Cornell University

NIH Research Projects · FY 2025 · 2025-08

Project Summary/Abstract Ubiquitination is a central mechanism that regulates a plethora of physiological processes in eukaryotes. Defects in the ubiquitination pathway are associated with many human diseases, including cancer and neurodegenerative diseases. Given the essential role of the ubiquitination pathway, it is not surprising that pathogens often exploit the host ubiquitin system to achieve their successful infection. My laboratory has a long-term interest in ubiquitin mechanisms in normal cell physiology and pathogen-host interactions. In collaboration with Dr. Scott Emr, we have discovered that several adaptor proteins mediating substrate ubiquitination by the Nedd4 family ubiquitin E3 ligases are themselves specifically di- ubiquitinated. We found that this modification is required for substrate ubiquitination, enhancing the interaction between the E3 ligases and their cognate adaptors, and promoting the intracellular recruitment of the E3s. However, the structural and biochemical basis of adaptor di-ubiquitination remains largely unknown. On the other hand, bacterial pathogens, such as Legionella pneumophila often exploit host ubiquitin to facilitate their intracellular proliferation. We have identified HECT-like ubiquitin ligases, secreted by the bacterium to hijack host ubiquitin. We also made significant contributions to the discovery and characterization of a novel type of ubiquitination, namely, the phosphoribosyl serine ubiquitin or PR- ubiquitination. Our laboratory also discovered specific DUBs that reverse PR-ubiquitination and an effector that catalyzes polyglutamylation on PR-ubiquitination ligases to inhibit the ligase activity in a Calmodulin- dependent manner. Built upon our previous findings, our future research under the MIRA will focus on two ubiquitin-related areas of research: 1) To elucidate the molecular mechanism of adaptor-mediated ubiquitination catalyzed by the Nedd4 subfamily of HECT E3 ligases; 2) To investigate the hijack of the host ubiquitin system by the intracellular bacterial pathogen, Legionella pneumophila. The characterization of specific di-ubiquitination of Nedd4 E3 adaptors will pave a new avenue to address key questions regarding how substrates are recognized and presented by adaptors and how the activity of Ne44d4 family E3 ligases is regulated. The study of ubiquitin hijacking in L. pneumophila infection is of also fundamental importance to our understanding of the versatile ubiquitin code. Since many Legionella effector proteins have eukaryotic origins evolutionarily. A fascinating future direction remaining to be addressed is whether there is a similar PR-ubiquitination system in eukaryotes. Taken together, our proposed in vivo and in vitro experiments using a combination of biochemical, cell biological, and structural biological approaches will enable us to make significant contributions to the understanding of novel molecular mechanisms of the ubiquitin system in normal cell physiology and in pathogen-host interactions.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT: Criminal justice contact is an important contributor to population variation in health outcomes, including mortality, morbidity, and aging. During and following the COVID-19 pandemic, people in American jails, prisons, and detention centers experienced elevated health risks that exacerbated their already high morality and aging precariousness. This proposal requests support for a three-year conference program on, “Health, Mortality, and Aging Among People with Criminal Legal System Contact in America,” with each year focusing on critical questions about the health of incarcerated people that have emerged since the COVID-19 crisis. These interdisciplinary conferences will harness the research and expertise of established and emerging scholars conducting research at the intersections of health, mortality, and aging in economics, sociology, demography/population science, law, criminology, public health, medicine, and public policy. The proposed conference programs are innovative by including the participation of people affected by legal system involvement, as well as by investigating population heterogeneity to formulate and to advance a bold new research agenda that will benefit affected communities. The program has four specific aims: (1) to advance scientific knowledge and research recommendations to improve health across the life-course for people involved in the criminal legal system; (2) to amplify the voices of affected people, families, and communities; (3) to train the next generation of criminal legal scholars; and (4) to engage multiple audiences. Deliverables will include: (1) three special issues, each devoted to a unique conference theme over the three years (i.e., mortality, health, and aging), allowing for the discrete and unique treatment of each topic; and (2) research briefs that translate findings into potential interventions and recommendations that broadly engage the individuals, families, and communities subject to criminal justice contact, as well as other stakeholders (government officials, prison and court actors, non-profits, and other advocates). By discussing and documenting how life-course transitions that intersect with the criminal legal system matter for socio-economic, health, and well-being, this conference will engage and advance the conceptual and empirical dialogues that began with several National Academies of Sciences, Engineering, and Medicine workshops in 2013 and 2020, and, more recently, a half-day seminar in 2024. As leaders in the criminal legal field, the investigative team and Cornell University are uniquely positioned to host this program.

NSF Awards · FY 2025 · 2025-08

This project aims to advance simulations of gas-liquid mixtures, especially when they break apart into small droplets or bubbles and when small droplets or bubbles come together. These fluid behaviors are commonplace in nature, for example in the breaking of waves at the ocean surface or formation of raindrops in clouds. They are also crucial for many engineering applications, such as the injection of fuel in combustion engines, the spraying of crop protection products, or the production of powders in the food and pharmaceutical industries. The most common simulation methods struggle to capture small but important details, such as very thin liquid sheets or tiny droplets, which limits accuracy and utility of the results. This project will develop new ways to represent and model these fine details of fluid behavior, resulting in more accurate simulations without requiring expensive computer resources. The approach will allow scientists and engineers to better predict how gas-liquid mixtures behave in complex situations, making engineering design more affordable and more accurate. The proposed research will also contribute to modernizing course content for training undergraduate and graduate students, while fostering collaboration with industry to promote the widespread adoption of open-source software tools. Multi-scale two-phase flows play a central role in many natural phenomena but also in several key industrial sectors, such as energy production, transportation, manufacturing, and the food and pharmaceutical industries. Traditional Eulerian interface capturing methods fail to accurately predict topology changes of the gas-liquid interface due to mesh resolution limits and numerical errors. To address these limitations, this project proposes two key innovations: (1) a new piecewise-quadratic interface representation that enables the capture of sub-grid scale structures such as thin films/sheets and ligaments, and (2) a new volume-filtered framework in which sub-grid scale surface tension-driven physics are accounted for through closure models. The outcome will be a framework capable of accurately predicting break-up and coalescence events as well as droplet size distributions in multi-scale two-phase flows, a feat that has so far remained elusive to even the most refined simulation frameworks. This project marks a shift from expensive and often insufficient direct numerical simulation to efficient, physics-informed modeling, through the introduction of novel sub-grid scale interface representation. This promises both more affordable and more predictive simulations of multi-scale two-phase flows. Industrial impact will be maximized by the open-source release of the developed numerical tools, their integration in commercial codes, and the organization of user workshops. The project will also modernize course content on multiphase flows to benefit engineering education. 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 · 2025-08

