded for any organism. We now seek to leverage the unique properties of this phenazine-cycling platform to gain a mechanistic understanding of how maintenance physiology is achieved. What limits the metabolic rate generated by cycling different phenazines over a physiologically relevant concentration range? How do cells economize their anabolic expenditures in a way that harmonizes with their low-power output? Aim 1 will explore what sets the energetic threshold for cellular viability, and how the physicochemical properties of phenazines impact how they are processed by the cell. Aim 2 will test the hypothesis that cells engaged in maintenance metabolism rely on anabolic recycling to economically spend their low energy resources; we predict that many of the key anabolic strategies needed for phenazine maintenance metabolism overlap those needed to survive other growth- arrested, phenazine-independent states, whereas others are specific for phenazine maintenance metabolism. The proposed work will provide us with new insights into the physiology of maintenance, an underexplored yet pervasive biological state, pointing us in the direction of new targets for drug development to treat devastating chronic infections.

NIH Research Projects · FY 2026 · 2026-04

Project Summary: Machine learning (ML) has the power to revolutionize chemical synthesis. However, its adoption by the organic chemistry community has been limited due to the reliance on highly specific datasets for model development and the challenge of evaluating the trustworthiness of predictions. The overall goal of this project is to integrate, high throughput experimentation (HTE), ML, and reaction descriptions encoding mechanisms to build predictive models that not only accurately determine the feasibility of a reaction for complex substrates but also provide a measure of uncertainty associated with the prediction, enabling more reliable and informed decision-making in chemical synthesis. This approach will yield a deep understanding of the use case reactions and enable the rational adaptation of reaction conditions to different substrates. We hope that the models developed on specific reactions can be adapted to many cases and will accelerate synthesis planning and reaction development. Successful ML models require a good coverage of the chemical space explored, a numeric representation accurately describing the underlying chemistry, and an adapted ML architecture. My research will focus on (a) developing and benchmarking optimal chemical space sampling using active learning, (b) setup new workflows to quantify reaction outcome in a HTE fashion and (c) develop unprecedented dynamic graph-based reaction representations. These representations should pave the way toward transfer learning between reaction classes. This unmet challenge has the potential to unlock the power of ML for organic chemistry. The K99/R00 Pathway to Independence Award will allow me to continue to leverage the expertise of Prof. Reisman and the resources at Caltech Center for Catalysis to master HTE and overcome real-life obstacles encountered during synthesis by designing ML to answer these experimental issues. Caltech’s close-knit community and emphasis on interdisciplinary work will allow me to leverage expertise across different fields. Prof. Reisman’s collaborations with Prof. Yue and Prof. Chawla, both experts in computer science, will provide me with the support and guidance to further develop my skills in active learning. The mentoring of Prof. Coley will complement my training in ML for chemistry, specifically graph based representations. Prof. Alexanian will advise me on C–H functionalization. This support committee constituted of organic chemistry and computer science experts will provide the best guidance in shaping my research trajectory, identifying research interests, and building strong partnerships. I will look for conferences to perfect my communication skills and attend Caltech-sponsored trainings in career development, grantsmanship, responsible research, and teaching. My mentor and my collaborators will aid me in establishing a solid foundation for my career and ensure that my research and training align with my long-term goals. My ultimate career goal is to become an independent researcher and leader in the field of ML for chemistry, specifically focusing on developing organic reactions modeling tools that change how chemists approach synthesis.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY Animals live in an interconnected social world and must interact adaptively with other organisms. Such interactions require translating complex multisensory profiles of other animals into appropriate behavioral responses. How the nervous system integrates socially relevant multisensory information to build internal representations of others and enact ecologically relevant behaviors is largely unknown. The goal of this project is to elucidate behavioral computations and neural mechanisms that organisms use to integrate salient multisensory features of other animals. I will use the rove beetle species, Dalotia coriaria, a newly developed genetically and experimentally tractable system. Dalotia is highly suited to investigate multisensory mechanisms that enable social interactions because 1) it is genetically amenable to neural manipulation and has a relatively small brain, and 2) possess a highly flexible abdomen that it engages during a variety of animal social interactions. The movement of its abdomen is a major, yet simple to observe and quantify, behavioral readout of the neural decision-making processes that occur when the beetle encounters different types of animals. I will first use three-dimensional tracking and novel machine learning approaches, such as neurosymbolic artificial intelligence, to classify the behavioral modules and stereotyped response patterns of Dalotia while interacting with a range of other species. I will then use a “beetle-on-a-ball” virtual reality setup to deconstruct the chemical and tactile sensory components of each organism and define the integration rules the describe how behavior is altered in unisensory verse multisensory conditions. Next, I will use gas chromatography electroantennogram recordings and in vivo 2-photon calcium imaging from the brain to identify multisensory representations of other species. Using an interdisciplinary approach, cutting-edge machine learning and computational ethology methods, and simultaneous neural and behavioral recordings, this project will uncover how animals build representations of other organisms from complex multisensory profiles. I will receive interdisciplinary technical and conceptual training from an advisory team of experts in the fields of neuroethology, chemical ecology, machine learning, and electrophysiology, which will prepare me for a successful transition to independent investigator.

