Massachusetts Institute Of Technology

NSF Awards · FY 2024 · 2024-09

In the data-driven world that we live in, sharing digital information is a key component underpinning a vast body of technologies. Low-latency, high fidelity access to information is central to algorithms that impact how we work, are entertained, how we travel, and our healthcare. Systems that, in particular, rely on wireless communication to deliver their services have become ubiquitous. With the increase in data transmitted over the air, however, the central resource that they depend on, spectrum, can become congested with multiple communications overlapping and negatively impacting each other. This project brings together diverse researchers from Northeastern University (NU), the Massachusetts Institute of Technology (MIT) and Boston University (BU) to develop methods that improve communication performance in shared, congested, and contested spectrum bands. The presence of interference in communications detrimentally impacts the throughput and reliability of systems. Interference and noise are often used interchangeably as they are commonly lumped together as general deleterious effects that corrupt communications. Interference, however, has a more structured form than noise. Central to this project is developing new means to leverage that structure to improve communication systems. By enabling more efficient use of scarce resources, more services can reliably co-exist, advancing national health, prosperity and welfare. By developing techniques that are receiver-only, it allows both backward compatibility and graceful adoption paths. Interference management motivates substantial engineering effort at all levels, from hardware design, to signal processing, to error correction, to retransmission, and resource allocation protocols. A traditional approach to managing interference is to consider its impact as being part of noise. This project aims to do more, leveraging the structure of interference to improve performance through receiver-side approaches only, thus circumventing barriers to technological adoption. When a modulated communications signal experiences interference that arises from other modulated communications, those characteristics can be taken into account. Even when an interferers' modulation may not be discerned, the interference can influence the noise experienced by a receiver in semi-predictable ways that can be exploited by a receiver. When interference is due to the presence of other communication systems where individual interferers' modulation can be detected but the signal not decoded, unlike in a multiple user system, this project proposes an approach that takes into account both noise and the restricted forms the interference can take. When channel and modulation may not be available at the receiver, interference will still have characteristics that are different from, e.g., Gaussian noise. The statistical characteristics of such interference can be used to improve forward error correction decoding, enabling reliable communication with less overhead, which this project explores. When interference is due to signals that vary more slowly than the communication, such as from electronic devices, the receiver cannot rely on knowledge of the structure of the interference, other than the fact that it will exhibit a slowly varying profile. In that case, this project aims to discover post-decoding the interference experienced by some signals and use it as a starting point to remove pre-emptively at least partially that interference from other signals that are proximate in time, and thus subject to a similar interference. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The composition of ecological communities (group of interacting populations in a given place and time) exhibits feedbacks with the physical and chemical environment, reinforcing or weakening the planetary life-support systems. These feedbacks are expected to be regulated by the availability of energy in the environment and both the capacity and efficiency of populations to use such energy. Thus, understanding the feasible limits of energy expenditures in ecological communities becomes instrumental to explain and predict the possibility of observing a community in nature, the response of such communities to random external perturbations, as well as changes in community composition and environmental conditions. Using mathematical models, this project will set out to answer how ecological and evolutionary processes drive energy limits (energy intake, energy requirements, productivity, efficiency, storage, distribution, and tolerance to random changes in the amount and rate of energy supply) in sustainable and unsustainable ecological communities. This knowledge is paramount to establish successful interventions in the conservation and restoration of ecosystems. This project will also provide training to graduate students. Organisms are not isolated units, but they form ecological communities, affecting the availability of useful energy and resources. At this other level of living matter organization, it is unclear whether the energy limits operating at the organismal level are conserved at the community level and lead to feasible ecological communities, or whether feasible conditions at the community level constrain organismal energy limits. While metabolic scaling theory has provided theoretical and empirical platforms to study energy limits at the organismal level, it remains unclear what are the feasible energy limits in ecological communities leading to sustainable systems. From an evolutionary point of view, efforts have concentrated on understanding the role of trait evolution (e.g., body mass, life span) on community-wide properties (e.g., emergence of communities through branching events and productivity). In fact, it has been shown that co-evolution