PROJECT SUMMARY / ABSTRACT In this project we are focused on chemically perturbing Mtb persistence mechanisms as a way to augment or shorten TB treatment. It is understood that Mtb metabolizes host lipids to persist and recent work has discovered that chemically stimulating cAMP production in Mtb blocks the bacterium’s ability to metabolize host lipids. Importantly, two clinical candidates with this mechanism of action (Rv1625/cya agonists) improve the bactericidal and sterilizing activity of BPaL and shorten treatment in mice. The overall objective of this project is to examine how cAMP stimulating compounds exert their inhibitory properties on Mtb and explore how these compounds could be leveraged to mitigate resistance while synergizing with other TB drugs. The central hypothesis of this work is that cAMP stimulators inhibit Mtb growth by perturbing bacterial lipid metabolism and combining multiple cAMP stimulators with unique targets will amplify this inhibitory process and reduce the frequency of resistance to this class of compounds. Activating cAMP synthesis in Mtb blocks lipid metabolism but a mechanistic understanding of this process is lacking. Thus, we will focus on characterizing a novel cAMP stimulator (CU35), determine how cAMP blocks lipid metabolism in Mtb, and we will combine multiple chemical and genetic approaches to amplify cAMP production in Mtb to determine how this impacts Mtb physiology (Aim 1). We have found that CU35 stimulates cAMP production independent of Rv1625/cya suggesting that other compounds that stimulate cAMP synthesis in Mtb may exist. To discover additional cAMP stimulators, we will conduct a novel high-throughput chemical screen to identify compounds that stimulate cAMP in a Rv1625/cya-independent manner. Compounds emerging from our screen will be prioritized based on structure novelty, safety, potency, PK properties, and synergy with known cAMP stimulators (Aim 2). The most important therapeutic property of cAMP stimulators is their ability to improve the bactericidal and sterilizing activity of BPaL in mice. Thus, we will evaluate our innovative idea that stimulating cAMP through multiple independent routes could amplify synergy with BPaL in mice. We will also evaluate lead compounds from our chemical screen for BPaL potentiating activity in mice (Aim 3). Overall, these studies are highly significant because they will provide information and new lead compounds to ultimately allow us to combine multiple cAMP stimulators with BPaL and achieve an innovative short acting TB drug regimen. The studies will provide a basic biological understanding cAMP signaling in Mtb, define the novel mechanism of action involving cAMP-dependent control of lipid metabolism in Mtb, and will address practical drug development questions around synergy with cAMP stimulators.

NIH Research Projects · FY 2025 · 2025-08

Project Summary Establishing a fundamental understanding of redox processes in biology is a key challenge with the potential to transform research in areas ranging from the development of biomimetic materials for efficient energy harvesting, small molecule drug design to mitigate disease pathways, and even in the search for quantum materials that exhibit sensitive control of electrons and spin dynamics. While electronic structure theory and multiscale methods like QM/MM can uncover experimentally inaccessible intermediate structures and even provide insights into some thermodynamic properties these methods are inherently static and do not account for quantized nuclear motion. Using the path integral formulation of quantum mechanics, we will build a theoretical framework that retains the computational efficiency of classical molecular dynamics but that can elucidate kinetic factors – uncover reaction pathways and compute rates – while taking into account the inherently quantum nature of charge transfer by capturing zero-point energy, tunneling, and even quantum coherence effects. Working with experimental collaborators, we will build a broadly applicable simulation toolbox for the characterization of redox processes in biology and we will demonstrate them on model systems including a nitrification enzyme, a multi-metal center drug candidate, and a radical relay protein that exhibits long range electron transfer (ET). These studies will allow us to answer key outstanding questions in redox biology: (i) Can we ‘see’ the sequence of electrons and protons transfer events in enzyme active sites? What residues control the order and efficiency of the transfer events? (ii) How does the environment (solvent, co-localized small molecules, metal centers, crowding) affect redox reactivity? Can we identify pathways for decoherence and energy dissipation and corresponding control strategies? (iii) What are the key dynamically and statistically correlated fluctuations that drive long range ET? Can we identify fundamental design principles for efficient long range ET?

NSF Awards · FY 2025 · 2025-08

Graphs, representing complex sensing and other societal systems like disease networks, social networks, and communication networks, are essential in understanding interactions within these systems. By accurately modeling relationships and structures within data via graphs, today machine learning over graphs (LoGs) plays a vital role in various applications. However, LoG introduces additional hyperparameters such as graph topologies and nodal embeddings into the already complicated neural network training processes. Traditionally, LoG approaches relied on user-defined heuristics to extract features encoding structural information about a graph. However, this process becomes prohibitively expensive in large models and high-dimensional data regimes, and the performance of LoGs highly depends on the choice of these hyperparameters. To address these challenges, the project puts forth a unified bi-level optimization-based training framework for LoGs with automatic selection of hyperparameters. The project also supports the education and diversity goals of the NSF by integrating LoGs research advances into machine learning courses taught in University of California at Irvine and Rensselaer Polytechnic Institute, making cutting-edge LoGs techniques more accessible to a wider range of researchers and students, fostering innovation and inclusivity in the scientific community. Towards this goal, this project aims to develop a bi-level optimization (BLO) framework for trustworthy and efficient LoG, called BLoG. In addition to the basic algorithm and optimization theory development for BLoG, the project will build a tri-level BLoG problem for robust and adversarial graph neural network training tasks, tailoring gradient-based BLO algorithms to these problems. The project will also develop a BLoG framework with multiple lower-level problems for multiple LoG tasks, named Fast-BLoG. Fast-BLoG will tackle fast and efficient semi-supervised graph neural network training. The project will highlight the advantages and new technical challenges of using the BLoG framework for handling machine learning tasks over graphs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-08