NSF Awards · FY 2026 · 2026-04

This award supports the renewal of the existing Research Experiences for Undergraduates (REU) site program associated with the Laser Interferometer Gravitational-Wave Observatory (LIGO) project at California Institute of Technology. The project will support ten undergraduate students for ten weeks of summer research each summer for three years. LIGO provides an exciting opportunity for undergraduate students to participate in a cutting-edge astrophysics project and to help develop detectors with unprecedented precision. REU participants work closely with individual LIGO scientists on projects involving many aspects of detector technology, data analysis, and source modeling. Every student develops skills in scientific writing and communication, through preparation of a project proposal, progress reports, and a professional-quality presentation and report at the end of the summer. The LIGO REU program provides undergraduate students with a rewarding research experience at the frontiers of precision metrology, strong-field gravity, and observational astrophysics in which they make real and lasting contributions to a major scientific effort. Students work with and are mentored by professional scientists from LIGO Laboratory, Caltech theoretical astrophysics, and the global LIGO Scientific Collaboration and learn first-hand how large projects are organized and operate. They acquire new skills that are applicable in a broad range of technical careers. Depending on the projects, students might gain experience with optics, lasers, electronics, servo controls, mechanical systems, data analysis, numerical relativity, astrophysical source modeling, software development, artificial intelligence, quantum information science, or high throughput computing. All students gain experience participating in astrophysics 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 2025 · 2026-03

PROJECT SUMMARY The realm of genomics has consistently sought more precise and sensitive methods for capturing and analyzing the vast spectrum of genomic variation that exists between individuals, cell types, or even within a single organism's different cells. The proposed project aims to innovate within this space by developing a novel computational algorithm, termed klue (k-mer based local uniqueness exploration), which promises an unprecedented sensitivity and comprehensiveness in detecting and analyzing genomic variation across samples. This algorithm leverages the power of colored de Bruijn graphs and the bubble structures contained therein to perform efficient and comprehensive genomic analyses, with particular utility in contexts like cancer genomics where variability is the norm rather than the exception. Conventional sequencing analysis often relies on mapping to reference genomes, limiting the sensitivity and scope of variant detection. klue aims to bypass the constraints of current methodologies by exhaustively capturing variation between samples or variation between a sample and the reference genome, and then using that variation information in the sequencing read mapping process. That variation information can be used to create a more comprehensive reference transcriptome that can be mapped against or can be used to infer cell or tissue identity. The project is structured around three specific aims designed to develop, assess, and apply the klue method: 1) Development: To develop and codify the klue algorithm into a user-friendly software tool, 2) Assessment: To evaluate klue's efficacy in accurately inferring cell identity from sequencing data, emphasizing its utility in single-cell sequencing experiments, 3) Application: To utilize klue for enhancing reference sequences that reads are mapped against, potentially uncovering features in the sequencing reads that would be otherwise undetected due to their absence from the reference genome. Altogether, klue has the potential to redefine the landscape of genomic sequencing analysis by enabling a more accurate and comprehensive understanding of genetic variation. By shifting the focus to “sequence differences” rather than “sequence similarities” (as is done in reference genome alignment), one can unlock a wealth of information previously inaccessible.