cannot continuously increase feasibility; on the contrary, it will be limited by limits in energy intake. Moreover, this work points to unknown trade-offs operating at the community level, as well as feasible energy limits that should be expected to be different from a scaled-up version of the organismal/population limits. For example, under which contexts co-evolutionary pressures at the organismal level can increase/decrease the feasible limits of energy expenditures in communities? The goal of this project is to provide a testable theoretical platform to understand how fundamental ecological and evolutionary processes drive the limits of energy expenditures in feasible communities. This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The brain is the most prominent biological computer. As a result, efforts to develop biocomputing systems have focused on using neurons as their basic building block. That approach misses the fact that every organ in the body continuously senses and responds to a variety of inputs. These inputs can be a combination of electrical, chemical, mechanical or optical. These organs make decisions (i.e. compute responses) based on those inputs. With that in mind, the project team will attempt to develop intelligent organ-like masses of cells (organoids) based on liver cells. The hope is that understanding how to develop functional intelligence in this system will make it possible to do so using any type of cells. The notion of creating intelligent biological systems gives rise to many ethical questions. Questions related to whether such systems could evolve a form of consciousness will be addressed. Focused participation in an annual Science Olympiad for middle school students will be a primary effort to broaden the participation of students in hands-on science and engineering. It is expected that this will lead to greater involvement of those students in STEM careers at all levels. PROGENIC enables an intelligent liver organoid to learn and adapt to environmental conditions like viral infections and toxins. Engineered cells in the organoid are equipped with genetically encoded artificial neuronal networks that operate using DNA, RNA, and proteins. These cells are designed to learn new functions by modifying the genetically-encoded weights within their artificial neural networks. This is implemented using on-demand DNA modifications to genetic circuits in the chromosomes of living cells and is induced by communication from designer guide cells carried to various locations in the 3D organoids by magnetically controlled microrobots. Assessment of these new neuronal networks is performed by exposing the organoids to variable environmental conditions and perturbations, including infections and toxins. After these perturbations/insults, intelligence is evaluated by quantifying organoid function (e.g. albumin and urea secretion). The project is structured into three aims. Aim 1 engineers liver cells to harbor multi-node artificial neuronal networks within each cell, using synthetic gene circuits that operate with DNA, RNA, and proteins. Aim 2 utilizes microscale robots to facilitate communication with cells in organoids that change neural circuit weights and enable learning. Aim 3 creates an intelligent liver organoid that modifies organoid structure and function and evaluates the performance of the biocomputing system. In addition, neuroethical issues, such as the possibility of consciousness and sentience arising in the de novo neuronal network, are also explored. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This research program will deepen our understanding of the final stages of stellar evolution, specifically focusing on red giants, which play a crucial role in enriching our Galaxy with elements essential for forming new stars and planets. The investigators will study how these dying stars shed mass through molecular "masers”. Masers are the radio wavelength equivalent of lasers, and they arise from molecules in the atmospheres of some red giants owing to the specific combination of gas density, temperature, and velocity. The presence of masers provides a unique tool to study the gas motions and physical conditions within these stars. This investigation will employ the Very Long Baseline Interferometry (VLBI), extending observations to wavelengths as short as 1 millimeter. This research program will provide training opportunities for undergraduate students and a postdoctoral researcher, fostering the next generation of scientific talent. Additionally, collaboration with science educators will produce accessible podcast episodes that communicate project findings to the public, enhancing scientific literacy and engagement. The project addresses critical gaps in our understanding of late-stage stellar mass loss, particularly focusing on the mechanisms driving winds from red supergiants and asymptotic giant branch stars. These winds are major contributors to the enrichment of galaxies with heavy elements and dust. The study will utilize VLBI observations across multiple frequencies, including unprecedented measurements above 200 GHz, enabling spatial resolutions as fine as 10–500 microarcseconds. This high resolution is essential for mapping the regions where stellar winds are launched, particularly using SiO maser lines. Methodologically, the project will develop advanced data processing techniques and calibration methods tailored for high-frequency VLBI observations. These innovations will be shared with the wider scientific community, enhancing the capability to study complex astrophysical phenomena. Coupled with ongoing research on evolved star atmospheres, this effort promises to deliver some of the most comprehensive views to date of red giant dynamics. In addition to its scientific contributions, the project will produce two podcast episodes aimed at broadening public