Mechanisms of robustness in organogenesis Project Summary/Abstract The size and shape of an organ are essential for its function. Developing organs are remarkably robust, meaning that they reproducibly grow to reach the same size and shape in each individual, despite variability in cell growth and division, the inherent stochasticity of gene expression, and environmental fluctuations. The systems-level mechanisms generating this robustness remain largely unknown. When developmental robustness mechanisms in humans are disrupted and these perturbations are not buffered, organ size and shape become variable, resulting in birth defects. My laboratory has focused on deciphering the systems-level mechanisms that yield robustness in organ size and shape. We have established the Arabidopsis sepal, the outermost green leaf-like floral organ, as a model system for studying robustness because we can image the development of living sepals, track the cell lineages, and quantify the cell growth, division, and expression levels of fluorescent markers in the cells throughout the development of the sepal, making it a powerful system for elucidating how cells give rise to the organ size and shape. Our interdisciplinary approach that combines advanced live imaging, genetic engineering, advanced image processing, and computational modeling, uniquely enables us to gain mechanistic insights that reflect both biochemical and mechanical factors of perturbation and robustness. Previously, we isolated the first sepal robustness mutants, and we demonstrated that robustness of size and shape is generated through: (1) precise initiation of organ development, (2) averaging of local fluctuations in cellular growth, and (3) coordination of growth across cell layers of the organ. However, the molecular details of how these mechanisms maintain robustness are largely unknown. Over the next five years we will focus on three complementary and synergistic research areas to further elucidate these robustness mechanisms. First, we will determine how initiating of the development of the organ from the stem cells is robust to stochasticity in hormone signaling. The plant hormone auxin is crucial for organ initiation and our preliminary data suggests auxin signaling is noisy early in development but becomes stabilized into four regions which give rise to four sepals. We will quantify intrinsic versus extrinsic noise in auxin signaling and determine the function of DRMY1 in dampening noise. Second, we will elucidate how organ growth is robust to cellular growth fluctuations. We will test the role of microtubules and intercellular mechanical signals in spatiotemporal averaging of cellular noise to produce robust organ shapes. Third, we will determine how growth coordination mechanisms produce organ shapes that are robust to mechanical conflicts caused by differential growth rates across cell layers. Our work points to a role for the mobile transcription factor ASYMMETRIC LEAVES 2 and microRNAs that can move from cell to cell through plasmodesmata (small pores connecting cells) in coordinating cell growth, which we will test. Our combined results will reveal mechanisms and principles of robustness that together produce highly reproducible organ sizes and shapes.

NSF Awards · FY 2025 · 2025-08

Given the growing sensing and computing power of wireless devices, amplified by increasing concerns on data privacy, a sizeable number of artificial intelligence and machine learning tasks are running at the devices distributed in wireless networks. Unfortunately, most of learning algorithm developments pay little attention to the underlying system constraints; and, most of existing communication and network designs rarely account for the unique characteristics of running learning tasks. To catalyze the synergies between rapid machine learning developments and the wireless network designs, this project aims at a transformative co-design of distributed learning algorithms and wireless networks. This CAREER project will further integrate an educational plan with the research goals by i) revamping the existing sensor network course with distributed learning components; ii) directly involving undergraduate and graduate students in research, especially from under-represented groups; and, iii) outreaching to the general public, in particular K-12 students and teachers. Towards this goal, this CAREER project will pursue i) system-aware learning, and ii) system-learning co-designs. For system-aware learning, the project will first develop resource-efficient distributed learning algorithms, whereby learning updates will be executed parsimoniously. Robust implementation of these resource-efficient algorithms will be studied to account for user mobility and adversarial attacks in unreliable wireless channels. Regarding system-learning co-designs, the project will develop new learning-while-managing algorithms that maximize the learning accuracy through joint learning, power control, queueing, and workload management schemes. This project presents an ambitious plan to enable system-learning co-designs of future wireless networks. Its fundamental advances will also impact sociotechnical systems, such as power grids, urban transportation systems and water/gas distribution 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 2025 · 2025-08

One of the foundational challenges in computer science is designing data structures that are both space- and time-efficient. Efficient data structures directly impact the performance, energy use, and cost of computing systems that underlie national-scale infrastructure—from supercomputers to cloud services to embedded systems in defense and transportation. In recent theoretical work, the PIs introduced a new technique, the tiny pointer, with the potential to substantially reduce the memory footprint of pointer-based data structures, enabling performance and efficiency gains across a wide range of systems. This project aims to translate tiny pointers from a theoretical insight into a deployable, foundational systems tool. By making tiny pointers broadly applicable, this research supports the national interest in advancing high-performance, resource-efficient computing critical to economic competitiveness, technological leadership, and secure infrastructure. At a high level, tiny pointers are compressed representations of memory addresses that retain compatibility with modern hardware and software systems. This project aims to develop foundational techniques for implementing tiny pointers, and the project has three primary goals: (1) to establish principles for building practical, scalable, and safe tiny-pointer abstractions; (2) to design and implement a high-performance library and OS module that expose these abstractions to both kernel and user-level applications; and (3) to demonstrate space and performance gains across a broad range of systems. Key applications include classical data structures (e.g., hash tables and trees), OS primitives such as page tables and page cache indexes, and flash translation layers (FTLs) in SSDs. For instance, by compressing pointers in x86-64 page tables, the project aims to increase fanout and reduce walk latency, potentially enabling a shift from 4-level to 3-level page tables. Similar benefits are expected for DRAM-resident mapping tables in SSDs. The project will also explore the impact of tiny pointers on systems such as IOMMUs, key-value stores, file systems, and large language models. 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 · 2025-08