NIH Research Projects · FY 2026 · 2026-03

TITLE Mechanisms in the mammalian circadian clock ABSTRACT The circadian clock is a conserved molecular oscillator that synchronizes biological processes with the ~24-hour day-night cycle, regulating critical functions such as sleep-wake patterns, metabolism, and immune responses. Disruptions in circadian rhythms are linked to disorders like Delayed Sleep Phase Disorder (DSPD) and Advanced Sleep Phase Disorder (ASPD), as well as psychiatric conditions such as depression and anxiety. Circadian oscillations are driven by a central transcription-translation feedback loop (TTFL) involving core clock proteins— CLOCK, BMAL1, CRY1, PER2, CK1d, and GSK3b. While studies have explored individual interactions between these proteins, key aspects of their network dynamics and the molecular mechanisms governing their nucleocytoplasmic transport remain poorly understood. This proposal seeks to address these gaps through three specific aims: (1) define the full protein interactome of the human circadian clock by reconstituting core TTFL proteins and mapping their interactions using biochemical and biophysical techniques and reveal complex dynamics, stoichiometry, and isoform-specific effects; (2) elucidate the molecular basis of CRY1- and PER2- mediated transcriptional repression of CLOCK•BMAL1 by resolving their atomic structures, while assessing how isoform variations and our recently identified DSPD-linked CRY1 variant impact repression; and (3) investigate the nucleocytoplasmic transport of circadian proteins by identifying their karyopherin (KAP) transport factors through systematic interaction studies with recombinant proteins, validated using in vitro transport assays and cellular localization studies. Collectively, this work will provide a comprehensive understanding of the dynamic interactions, transcriptional repression mechanisms, and nucleocytoplasmic transport processes essential for establishing the central TTFL and sustaining circadian rhythms. These findings will advance our knowledge of circadian biology and identify potential therapeutic targets for sleep and psychiatric disorders associated with circadian dysfunction.

NIH Research Projects · FY 2026 · 2026-03

Project Summary This MIRA renewal proposal builds on the foundational advances made in the previous funding period to further explore and expand the synthetic utility of carbocation chemistry, with an emphasis on catalytically generated, highly reactive vinyl carbocations. The overarching goal is to develop novel, selective synthetic methodologies that leverage the unique reactivity of these intermediates, enabling the construction of complex, enantioenriched molecular frameworks relevant to bioactive compound development. The proposed research encompasses three major thrusts: (1) expansion of enantioselective C–H insertion reactions of vinyl carbocations to access diverse cyclic and polycyclic systems, (2) development of innovative electrophilic reactions and cross-coupling methodologies involving vinyl carbocations, and (3) the creation of advanced (bio)catalysts, including chiral IDPi catalysts and engineered protein enzymes, to mediate selective transformations. In parallel, the program seeks to deepen the mechanistic understanding of carbocation chemistry through computational and experimental studies. Building on the applicant’s pioneering efforts in microcrystal electron diffraction (microED), the renewal also aims to advance the application of microED in structural studies of reactive intermediates and organic synthesis. The anticipated outcomes of this work include transformative synthetic methodologies and fundamental insights into carbocation reactivity and catalysis. These contributions will not only address longstanding challenges in organic synthesis but also support the mission of NIGMS by advancing basic science that underpins biomedical innovation.

NSF Awards · FY 2026 · 2026-03

This RAPID project will investigate the effects of ongoing wildfires burning across Canada that continue to degrade the air quality in many parts of the U.S., including the Atlanta area. Aerosols emitted by wildfires can travel thousands of miles away from their sources and continue to age along the way, blending in with aerosols emitted in downwind areas. The Atmospheric Science and Chemistry mEasurement NeTwork (ASCENT) site in Atlanta has recently observed increases in organics, black carbon, and potassium at the site that is attributed to Canadian wildfires. Aerosols from wildfires can adversely impact those with asthma and other respiratory diseases. Results from this work will facilitate a better understanding and representation of the aging of wildfire aerosols in models for improved prediction of their widespread impacts. The overall goal of the project is to deploy an advanced mass spectrometer at the ASCENT site in Atlanta to quantitatively constrain the impacts of Canadian wildfires on particulate levels and composition in the Atlanta area. This mass spectrometer is capable of real-time measurements of speciated gas- and particle-phase organics. With the timely deployment of the advanced mass spectrometer and co-located ASCENT measurements at a time with both wildfire smoke and biogenic emissions from local forests and plants, this work will provide for the first time a quantitative assessment of the full influence of Canadian wildfires on particulate levels and composition in Atlanta. The project will quantify not just fresh smoke aerosols, but also aged smoke aerosols, and how fresh and aged aerosols can affect gas/particle partitioning of biogenic organics. This project will provide training opportunities for a postdoctoral researcher and a graduate student on a timely and important research topic that has wide relevance and interest. 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