understanding of stellar evolution and its implications. These episodes will feature project updates and interviews with team members, contributing to public engagement with astronomy and astrophysics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The ability to place atoms at chosen locations to reproducibly create millions of identical arrangements would greatly benefit quantum science and engineering. A goal of particular importance is the creation of atomic arrangements that interact with light, since absorbing and emitting photons is a way of performing the operations that take place within quantum computers and other quantum devices. This proposal aims to move precisely chosen groups of atoms within a crystal using a finely focused electron beam in an electron microscope, and measure the effect this has on the interaction of the crystal with light. To ensure flawless positioning of each atom the proposal will develop autonomous operation of the electron microscope, and the response to light will be measured by building a miniature optical laboratory inside the microscope. The outcome of this work will be the construction of complex arrays of perfectly ordered atoms and the demonstration of their promise in quantum photonic devices. The research activities will be integrated with education, outreach, and community engagement by developing a microscopy-based initiative, Extreme Imaging, which aims to achieve societal impact by engaging high school students and their teachers through technical imaging. Scalability is a challenge in realizing quantum technologies, due to the lack of reproducibility in engineering identical quantum wave functions. In particular, the deterministic and atomically precise generation, placement, and integration of large numbers of identical quantum defects in solid-state materials is not possible with current techniques, even though this is crucial for the construction of large-scale and highly integrated photonic quantum systems. This proposal aims to overcome this scalability barrier by opening a new pathway towards atomic level design of quantum devices. The proposal will construct an “autonomous quantum microscope” using concepts from quantum photonics, in situ transmission electron microscopy, and artificial intelligence, enabling the engineering of patterns of identical quantum defects in materials while simultaneously measuring their optical properties. Millions of identical quantum defects will be integrated into predefined photonic circuitry with a degree of precision that enables the fabrication of arbitrary arrays. The proposal ultimately aims to develop an autonomous process by which vast entangled photonic states can be created and measured. The researchers trained in this project will address an area of national need in advanced instrumentation development and quantum information science. This research will also be coupled with educational outreach activities, also based on microscopy and imaging, to inspire and prepare the next generation of experts in technical design and microscopy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

With the support of the Chemical Synthesis program in the Division of Chemistry, Professor Daniel Suess of the Massachusetts Institute of Technology will study how to efficiently convert atmospheric nitrogen into ammonia and other substances that can be used as fertilizer. Currently, this is done using the Haber-Bosch process that is both energy intensive and generates about 2-3% of the annual carbon dioxide emissions world wide. In contrast, naturally occurring enzymes, called nitrogenases, can perform this goal at room temperature without the formation of carbon dioxide. This project will utilize the knowledge of these enzymes to design small molecules that exhibit nitrogenase activity. This will reveal the fundamental chemical bonding that underlies efficient nitrogen activation and provide a blueprint for the design of catalysts for nitrogen fixation. The research will enhance our understanding of the natural world, particularly the roles of nitrogenases in the global nitrogen cycle and our ability to provide the fertilizer needed to support food productions. In educational and outreach activities, the Suess lab will host local high school students as interns, engaging them in the research activities and more generally in the process of scientific discovery; and mentor elementary-school girls, helping to cultivate their interest in STEM disciplines. The project will utilize synthetic chemistry to understand why the Mo, V, and Fe-only nitrogenases show different proclivities for productive dinitrogen reduction. Specifically, the bonding in synthetically versatile, cubane-type [MFe3S4] model clusters and electronically simplified variants thereof that are more tractable for analyzing M–Fe bonding will be studied. An objective is to determine how the composition of the cluster affects the strength of the M–Fe bonding and how this in turn impacts the cluster’s ability to bind and activate dinitrogen and related substrates; and help rationalize why metal ion doping in heterogeneous catalysts often improves dinitrogen reduction catalysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