The Northeast United States is struck with high-frequency and low-predictability frigid winter days, scorching heatwaves, and flash floods. This project aims to develop the simulation tools, partnerships, and collaboration networks necessary to create an incubator for Enhanced Geothermal Systems (EGS) for direct geothermal energy use – primarily towards district heating and cooling. With its infrastructure mostly deep underground and a local energy distribution network, EGS for direct heat use is not vulnerable to floods. In contrast to intermittent wind and solar energy, geothermal district heating and cooling are fully dispatchable as they do not require energy storage. Geothermally useful formation temperatures are regionally ubiquitous and accessible, making EGS technology a viable option to alleviate increasing demands on the electricity grid for heating and cooling. Indeed, EGS for direct district heating could satisfy heat demand of about 45 million U.S. households, and EGS-based resources are theoretically sufficient to heat every U.S. home and commercial building for at least 8,500 years. With input from various established partnerships, the models developed in this project will quantify the performance of the EGS technology to determine the engineering, environmental, and economic metrics needed for scalable deployment in the Northeast. A novel geothermal reservoir model will support a feasibility assessment for EGS district heating in the Northeast of the U.S., where subsurface temperatures are significantly lower than in the West at similar depths. Outcomes include models of highly anisotropic rock with fractures interacting at multiple scales; validated models of pressurized fracture propagation in the presence of natural discontinuities; and risk assessment for induced seismicity during reservoir stimulation. Additionally, energy recovery and thermal drawdown will be rigorously estimated for various underground infrastructure designs, integrated heat pump systems, and EGS operational conditions. Partnerships will be developed to model the economic potential of geothermal direct heating in the Northeast, incorporating the full costs and benefits of EGS in comparison to baseline heating sources. Compared to prior models, the proposed project will consider a larger area, higher resolution data, and updated assumptions regarding energy consumption and heating, natural gas and electricity prices, the cost of capital, and federal and state policy incentives. A transformative approach will be adopted to integrate estimates of the economic value of avoided externalities, and to develop levelized costs of heat directly produced by EGS. 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 · 2025-08

PROJECT SUMMARY Polycystic ovary syndrome (PCOS) affects 1 in 8 women of reproductive age imparting serious reproductive, cardiometabolic and psychosocial health complications for patients across the lifespan. Despite a high prevalence, under- and misdiagnosis of PCOS is common and contributes to significant delays in the delivery of evidence-based care. Lack of standardization in the clinical evaluation of PCOS features contributes to these delays in diagnosis. We have shown that ultrasonographic aspects of ovarian morphology have high diagnostic accuracy for PCOS and recently provided the new international standards for defining polycystic ovaries on ultrasonography. However, conventional assessments of ovarian morphology have moderate to poor inter-rater reliability which impacts their performance in clinical practice. Artificial intelligence provides an exciting opportunity to harmonize the ultrasonographic evaluation of PCOS. We propose that the use of deep- learning approaches for image classification, segmentation, and integration with clinical and biochemical markers can provide a framework to enable the timely detection and treatment of this highly prevalent condition. To that end, this project will develop a model for the automated characterization of polycystic ovarian morphology on ultrasonography (Aim 1), that can be leveraged alongside clinical and biochemical parameters to detect PCOS status (Aim 2). Our approach involves the use of a highly unique archive of ultrasonographic images and volumes of the ovaries garnered from well-characterized women across the reproductive spectrum, as well as external validation of the model for PCOS status using 2-dimensional and 3-dimensional views of ovarian morphology. The hypotheses to be tested are that a trained and externally validated deep learning model can detect follicle excess, ovarian enlargement and/or stromal aberrations that effectively define polycystic ovarian morphology, and that integration of one or more clinical or biochemical parameters yields a high performing model for the classification of PCOS status. By providing a model for the automated detection of PCOS, we expect to make important strides toward resolving subjectivity in the clinical evaluation of PCOS. Ultimately, standardization in the diagnostic assessment is needed to obviate delays in detection and facilitate access to treatment and prevention strategies that improve the overall health and well-being of patients living with PCOS.

NSF Awards · FY 2025 · 2025-08

Nontechnical description: This project will investigate defects in the semiconductor gallium nitride with an eye toward sensing applications. While gallium nitride is already an important semiconductor for power electronics used in chargers and electric cars, it was also recently discovered that it contains individual defects that behave like single atoms that are interesting for quantum technologies. These defects have a property called spin that allows them to sense the magnetic field in their environment. Because the optical response of the defects also depends on the spin, one can use light to measure magnetic field in a tiny volume. This attractive behavior motivates a deeper investigation into the spin and optical properties of these defects, and an effort to understand how they may be engineered. This project will also help to educate students on science and technology. This includes training for undergraduate and graduate students. For high school level students, the team will help students to understand what an Engineering Physicist does and how that can enable an advanced workforce. Technical description: This project will probe the optical and spin levels of isolated defects in gallium nitride, seeking to discover their nature as well as how they are coupled together to enable optical spin readout. These experiments will include time-resolved optical measurements that seek to understand the rate of excitation and relaxation between different states of the defect depending on spin. In this work the team will study defects individually, examining defect-to-defect variations and which models of the optical properties can explain those variations. Also using the spin as a coherent probe of its local environment, the research team will investigate the electronic and nuclear spins that are nearby and thus seek to establish the structure and nature of the defect. For this purpose, they will use optically detected spin resonance, adapting measurement protocols that were developed for nuclear magnetic resonance and electron spin resonance. The research team will simultaneously investigate the materials conditions necessary to create and engineer these defects. Working with collaborators that grow gallium nitride, the team will research the doping, crystal growth face, and substrate combinations that encourage the formation of these defects, with the goal of controlling their creation and concentration. This investigation will lay the scientific and technical foundation for a quantum sensing platform that can be monolithically integrated with gallium nitride electronics and optoelectronics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-08