Turbulent fluid flow plays a central role in technologies encountered in daily life, including aircraft, automobile, and energy systems. The ability to predict the behavior of a turbulent flow near solid surfaces is important, because it affects drag, efficiency, and stability. The flow can separate from a solid surface in many practical situations. Separation creates complex motions that are difficult to predict. Current modeling tools often struggle in these conditions, which limits their usefulness as a design tool. This CAREER project will improve the reliability of turbulence modeling. It will address how small motions near surfaces influence the overall flow behavior. The outcomes will strengthen predictive models and keep their computational costs manageable. The project will integrate research and education by training students in fluid dynamics and modeling techniques and by engaging audiences in outreach activities that promote scientific literacy. The project aligns with NSF priorities by supporting advanced manufacturing of engineering applications such as aircraft, ships, and energy systems. The research will focus on understanding how the directionality of unresolved small-scale turbulent motion affects models used for large-scale flow prediction near walls. The project will begin by analyzing high-resolution flow data to examine how these small motions behave under different conditions, including both attached flows and flows that separate from surfaces. This analysis will reveal which features of the small-scale motion are most important for accurate modeling. Guided by these findings, new modeling approaches will be developed that incorporate these effects into existing near-wall frameworks. The models will be evaluated using a range of challenging flow configurations relevant to engineering applications and assessed against experimental measurements and high-fidelity reference data. This project will support a sustained research and education program, providing hands-on research experiences for undergraduate and graduate students, developing course and training materials in turbulence modeling, and hosting outreach activities for local high school students and teachers. The outcomes will contribute to increase the education of engineering students while advancing tools that benefit aerospace, automotive, and energy industries. 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 project will support the continued access of U.S. researchers to the CLOUD consortium at CERN to study the chemistry and physics that drive new-particle formation and growth in Earth's atmosphere. CLOUD is a unique state-of-the-art 26 cubic meter stainless-steel chamber with precise control over temperature and relative humidity. There is no chamber facility within the USA with these capabilities. Because these particles influence cloud formation and how energy moves through the atmosphere, this research helps improve our understanding of weather behavior and atmospheric conditions. Experiments over the next three years using the CLOUD chamber will include a focus on developing molecular descriptions of new particle formation and advancing parameterizations for use in scientific models. These experiments will include mixtures relevant to the upper atmosphere, with sulfuric and nitric acids, ammonia and other bases, and iodine oxidation products. The CLOUD experiments often lead to advances in instrumentation and provide an ideal test bed for new instruments. Students and early career researchers will be integrated into an international collaborative network of students and senior researchers and have the opportunity to work with some of the most highly regarded scientists worldwide in the study of aerosols and atmospheric chemistry. 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 2025 · 2025-12