As human-emitted greenhouse gas pollution warms the planet and changes the dynamics of the climate system, losses due to weather extremes and their impacts on human life and property has become a significant and costly challenge. Reliance on historical records with outdated climate states, coarse model resolution, and incongruency between the spatiotemporal scale of impacts exacerbates the problem and presents serious difficulties for the insurance and finance sectors that rely on accurate assessment of natural perils and the corresponding uncertainties around their frequency, intensity, and duration. This knowledge is required to cover climate disaster related losses that, annually, reach well into tens to hundreds of billions of dollars. To address this challenge, three institutions: George Mason University, the Massachusetts Institute of Technology, and the City University of New York have come together to plan an industry-university cooperative research center that addresses the critical, high priority needs of the insurance and finance sectors, both of which are wrestling with uncertainties in assessing risks and damages due to climate-related disasters. Center research thrusts include: (1) improving climate predictions at spatiotemporal scales needed by the insurance and finance industries; (2) modeling the catastrophic impacts of natural perils to critical infrastructure systems; and (3) quantifying how the local environment modifies the frequency, intensity, and impact of weather-related natural perils on people and property. Broader impacts of the Center would include increased national economic stability by providing better and more reliable tools for assessing climate risk; training the next generation of climate science, engineering, and policy professionals able to tackle the challenges that a changing climate poses to the nation; and broadening the diversity of underrepresented groups in climate disaster modeling field. Research conducted by the Center for Climate Risk Applications, now in the planning phase, will focus on addressing existing gaps on the impact of climate change on a range of natural perils by analyzing state-of-the-art climate model ensembles, improving existing models, and advancing the science of integration between climate modeling and asset-scale risks. Research will analyze and improve the output of climate models at the actionable spatial and temporal scales required by the insurance and finance sectors of the economy. The Center will also develop new methods for downscaling hazard information to asset-scale granularity, while quantifying uncertainties of year-to-decadal climate predictions. Additional work will address the sensitivity of interconnected infrastructure systems to a changing landscape of natural perils and the potential for disruption of critical services and supply/value chains. Natural disasters impact people, not just infrastructure; thus, the Center, presently in the planning stage will also focus on how public policy and regulation impacts the insurance of properties, as well as how existing frameworks for decision-making around these perils inform resilience efforts in the private and public sectors. The Center's education and outreach activities will help enable and maintain healthy insurance and reinsurance markets to promote economic stability and growth in the face of severe threats from climate change to life and property. It will also develop a diverse, knowledgeable, and capable workforce necessary to quantify risks of climate change for those owning assets that need protection as well as the need to improve their ability to understand and predict risks and create policies, standards, and incentives that reduce the risks of loss due to climate change. The role of the Massachusetts Institute of Technology's role will be the contribution of multi-sector and cascading disaster dynamics, policy, and climate science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

By the end of this century, sea level is expected to rise by one meter due to climate change and inundate coastal areas that are home to millions of people in the United States. As sea levels rise, groundwater levels will also rise, and groundwater salinities will increase, causing damage to subsurface infrastructures such as tunnels, building foundations, and stormwater and sewer systems. Until now, coastal risk research has largely excluded groundwater-related hazards because they are harder to observe than floods that occur above ground. The long-term vision for this project is to advance new methods for mapping risks to subsurface infrastructure in coastal communities due to sea level rise. The project will co-develop new approaches for assessing exposure and vulnerability to shallow groundwater hazards so that communities can use maps to understand potential damage from rising and salinizing groundwater and make informed decisions about how to maintain or surrender subsurface infrastructure. One important outcome of this planning proposal will be the formation of a Boston area coalition of scientists and community leaders who are concerned about risks to subsurface infrastructure from sea level rise that will collaborate on new risk assessment methods. The coalition will be engaged in conversations about adaptation pathways for mitigating the effects of sea level rise. The activities and products from this planning project will catalyze a larger-scale proposal to extend risk assessment methods to new infrastructure categories and coastal communities throughout the United States. A postdoctoral scientist and PhD student will be mentored in a highly interdisciplinary endeavor that will generate new knowledge at the interface of geoscience, engineering, economics, and urban planning. This pilot-scale effort will focus on a specific subsurface asset category, such as stormwater and sewer systems, and apply workflows to at least two distinct testbed communities in the Boston area, which encompasses the densely urbanized shoreline of Boston Harbor and a diverse set of ex-urban communities. One anticipated product will be pilot hazard, exposure, vulnerability, and impact (or risk) maps. The project will also generate a clearer picture of challenges to upscaling or transferring the workflow to other asset categories and communities. Lastly, a workshop will be convened where prototype risk maps will be used to engage community leaders in adaptation planning conversations to mitigate the impacts of sea level rise on coastal