PROJECT SUMMARY. Polycystic ovary syndrome (PCOS) affects 1 in 8 women of reproductive age imparting reproductive, cardiometabolic and psychological health complications for patients across the lifespan. PCOS is defined by the presence of 2 or more cardinal features including ovulatory dysfunction, hyperandrogenism and polycystic ovarian morphology (PCOM). Although PCOS manifests during adolescence, delays in diagnosis are common as the cardinal features are said to overlap with characteristics of the normal adolescent reproductive transition. Current standards to define PCOS in adolescence differ from those in adulthood – they do not include PCOM owing to a lack of data. This exclusion results in a narrow spectrum of PCOS during adolescence which may not fully capture all with PCOS until 8y post-menarche when adult-criteria for PCOM are available. Our preliminary data support that ovarian morphology has diagnostic accuracy for PCOS in adolescents and reflects the severity of reproductive and metabolic symptoms in a manner similar to adults. Further, in a longitudinal pilot evaluation of ovarian morphology in healthy adolescents, we show that ovarian markers in the first post-menarcheal year, including anti-Müllerian hormone, predict persistent menstrual cycle irregularity at 2y post-menarche suggesting that morphologic indicators precede in the timeline to aberrant reproductive maturation. We propose that an understanding of the natural history of PCOS during adolescence can provide a framework for the earliest detection and mitigation of this highly prevalent disorder. To that end, this project will define the trajectory of PCOS symptoms during early adolescence (Aim 1) and develop a nomogram of ovarian form and function during the first 8 gynecological years (Aim 2). Our approach involves elucidating the time course of divergences in ovarian morphology, androgen concentrations and menstrual cyclicity in adolescent cohorts who develop PCOS versus those who establish regular menstrual cycles in the first 3y post-menarche. Further, we will conduct a cross-sectional analysis of sonographic and serum markers of PCOM across gynecological ages <1y– 8y in order to define PCOM during this developmental period. The hypotheses to be tested are that changes in ovarian morphology that occur in association with the establishment of regular menstrual cycles differ from those seen in adolescents with persistent cycle irregularity, hyperandrogenism and/or PCOS, and that early and distinct aspects of ovarian morphology predict the likelihood of PCOS in early gynecological life. By defining the normal limits of ovarian form and function during adolescence, we also expect to generate developmental-stage specific criteria for PCOM that reveal the actual clinical spectrum of PCOS during this life stage. This research will immediately improve diagnostic experiences for patients and families by resolving the ambiguity related to the role of ovarian morphology in the early detection of PCOS. Likewise, a new understanding of the emergence of PCOS in adolescence will usher opportunities for the timely intervention, and possible prevention, of this major problem in women’s health.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Microbes play crucial roles in human health. They contribute as pathogens, as sources of enzymes and bioactive compounds, and through their roles in the communities that make up the microbiome which impacts numerous diseases and physiological processes. Cultivating microbes in isolation is a key step for understanding their biology and utilizing their products. Culture allows controlled manipulations, genetic perturbations, analysis of metabolites, and testing interactions with the host and other microbes. However, it is currently difficult to establish culture conditions for uncultured microbes, and many important microbes have not yet been cultured in isolation. Recent advances in the field have made this challenge more tractable, and here we propose to unlock the study and utilization of uncultured microbes by establishing their growth conditions. This work builds on the PIs background in microbiology and microfluidics, especially utilizing two recent contributions: the “SPOTs” liquid handling platform, which allows high-throughput, high-performance liquid handling with unprecedented ease; and a scalable pipeline for 16S genotyping which allows cost effective parallel processing of samples for high- throughput sequencing. This work will be focused on two related projects. In the first project we seek to establish the first axenic culture of the intracellular bacteria Wolbachia, from order Rickettsiales. Wolbachia is prevalent in insects and nematodes playing a key role in and filarial diseases and the spread of viral diseases through insect vectors. To culture Wolbachia, we will generate many thousands of diverse media compositions using the SPOTs platform then monitor cell metabolic activity with redox active dyes and cell division with microscopy to determine which conditions are most favorable. Iteratively we incorporate data from each experimental cycle to refine the media compositions until they can support growth. Axenic Wolbachia growth would be a major advance that may also unlock culture of additional organisms from Rickettsiales, of which there are dozens of important uncultured pathogens. In the second project we will utilize a “many-by-many” strategy, generating many diverse medias, using SPOTs, to simultaneously grow many different microbes from different starting communities including fecal and soil samples. Growth will be monitored by microscopy and which organisms grow in which media compositions will be determined using our high throughput 16S genotyping method. We expect that this approach will culture a wide range of yet-uncultured organisms, enabling their characterization and use and providing a rich dataset linking different microbes to the conditions in which they grow. The impacts of this work include catalyzing the study and use of new microbes by determining their culture conditions and contributing a powerful new approach for addressing combinatorial questions in biomedicine.

NIH Research Projects · FY 2026 · 2025-07

Project Summary/Abstract Mental health disorders associated with memory deficits have been on the rise for a few decades, highlighting the need for understanding the brain mechanisms of memory from a systems neuroscience point of view. One of the outstanding problems regarding the mechanisms of memory is how the brain can continuously incorporate new memories while leaving recently acquired knowledge intact. The current accepted model sets that recently acquired memories are reactivated in the hippocampus during sleep, an initial step for memory consolidation, during sharp-wave ripples (SWRs), hippocampal oscillations important for memory and planning. Yet, this process is concomitant with the hippocampal reactivation of prior memories, posing the problem of how to prevent interference between older and recent, initially labile, memory traces. Theoretical work has suggested that consolidating multiple memories while minimizing interference can be achieved by randomly interleaving their reactivation. Alternatively, a temporal micro-structure that promotes the reactivation of different types of memories during specific substates during the overall course of the sleep could exist. Indeed, work in humans suggests that non-rapid eye (NREM) movement and rapid-eye movement (REM) states can be further subdivided into substages that differently correlate with memory processes. To understand this, we propose to simultaneously monitor memory reactivation (using electrophysiology) in combination with brain states and substates (using pupillometry) in naturally sleeping mice after performing a hippocampal dependent memory task. Our preliminary data show that we can successfully record pupillometry and electrophysiology in naturally sleeping mice and distinguish both NREM and REM phases of sleep. Furthermore, our preliminary data suggest that pupil fluctuations can reveal a previously unknown micro-structure of non-REM sleep and associated memory processes, such as memory replay. Specifically, our preliminary data show that memory replay of recent experiences dominated in sharp-wave ripples (SWRs) during contracted pupil substates of non-REM sleep, while replay of prior memories preferentially occurred during dilated pupil substates. Initial experiments show that closed-loop disruption of SWRs during contracted pupil non-REM sleep impaired the recall of recent memories, while the same manipulation during dilated pupil substates had no behavioral effect. We will investigate what are the underlying mechanisms of the distinct pupil associated memory processes during NREM sleep. If successful, the results of this proposal will solve a long-standing question in traditional neuroscience of learning and memory: how the brain can multiplex distinct cognitive processes during sleep to facilitate continuous learning without interference. Applying the concept of isolating pupil-guided cognitive substates to other domains (such as learning or attention) can offer novel insights into our current understanding of cognition. Importantly, using this methodology during pathological conditions can inform current diagnosis pipelines of different mental health conditions associated with memory deficits.