PROJECT SUMMARY Sleep disorders affect millions of people in the U.S. each year, and efficient sleep is vital for human health and cognitive functions. Developing an understanding of the molecular and genetic pathways that regulate this highly conserved behavior is essential for developing therapies to address these disorders. Human genome-wide association studies (GWAS) have identified many genomic loci that contain genetic variants associated with sleep phenotypes. However, because most of these variants are in non-coding regions, it is unclear which gene within each locus is responsible for the associated sleep phenotype. Thus, these screens have identified many candidate human sleep disorder genes, but their potential contributions to sleep must to be tested in an animal model. In order to address this need, the Prober lab has used CRISPR-Cas9 to mutate and test the zebrafish orthologs of many GWAS-identified human candidate sleep disorder genes. Zebrafish are a useful model system for this purpose because many mutant lines can be quickly and inexpensively generated using CRISPR-Cas9, and the amenability of zebrafish to high-throughput sleep assays makes it possible to efficiently test many different mutants for sleep phenotypes. Additionally, zebrafish are diurnal vertebrates, like humans, whose demonstrated genetic and neuronal mechanisms that regulate sleep are broadly conserved with those of mammals. This project focuses on one gene identified in this screen, the largely uncharacterized protein kinase serine threonine kinase 32a (stk32a). In preliminary studies, we found that stk32a mutant zebrafish exhibit increased nighttime sleep. I will exploit advantageous features of zebrafish to determine the mechanisms through which stk32a acts to influence sleep. First, based on the night-specificity of the mutant phenotype, I will assess whether stk32a regulates dark-dependent, circadian-dependent, or homeostatic mechanisms of sleep regulation. I will do so by performing behavioral experiments using different lighting conditions, by monitoring rebound sleep after sleep deprivation, and by testing for interactions with genes that regulate each of the above mechanisms of sleep control. Second, based on my preliminary gene expression and behavioral data, I will test the hypothesis that the stk32a mutant phenotype is caused by disrupted detection of sensory stimuli by lateral line hair cells. To do so, I will assess the effects of stk32a mutation on lateral line hair cell structure and function, test the effect of disrupting the lateral line on sleep, and test whether rescuing stk32a function in hair cells is sufficient to rescue the stk32a mutant sleep phenotype. The results of these experiments will help to determine the molecular and genetic mechanisms through which stk32a regulates sleep. Together with the NIH’s “Illuminating the Druggable Genome” program, where work is being done to identify stk32a small molecule inhibitors, these studies may eventually lead to novel therapies for sleep disorders based on inhibition of stk32a function.

NSF Awards · FY 2025 · 2025-10

Generating nuanced yet interpretable hypotheses from noisy and high-dimensional observations is a core activity across numerous scientific disciplines. For example, across neuroscience, genomics, biomechanics, and ecology, researchers analyze images and videos to study phenomena ranging from the genetic expression of lab mice to global species distributions of birds. Useful scientific hypotheses are typically instantiated on symbolically interpretable concepts and attributes (such as stride periods and center of mass oscillations when studying kinematics of walking). A key challenge is to extract such symbolically interpretable hypotheses from raw high-dimensional observational data. To address this challenge, this project aims to develop a novel neurosymbolic programming framework, called foundation model programming, for generating symbolically interpretable scientific hypotheses from high dimensional observational data. The main idea is to represent interpretable hypotheses as neurosymbolic programs that use symbolic primitives as well as neural modules, including foundation models. The use of neural and foundation models allows the hypotheses to be full-stack, modeling both the extraction of relevant patterns and motifs from high-dimensional raw data and reasoning over those patterns. The core technical benefit of this approach is that it inherits both the contextual flexibility of modern foundation models given high-dimensional inputs, and the rigorous reasoning abilities offered by neurosymbolic approaches. The proposal is backed by a substantial amount of prior work on both neurosymbolic learning and applying foundational models in scientific domains, including foundation model-enabled hypothesis generation for low-dimensional data, self-supervised symbolic feature extraction from high-dimensional data, data-efficient expert-in-the-loop training approaches, and deep deployment into real scientific workflows. Importantly, the use of foundation models enables building methods that are more autonomous, and require fewer manual annotations by the expert. This project will develop algorithms that can jointly reason over what are the most useful symbolically interpretable concepts or motifs that can be extracted from the raw data, as well as how to compose those concepts into coherent hypotheses. This paradigm mirrors how humans develop hypotheses, by jointly establishing a discrete vocabulary of concepts (from continuous high dimensional descriptions) and reasoning over those concepts, thus enabling interpretability. This project will benchmark the developed methods on several scientific tasks and domains and 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.