communities. These experiences will inform a future proposal to develop risk maps for new geographic areas and engage communities in adaptation planning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The broader impact of this I-Corps project is the development of image-guided therapy for local injection into tumors ("intratumoral") to treat immunotherapy-resistant solid cancers. Treatment for such diseases constitutes a $125 billion market. Currently, the only approved intratumoral therapy costs $65,000-100,000/patient and is reimbursed by insurance. Through early findings, it was identified that pharmaceutical companies with immune-oncology therapeutics are interested in strategic partnerships and acquisitions to improve the delivery of their drugs. Intravenous or oral medications struggle with toxicity and efficacy issues, motivating pharmaceutical companies to partner with intratumoral drug delivery technologies. Current intratumoral approaches are limited by rapid drug leakage out of the tumor (>70% has dissipated within hours) as well as the need for frequent, impractical repeat doses (clinical workflow only allows monthly dosing or less). Initial discussions with pharmaceutical heads of immune oncology departments and physicians running clinical trials indicates stage IV, Microsatellite instability (MSI)-low colorectal cancer with metastases to the liver is a strong clinical need and potential initial market, with additional interest in pancreatic, lung, and triple-negative breast cancer. 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 a local, sustained intratumoral drug delivery system to create a "cancer vaccine." By modifying the local tumor microenvironment, this technology educates the immune system to fight cancer locally and at distant metastatic sites (the "abscopal effect") with minimal toxicity risk. Current intratumoral drug delivery methods have failed due to rapid drug leakage, difficulty in visualizing the drug upon intratumoral injection, and insufficient drug persistence for a sustained immune response. This solution addresses these issues with an injectable drug delivery platform that delivers a high payload of an immune-activating drug into a tumor, which then which solidifies at body temperature to keep the drug in the tumor for sustained release with low therapeutic leakage. The technology also includes a low dose of an imaging agent to ensure accurate placement. This therapy creates a sustained immune response against traditionally immunotherapy-resistant solid tumors, resulting in the destruction of tumors locally and at untreated metastatic sites with high rates of complete regression in 90-day survival studies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The development of the Standard Model (SM) of particle physics is a major intellectual achievement. The validity of this model was further confirmed by the discovery of the Higgs boson at the Large Hadron Collider at CERN. However, the Standard Model leaves open many questions, including why matter dominates over anti-matter in the Universe (CP violation), the values of the masses of quarks and leptons, and the properties of dark matter, among others. Most explanations require new phenomena, which we call Beyond the Standard Model Physics (BSM), and which the LHCb experiment at CERN has been designed to explore. This award provides facility operations, maintenance, and computing support to enable the participation of U.S. scientists in the LHCb experiment at CERN. The LHC is the premier High Energy Physics particle accelerator in the world and is currently operating at the CERN laboratory near Geneva Switzerland, one of the foremost facilities for answering these BSM questions. The LHCb experiment is one of four large experiments at the LHC and is designed to study in detail the decays of hadrons containing b or c quarks. The goal is to identify the existence of new physics beyond the Standard Model by examining the properties of hadrons containing these quarks. The new physics, or new forces, can be manifest by particles, as yet to be discovered, whose presence would modify decay rates and CP violating asymmetries of hadrons containing the b and c quarks, allowing new phenomena to be observed indirectly. U.S. groups play a leading role in the physics analysis, hardware development, and computing of LHCb. This award will support the NSF-supported groups' share of common items necessary for the experiment, maintenance and operations support for U.S.-delivered components, co-location of an LHCb Tier-2 computing facility, and R&D to optimize the operation of the experiment. The broader impacts of this award cover many areas, from student research experiences for graduate and undergraduate students, to very active QuarkNet Centers and Masterclass programs for high school teachers and students. A steady stream of undergraduates has been working in the participating university laboratories, where graduate students also have direct engagement in both instrumentation development as well as data analysis. Undergraduate and graduate students will be direct participants in the fabrication, testing, and maintenance of detector components under U.S. responsibility. Early career and nontraditional researchers will be integral participants in this exciting program of research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Lithium niobate (LN), first synthesized 70 years ago, is highly valued in photonics for its excellent material properties and ability to facilitate frequency mixing across a broad spectrum, from gigahertz to petahertz frequencies. Recently, the commercial availability of thin-film LN has revitalized interest in the material due to its ability to promote tight mode confinement, thereby enhancing frequency-mixing efficiency and enabling new avenues for optical property optimization, such as dispersion