NSF Awards · FY 2025 · 2025-07

Quantum computers and simulators offer a powerful new approach to exploring the behavior of complex physical systems, from exotic materials to fundamental interactions in nature. However, many of the most important models in physics cannot be studied with today's quantum hardware, which is limited in the types of interactions it can natively implement. This project addresses that limitation by developing a new method that combines two core modes of quantum computing: digital gate operations and analog evolution, into a hybrid approach within a single experiment. By enabling new classes of interactions that are currently inaccessible, this work expands the scientific reach of quantum simulators and enhances their ability to address open questions in condensed matter and high-energy physics. The project also contributes to the progress of science by training graduate and undergraduate students in advanced quantum technologies, helping build a skilled workforce aligned with national priorities in quantum science and engineering. Technically, the project seeks to realize effective quantum Hamiltonians involving higher-order spin interactions by interleaving a relatively small number of entangling digital gates with analog Hamiltonian evolution. This hybrid digital-analog technique avoids the approximation errors that limit purely digital simulations and may significantly broaden the class of quantum models that can be implemented experimentally. Using trapped-ion quantum processors, the research will demonstrate programmable many-body Hamiltonians containing three- and four-body interaction terms. A complementary theoretical effort will identify the full range of Hamiltonians accessible with this approach and assess its applicability to other quantum computing platforms. By extending the boundaries of what can be simulated with quantum hardware, this project lays the foundation for more powerful and versatile quantum simulators capable of addressing fundamental challenges across multiple areas of physics. 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 · 2025-07

Machine learning (ML) has transformed how we solve complex problems, from understanding languages to making accurate predictions in medicine and economics. However, modern ML models have grown extremely large—often involving trillions of parameters—that they can no longer run efficiently on a single computer. Instead, these enormous models must be distributed across many powerful processors, known as accelerators, in data centers. A critical challenge in running distributed ML models efficiently is managing the communication between accelerators. When different accelerators share information, this process — called collective communication — becomes a bottleneck, slowing down training and inference tasks. Current approaches to managing communication assume all connections between accelerators are equal. But in reality, connections can vary widely in speed and capacity, creating inefficiencies. This project aims to significantly improve collective communication by creating software tools and algorithms specifically designed for the diverse connections found in modern cloud-based accelerator systems. First, the project will measure how communication speeds and delays vary between accelerators, accounting for complexities like proprietary technologies and hidden network paths within data centers. Next, these measurements will be used to automatically generate optimized collective communication strategies tailored to specific cloud setups. This approach ensures that each deployment — whether it involves multiple servers within one data center, or servers spread across multiple data centers — benefits from customized, efficient collective communication. The project will have broader impacts beyond just technical improvements. Accelerating ML processing helps reduce the energy consumption and operational costs associated with data centers. The project will also make powerful ML tools more accessible for classroom education and engage with high school students through educational workshops to foster interest in science and technology. Releasing software tools, datasets, and findings openly will drive widespread adoption and enable the broader community to benefit from more efficient ML technologies, benefiting various fields like management and economics. All research artifacts developed through this project will be made publicly available via a dedicated project website at www.cccl.network. The website will be actively maintained throughout the project duration and archived for continued access beyond the project’s conclusion. 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 · 2025-07

Randomness is a fundamental resource in modern computation, with critical applications in securing internet communication, executing machine learning algorithms, and simulating complex systems such as financial markets and physical processes. However, high-quality random bits are difficult to obtain from natural sources. The area of randomness extraction is a systematic study of defective sources of randomness found in nature and devising efficient algorithms that provably generate high-quality randomness that are critical for above mentioned applications. Pushing the frontiers of this line of research is the main goal of this proposal. The outcomes of this work will improve the security of everyday online systems, enhance the performance of randomized algorithms, and support more accurate simulations across science and engineering. The outcomes of this work will support secure digital infrastructure, advance science and technology, and enhance economic resilience. The field of randomness extraction has seen major success over the past decade, with powerful constructions and deep connections across theoretical computer science. This project builds on that foundation by exploring new models of defective randomness sources that go beyond the traditional assumption of independence. These include sources with correlations, dependencies, or structural limitations that naturally arise in practice. The project investigates efficient algorithms that can extract or condense randomness in these more realistic scenarios, with applications in areas such as leakage-resilient cryptography, fault-tolerant distributed computing, communication complexity, and circuit lower bounds. A complementary goal is to construct extractors with extremely low error rates, a key requirement for cryptographic applications, which remains a central open problem despite substantial progress. Together, these directions represent key directions in advancing both the theory and practical relevance of randomness extraction. 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 · 2025-07

Anosov flows are an important and fundamental class of dynamical systems. They give mathematical models for chaotic motion (sensitivity of outcomes or trajectories to initial conditions), but at the same time exhibit robust global behavior. This confluence of chaos and stability makes them particularly interesting for study and an important mathematical entry-point to understanding the theory behind such physical phenomena. This project will describe and classify all the Anosov flows on three-dimensional spaces, a problem which has been open and of interest since the 1960s. There are already many known examples in the three dimensional space, and the project will show how the dynamics of these various examples of flows are related to geometrical features of the space. Broader impacts include mentorship and training of early career researchers, building new bridges between different areas of mathematics, such as geometry, topology and dynamics, and a workshop to improve communication by mathematicians. The technical goals of this project focus on constructing a dictionary relating the topology and geometry of the foliations associated to an Anosov (or pseudo-Anosov) flow on a 3-manifold with the structure of the 3-manifold and the dynamics of the flow. This builds on previous work of the PI, which used a dimension-reduction technique to translate the classification problem for flows into one for "Anosov-like group actions" on the plane, and then translate results back from the plane to the 3-manifold. The project will carry out a detailed study of such actions to produce new invariants for classification and solve open conjectures such as the finiteness problem. In doing so, the project will also deepen our understanding of the relationship between actions of discrete groups on the plane and the circle, and continuous flows on 3-manifolds, providing new links between dynamics and low-dimensional topology. 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 · 2025-07