NSF Awards · FY 2025 · 2025-10

New technologies are needed for the U.S. to maintain leadership in the development and manufacture of next generation computer chips. A critical step in the manufacture of computer chips is the deposition of electronic materials, layer by layer, onto a surface, in precise patterns to create a circuit. A particularly precise method is called atomic layer processing, which can deposit a single layer of atoms onto a surface (atomic layer deposition), or remove a single layer of atoms from a surface (atomic layer etching). However, these processes require very high temperatures and do not work for all electronic materials. A bold new idea is to use electrons to enable the deposition or etching processes to work at lower temperatures. In the research funded by this award, atomic layer processing systems will be equipped with special emitters that direct an electron beam to the surface and drive reactions that would not otherwise be possible. Special instruments designed to work inside the atomic layer processing equipment will measure the progress of the reactions in real time, providing fundamental information that can be used to control and improve the process. Once this concept has been demonstrated, the project seeks to make superconducting thin films that can be used as “qubit” chips in quantum computers. The project will also pioneer new ways to train students in use of clean room technology that is central to the semiconductor industry. The training modules will use augmented reality / virtual reality tools to make this training available to students who do not have access to clean room facilities. Atomic layer processing (ALP) is based on self-limiting chemical reactions of gases, plasmas, or molecular precursors at a surface, enabling deposition and etching with monolayer control. This project will advance fundamental understanding of electron-enhanced ALP (EE-ALP), where electrons are used to drive electron-stimulated reactions that would otherwise not occur in the given conditions. Although electron-stimulated chemistry has been studied in other contexts, the chemical mechanisms occurring in the unique self-limiting surface reactions of EE-ALP remain largely unexplored. A key component of our technical approach will be the first use of mid-infrared frequency combs to track the dynamics of interacting chemical species in ALP in real time, allowing a quantitative description of the chemical kinetics to be revealed. The fundamental understanding created by this project will open new process windows and new chemistries for deposition and etching of thin films with atomic precision, which is key to enabling future technologies such as quantum computers. The knowledge will also impact adjacent fields involving molecule-substrate interactions such as heterogeneous catalysis. Anticipated Transformative Impact:Superconducting thin films for quantum computer chips. 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

Observing and modeling the merger of neutron stars with each other and with black holes are challenging tasks that push understanding of the universe as embodied by Einstein’s equations for general relativity. Observatories such as National Science Foundation-funded Laser Interferometer Gravitational-Wave Observatory (LIGO) need the results of computational models to help understand the signals they receive. Computational modeling of neutron star mergers in turn is extremely demanding and requires the use of the most advanced supercomputers. Even on these unique computing resources operated by the US Department of Energy and the National Science Foundation, computer models may still require weeks to run. Improvements to gravitational wave modeling software that take advantage of improved algorithms have the potential to reduce this execution time down to hours. This project improves the open-source gravitational wave-modeling software SpECTRE to use new algorithms and to make optimal use of one-of-a-kind supercomputing resources. The results from these computations are needed for scientists to understand black holes, the expansion of the universe, and how stars explode and leave behind black holes. The transformative techniques used by SpECTRE have the potential to also be applied to research areas in fluid dynamics, geoscience, plasma physics, and nuclear physics and engineering. The project is training the next generation of computational astrophysicists on the use and extension of SpECTRE through summer schools. The investigating team engages the public through visualizations and movies posted on social media and through public outreach events. The new SpECTRE code uses a hybrid finite difference-discontinuous Galerkin method, task-based parallelism, and the U.S. cyberinfrastructure Graphical Processing Unit (GPU) abstraction library Kokkos to accomplish its goals. This framework will allow multiphysics applications to be treated both accurately and efficiently on the new architectures of petascale and exascale machines. The code is designed to scale to over a million cores for efficient exploration of the parameter space of potential sources and allowed physics, and for the high-fidelity predictions needed to realize the promise of multi-messenger astronomy. The software design separates parallelism and physics capabilities in a way that makes adopting new computing paradigms and libraries possible without rewriting the physics modules. The code will allow astrophysicists to understand electromagnetic transients and gravitational-wave phenomena in compact objects, to reveal the dense matter equation of state, and to perform binary black hole simulations at the accuracies necessary for next-generation detectors. The key algorithmic innovations in the code, the hybrid finite difference-discontinuous Galerkin method coupled with task-based parallelism and GPU offloading, promise revolutionary impact in other fields relying on numerical solution of partial differential equations at the exascale. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Physics and the Division of Astronomy in the Mathematical and Physical Sciences Directorate. 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 advance our capabilities to understand and predict two critical aspects of the Earth system by pioneering a new approach driven by artificial intelligence (AI). For decades, the complexity of the underlying physics has limited progress in quantifying the cooling effect due to atmospheric aerosols and their effect on clouds and the rate of Arctic sea ice melt. This project tackles these challenges by using advanced AI to learn directly from satellite observations and laboratory data, developing more accurate and reliable computer simulations. These will in turn result in improved predictive capabilities that are vital for U.S. strategic interests, for example, as a warming Arctic opens new maritime shipping routes essential for commerce and security. More reliable environmental intelligence will support better-informed decisions for infrastructure planning and risk assessment. The project will also make all its AI tools and software openly available and will train a new generation of researchers in these cutting-edge methods. To address current limitations in Earth System Models (ESMs), this project will develop and implement novel parameterizations for aerosol-cloud interactions (ACI) and Arctic sea ice thermodynamics. The research will leverage AI, specifically a novel framework called Ensemble Kalman Diffusion Guidance (EnKG), to learn from a diverse range of observational and laboratory data. For ACI, the project will develop new data-driven models for how aerosols form cloud droplets and ice crystals, using high-fidelity simulations for pre-training before online fine-tuning in an ESM, specifically the ESM developed by the Climate Modeling Alliance (CliMA). For sea ice, the research will build an improved thermodynamic model, incorporating machine learning components to better represent processes such as melt ponds and albedo feedback. The EnKG framework will be developed to efficiently train these embedded ML parameterizations using large-scale satellite observations without requiring model derivatives. Finally, the project will conduct ESM simulations using the new parameterizations to provide improved, uncertainty-quantified estimates of aerosol radiative forcing and more robust projections of future Arctic sea ice decline. 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