engineering. This project seeks to develop pre-patterned freestanding single-crystalline LN photonic components for advanced quantum photonic integrated circuits. These components will serve as core elements in visible light communication platforms, offering superior performance compared to conventional materials. This work addresses significant challenges in the fabrication of thin-film LN, such as overcoming limitations of the smart-cut process and improving etching techniques. By advancing the integration of high-quality LN films, this project will promote scientific progress, enhance national technological capabilities, and support educational and diversity initiatives by involving K-12 and college-level students in hands-on experiments and workshops. This project will also benefit society by paving the way for advanced quantum photonics and communication technologies. This project aims to develop pre-patterned freestanding single-crystalline LN photonic components to address the limitations of conventional LN fabrication techniques. The PI’s team has demonstrated a universal mechanical exfoliation method to produce freestanding single-crystalline membranes from complex-oxide materials. This project will build on that foundation to develop selective epitaxy of LN on pre-patterned substrates, achieving 100% yield exfoliation of damage-free LN waveguide patterns, and integrating these waveguides into photonic circuits for visible wavelength communication. Throughout the development, the project will precisely engineer photonic components to meet the stringent requirements of quantum photonics. Additionally, the team will optimize wafer-scale tailoring technology for high-density photonic components, advancing large-area quantum photonic platforms. Successful implementation of this technology will have a significant impact on the quantum photonics community by enabling damage-free, ultra-thin LN photonic components for visible wavelength communication. This addresses major limitations of conventional waveguides, such as poor optical confinement due to narrow bending radius and limited modulation capabilities. The project will also pioneer hybrid integration strategies for multiple photonic components, fostering unconventional functionalities and geometries in integrated platforms. Ultimately, this research will innovate photonic integration technology, advancing quantum processing and networking. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This research focuses on understanding how people make decisions and interact based on information. One part explores the long-run implications of people not fully accounting for the ways that their memory can be limited or imperfect, which can lead them to, for example, overestimate their abilities and/or neglect large but infrequent losses. This work has important implications for such macroeconomic issues as why stocks tend to outperform bonds even after correcting for risk, and also helps explain why people’s decisions can seem random. Another part delves into how people decide whether to share stories on social media. The research also examines how community dynamics influence cooperative behavior among interacting partners, which is important for understanding how societies function. The project also explores the behavior of agents who prefer to be surprised, such as those who prefer watching a close sports game to one where their preferred team is almost sure to win; this has implications for voter turnout and online gambling. Lastly, the research develops a method to evaluate theories that predict behavior across different situations, comparing economic models with machine-learning algorithms. This approach is valuable across economics disciplines including development, industrial organization, labor economics, and public finance. The work on limited memory assumes that agents only remember a random subset of their past experiences and that agents are naive about this. It shows that the agent’s long-run behavior satisfies a fixed-point condition that is formalized as a limited memory equilibrium. The research relates limited-memory equilibrium to the literature on stochastic choice, and also considers how closely the behavior of agents with “almost unlimited” but imperfect memory approximates that of agents with perfect memory. The work on social media starts from the premise that people prefer to share accurate content they will pay an attention cost to distinguish true content from false. This research uses results on stochastic approximation via differential inclusions and extends previous results on generalized Polya urns to urn systems that are only piecewise continuous. The work on community enforcement studies how people who interact with a series of partners can use information about their current partner’s past behavior to guide how they interact with them now. With the same technology, self-interested cooperation is less efficient in these settings than in fixed partnerships, but this can be offset by gains from specialization. The research analyzes how the tradeoff between these two forms of social organization varies with the parameters of the interaction. The work on risk and surprise uses results from convex analysis and optimal transport to analyze all concave preferences over risky lotteries that satisfy a form of smoothness condition. It shows how to view any such preference as arising from a taste for surprise, and uses this interpretation to develop new and tractable models of risk preference as well as new interpretations and results for some existing models. The work on transfer performance provides a tractable approach for evaluating cross-domain transfer performance--how accurately will a model estimated on one domain (e.g. a ountry or a set of lotteries) generalize to a new domain--based on techniques that generalize conformal inference by allowing behavior in different domains to be governed by different distributions that are themselves drawn identically and independently from a fixed but unknown meta-distribution. It derives finite-sample forecast intervals for a large class of measures of transfer performance that can be used to evaluate both economic models and black box algorithms. It then uses these forecast intervals to compare the generalizability of economic models and machine learning methods when predicting certainty equivalents for lotteries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-09