This I-Corps project focuses on the development of multiple-seed pellets that can be used to plant a wide range of plant species. The plants include many species that attract and foster beneficial insects including pollinators (bees), predators of insect pests (ladybugs), and threatened species (monarch butterflies). There are currently not enough beneficial plants in and around crop fields to support full pollination and suppression of insect pest outbreaks. Some key plants like milkweed have become so scarce that insects that depend on them are now threatened and require restrictions on key pest management practices. Restoring these plants benefits individual farmers and society by keeping the food and fiber supply secure and available at a reasonable price. Many of the most beneficial plants grow vigorously and are perennial so that once planted these beneficial plants can persist for several years. The challenge is that the seeds of the plants span a wide range of sizes and shapes. Several surveys have confirmed that farmers are aware of the benefits of establishing these beneficial plants, but the lack of an affordable, effective planting method is too great of a barrier to overcome. Multi-seed pellets that are the same size, shape, and weight as standard crop seeds (e.g., corn, soy, cotton) will be commercialized. These can be easily planted using the standard crop planting equipment that farmers already own, to establish a custom set of beneficial plants rapidly and reliably. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of technology that allows the planting of almost any plant seed, regardless of size, shape, and weight, with commonly available, large-scale planting equipment that is designed to plant only seeds that fall within a very narrow range of physical characteristics. These multi-seed pellets integrate discoveries involving seed preparation, pelleting materials, and high-volume pellet production. The U.S. Department of Agriculture recognizes the importance of incorporating diversity through the targeted reintroduction of plant species beyond the relatively small number that are grown as crops. What is new is a planting technology that outperforms the most common current methods of hand broadcast or specialized seed drills (not commonly available). Surveys indicate that most farmers would establish conservation habitats if they could be planted with equipment they already use, even without compensation. 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 · 2025-07

The 140,000 small public water systems across the U.S. face major challenges in maintaining or gaining access to safe drinking water. These include escalating construction costs, limited access to federal funding, diseconomies of scale, and turbid source waters due to more frequent storms. Exacerbating these hurdles is the lack of affordable drinking water treatment options. Small public water systems need access to technology that makes it easier and less expensive to treat water, financing that eases the burden of capital costs, and capacity-building to ensure reliable delivery of safe drinking water. VersaWater - a collaboration of researchers and practioners - offers a community-centered approach for water treatment that will increase the number of Americans with access to safe clean drinking water. Specifically, VersaWater offers a community-centered approach for the implementation of reliable and safe water treatment. These systems are electricity-free prefabricated water treatment plants (WTPs), designed to reliably treat turbid waters at a lower cost using gravity power alone. These systems have elegantly simple flow control, chemical dosing, self-cleaning features and easy to replace parts. VersaWater's implementation process includes support for the community in accessing federal funds, regulatory compliance, and hands-on learning for system's operators and managers. VersaWater does the behind-the-scenes work of converging the right partners to accompany small communities on the path to achieving safe water access, providing what they need through one organization (a "utility-in-a-box"). As a result, Versawater is addressing a public health gap and critical water security issues in the United States. The project advances scientific knowledge in water treatment while supporting sustainable and resilient water infrastructure. By combining cutting-edge research with a comprehensive, participatory implementation process, VersaWater sets a standard for addressing global water access challenges effectively and equitably. The VersaWater team has developed an implementation framework for VersaWater's prefabrication technology, which will be refined through a piloting process in Phase 2. VersaWater's next phase of work is organized into four core components structured as a series of pilots. Each component is designed to progressively increase community involvement and external investment. Workforce development will occur concurrently through several training opportunities. A Demonstration Plant serving 250 people (PF250) will be installed at an educational institution for learning and training. The PF250 produces 18,000 gallons of drinking water per day complete with an automated coagulant doser to enable operators to spend less time on-site and decrease operation costs. A Cohort 0 will be comprised of two small water systems in Puerto Rico (PR). PR small water systems experience disproportionately high poverty rates and challenging treatment conditions caused by climate-related storms. These installs will be accompanied by simulated applications for funding to develop capacity. Cohort 1 will be comprised of PR and rural Northeastern U.S. small water systems. Communities will be guided through submissions of applications for shovel-ready implementation projects to federal funders. After this Cohort, VersaWater plans to be in operation mode and expanding its "utility in a box" implementations year after 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 · 2025-07

The investigators will explore how meltwater from surface channels called moulins (holes in the ice that funnel water downward) affects the hidden drainage system beneath Greenland’s glaciers. They will conduct a field study to see how water connects isolated pockets of space at the glacier’s base with larger drainage networks and how these connections influence seasonal changes in ice speed. The team will use tools like seismic sensors, radar, and measurements of ice movement to track how these systems evolve together. By observing changes in the number, size, and location of these underground cavities over time - linked to how much meltwater flows in - they will determine how the hydrological connections between these spaces affect how fast the ice is moving. This research builds on earlier discoveries showing that Greenland’s summer slowdowns (when glaciers move less) happen not because water channels grow larger, as seen in mountain glaciers like those in the Alps, but because isolated pockets under the ice merge into bigger drainage pathways. The study will focus on western Greenland’s Paakitsoq region. The investigators will create a Virtual Reality (VR) module showcasing fieldwork on the Greenland Ice Sheet in partnership with the Museum of the Earth (Ithaca, NY) and the Kangiata Illorsua-Icefjord Center (Ilulissat, Greenland). The investigators will conduct a field campaign focused on understanding how hydraulic connections between isolated cavities at the bed surface of the Greenland Ice Sheet and the broader distributed subglacial drainage system evolve, and how this "connectivity" affects the seasonal changes in ice velocity. The team will integrate ice dynamic, hydrologic, and geophysical (seismic and radar) methods to monitor the co-evolution of moulin-connected subglacial channels, well-connected regions of the distributed system, and hydraulically isolated bed cavities. By quantifying changes in cavity number, size, and spatial distribution over time - linked to observed meltwater inputs - the researchers will assess how bed cavity connectivity modulates ice motion. This work aligns with findings from the Greenland Ice Sheet (GrIS) observations, which show that summer slowdowns occur not due to conduit expansion (as seen on Alpine glaciers) but through increased connectivity within the distributed system as isolated bed cavities integrate into larger drainage pathways. The field campaign will focus on the Paakitsoq region of western GrIS, where supraglacial meltwater inputs are monitored to trace their subglacial impacts. Understanding these processes is critical for predicting how future meltwater increases will influence GrIS mass loss, particularly as seasonal connectivity changes modulate ice flow and stability. 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 · 2025-07