Quantum computing has inspired global excitement owing to its potential to efficiently solve computational problems that are extremely difficult on classical computers. However, significant technological advances are necessary to achieve the full potential of quantum computing. This project will investigate the origins of noise in SiGe-alloy-based transistors which could be used to measure the qubit state. Unlike current technology, these transistors have potential for future large-scale quantum computing platforms because they can be scaled and integrated into a complex circuit by leveraging mature Si technology. However, their noise performance remains inadequate for demanding applications in quantum computing. This project will address this knowledge gap via a collaborative research effort between researchers in the UK and US specializing in materials growth and device physics, respectively. The project will create fundamental knowledge regarding the structural and chemical properties, electrical characteristics, and microwave noise performance of SiGe heterojunction bipolar transistors (HBTs) optimized for cryogenic operation. The physical origin of discrepancies of cryogenic electrical transport characteristics from theory and their effect on microwave noise performance remain a topic of intense interest. This work will address this knowledge gap via a collaboration involving epitaxial growth of custom SiGe/Si epitaxial films and fabrication of HBTs, followed by materials and electrical characterization at cryogenic temperatures. The team combines expertise in the epitaxial growth of SiGe/Si heterostructures on Si and semiconductor materials and devices characterization (Myronov, UK) and in HBT device fabrication and characterization (Minnich, USA). The integration of growth, fabrication, and characterization tasks in a single effort will allow for significant new insights into HBT device physics which is not possible from independent investigations. 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 program aims to create semiconductor amplifiers with outstanding noise performance in the microwave spectrum. Researchers from the California Institute of Technology (CalTech) and the University of Nevada-Reno (UNR) will accomplish this goal by leveraging a new nanofabrication method, atomic layer etching, which permits semiconductor device manufacturing with atomic-scale precision. If successful, these new amplifiers will be 30% more sensitive to radio emissions from space, meaning that less time will be needed for radio telescope measurements than is currently needed, potentially enabling new scientific discoveries. Based in part on this research, the team will develop a new college course at the UNR on nanofabrication, which will bring new technical training opportunities to students at UNR and in Northern Nevada. High electron mobility transistors (HEMTs) are used ubiquitously throughout radio astronomy observatories. Despite their impressive noise performance, their noise temperature has plateaued in recent years, in part due to challenges in scaling their dimensions even smaller. This project aims to overcome these limits by leveraging a new nanofabrication method, atomic layer etching, into the fabrication of low-noise HEMTs for the first time. The microwave noise performance has potential to improve by 30%, thus enabling significantly improved observational efficiency. These new HEMTs will be directly deployable to radio telescope systems as a drop-in replacement for the low-noise amplifier, meaning that an immediate improvement in sensitivity can be achieved without requiring any other upgrades. To maximize the broader impact of this project, the team will develop a new course on nanofabrication at the University of Nevada-Reno as well as incorporate graduate research activities into their new cleanroom facility. 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.