Systems based on modern large language models (LLMs) play an increasing role in how users access information and compose text. For instance, a user executing a web search will increasingly rely on LLMs to summarize their search results, rather than viewing individual web pages, and they might use LLMs to “talk to” long documents like financial reports, rather than reading them in their entirety. To support these new paradigms, it is important that an LLM be able to generate responses that are factual, informative and safe. However, satisfying these criteria is not sufficient: a response should also be at the right level of abstraction or detail, in the right format, creative where appropriate, and aligned with other user needs. Current practice has neglected evaluation of these more subtle factors. This project proposes to address these shortcomings by identifying a set of “evaluation concepts” to indicate the kinds of areas where LLMs are failing, like “lack of detail in a list.” The project will then develop technology for automatically evaluating and improving LLM responses according to these concepts. This project aims to improve the evaluation and the functionality of LLMs in two ways. First, the project will discover a concept taxonomy and learn how to evaluate LLM responses according to the concepts in that taxonomy. This process will necessitate advances in reward models, which are themselves LLM, customized to reliably score responses. Second, these reward models are applied to actually improve the LLMs’ responses. Specifically, the project will curate training data exhibiting the correct kinds of behavior for each concept, enabling training of LLMs that do better on those concepts. Finally, the project will develop methods for iteratively improving responses using our reward models. The project will open-source the concept taxonomy and reward models that will outperform closed-source, proprietary models. These models will enable the public to have a better sense of the performance of LLM systems across a variety of applications, and will drive the open-source community to build stronger, more reliable LLM 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 2025 · 2025-07

Image-based computational models of the cardiovascular system play an increasingly important role in advancing the fundamental understanding of cardiovascular physiology and supporting clinical diagnosis and treatment planning. However, traditional models are primarily based on well-posed physics that are solved numerically, and their reliability is limited because of unknown or uncertain modeling conditions. On the other hand, sparse and noisy data have become increasingly available thanks to the rapid development of medical imaging techniques (e.g., flow MR images), which can be utilized for model inference and uncertainty reduction. Hence, forward uncertainty quantification and inverse data assimilation in cardiovascular simulations are of paramount importance to enhancing predictive confidence and prompting clinical translation efforts. This project will develop computational cyberinfrastructure for data-enabled forward and inverse stochastic cardiovascular modeling by leveraging recent advances in scientific machine learning. The project aims to establish a novel paradigm of data-augmented cardiovascular fluid-structure simulations, which could help transform personalized cardiovascular diagnostics/therapeutics, leading to higher quality of life. Moreover, this research program will also try to address long-standing challenges in effectively engaging students in STEM education across K-12, undergraduate, and graduate education by promoting an interactive and inclusive learning strategy. In particular, the PI will (1) design pedagogical software using physics-informed transfer learning for rapid interactive fluid simulation based on hand-drawn sketches; (2) develop new modules on Artificial Intelligence & Mechanics for U.S. Department of Education TRiO programs to engage K-12 students from low-income families in emerging interdisciplinary STEM fields. The overarching goal of this CAREER program is to pioneer a scalable and transformative computational cyberinfrastructure for forward and inverse uncertainty quantification (UQ) of cardiovascular modeling based on physics-informed Bayesian geometric deep learning, leveraging physics/physiological knowledge to enable efficient probabilistic learning with sparse and noisy data. This project tackles the fundamental challenges faced by the traditional paradigm of modeling cardiovascular fluid-structure interaction (FSI) dynamics. In the proposed framework, geometric deep learning models will be constructed based on both (partially) known physics and sparse measurement data in a Bayesian manner, enabling efficient forward and inverse FSI simulations with quantified uncertainties. Specifically, the PI will (1) formulate a variational PDE-informed, discretization-based learning framework using graph convolutional networks and use a reduced basis to constrain the dimension of the solution space, facilitating network training; (2) enable high-dimensional UQ capability of the proposed learning framework based on scalable variational Bayesian inference; (3) establish a multi-fidelity meta-learning strategy to parameterize solutions in the physical parameter space for rapid surrogate modeling, on the path to real-time cardiovascular simulations. The fast inference speed, strong expressibility, and GPU parallelization of deep learning models will be exploited to enable large-scale stochastic FSI simulations with patient-specific geometries. This project will build a solid foundation for developing the next-generation computational cyberinfrastructure of cardiovascular FSI modeling. 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 · 2025-07

This project supports a critical step toward realizing practical, large-scale quantum computers, machines that promise transformative advances in national cybersecurity, economic competitiveness, and scientific discovery. However, existing physical components are too error-prone to construct a useful quantum computer. The PI and research team aim to adopt an emerging physical approach for storing and manipulating quantum information called “bosonic encodings”. This approach enjoys a high degree of efficiency in the number of resources necessary to protect against errors in stored information. However, the available quantum logic operations do not retain the protection of bosonic encodings. Therefore, our research will develop new strategies for quantum logic operations that preserve the built-in error protection of bosonic encodings. By conducting such research, the project will take a major step toward scalable quantum computers. The project serves the national interest by promoting the progress of science through research that pushes the boundaries of quantum control and error correction, which are critical building blocks for quantum computers. Bosonic encodings of logical quantum information offer hardware-efficient approaches to quantum error correction. This has the potential to reduce the resource-overheads necessary to achieve a useful quantum computer. However, missing from this approach is a complete set of logical gates to enable active manipulation of stored information while maintaining the full error correction properties of the encoding. With a combined theoretical, computational, and experimental effort, the team proposes to advance quantum control methods so that this becomes possible. Specifically, the research team will aim to advance our novel concept of photon number parity-nested control operations on a binomial encoding by taking advantage of optimal-control derived squeezing-based operations of an oscillator. The research team will complete analytic and numerically-optimized gate sets for such squeezing-based operations and accomplish experiments that accomplish error-transparent magnitude-mixing operations on a bosonic encoding in superconducting hardware. Additionally, the PI will host a workshop for novel circuit elements for quantum information devices to engage students and faculty from a broad range of universities. 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.