Project Summary Mindfulness has been defined as accepting, open-minded attention to the present moment. Mindfulness has been conceptualized as a trait (e.g., mindfulness disposition in everyday life) and a state (e.g., mindfulness in the moment). Greater trait mindfulness, as measured by self-report questionnaires like the mindful attention awareness scale (MAAS) and the five-factor mindfulness questionnaire (FFMQ), is associated with improved well-being and decreased psychopathology. The underlying brain bases, however, of trait mindfulness or the state of mindfulness are as yet poorly understood. A promising research approach is to correlate mindfulness trait measures with task-independent brain functions as measured by resting-state functional magnetic resonance imaging (fMRI) and to use cutting-edge fMRI methods to identify state mindfulness. In our first aim, we aim to identify the most reliable brain network connections that predict trait mindfulness, using three resting state datasets totaling more than 350 adults. We use connectome predictive modelling, a data-driven machine learning approach that identifies whole-brain features that correlate with the behavior of interest. In our second aim, we propose to examine state mindfulness independently in a sample of 100 high-rumination adolescents. We will look at fluctuations in brain networks over time using dynamic functional connectivity methods. We will ask whether changes in behavior on a mindfulness-related task, as well as responses to self-report experience prompts over three days, correlate with these dynamic functional connectivity measures. Our hypothesis is that a state of attentional network anticorrelations (e.g. between the default mode network and frontoparietal network) is associated with mindful attention. Lastly, we will assess the relationship of the dynamic brain changes to rumination and other clinical symptoms in the adolescents. This will shed light on brain-mechanistic links between mindfulness and decreased psychopathology. These conceptually related but independent aims may contribute to a shared understanding of mindfulness, i.e., specific brain networks involved in predicting trait mindfulness may also be implicated in state mindfulness. This research promises to deepen our understanding of mindfulness, paving the way for brain- based insights into how it supports well-being.

NSF Awards · FY 2024 · 2024-09

The global energy transition from fossil fuels to renewable sources and the accompanying need for energy storage, especially for electric vehicles, are generating a massive demand for critical and conventional metals. Refining these metals from ore-bearing rocks leaves a colossal volume (i.e., 5-7 billion m3 per year) of residues that are typically impounded for long-term storage and disposal as high-water-content, hydraulic slurries referred to as ‘tailings’, which are retained by earthen dams. The stability of these tailings storage facilities has been a major challenge facing the mining industry and a safety concern to the public and the environment. In parallel, the mining industry must also address regulations to reduce its carbon footprint that currently contributes to 2-3 percent of total anthropogenic CO2 emissions. Research funded by this award will address these two issues simultaneously by studying carbon mineralization of tailings materials. Geomimetic carbonation will be studied and engineered to convert the gaseous CO2 into solid carbonates that can concurrently serve two purposes: 1) to permanently sequester CO2 within tailings storage facilities; and 2) to serve as a cementing agent for stabilizing the tailings. Therefore, the project will advance the fundamental science to accelerate anthropogenic carbonation, and evaluate this technology as a potential engineering solution to reducing the risk of mine tailings failures. The project also contributes to mitigating effects of climate change by reducing the carbon emissions across the mining industry. This research project’s goal is to study the anthropogenic carbonation process to stabilize mine tailings and hence create fundamental knowledge on the coupled yet competing and counteracting processes of consolidation versus cementation/CO2 mineralization occurring in both legacy and active tailings storage facilities. The specific goals are to uncover the effects of geomimetic carbonation on the mechanical stabilization of mine tailings and, hence, to devise an innovative approach to offset greenhouse gas production in the mining industry while simultaneously improving the stability of tailings storage facilities. Therefore, the research project will contribute to advancing sustainability, environmental protection, and circular economy, generating significant impacts on the environment and human society at multiple scales and dimensions. The project involves an integrated experimental and computational study of the kinetics of silicate dissolution, carbonation, effects of catalysts, and resulting cementation effects. The computational work will focus on the formulation of new soil constitutive models to simulate concurrent growth of carbonation/cementation and self-weight consolidation of the hydraulic fills. Numerical simulations will investigate the effectiveness of carbonation systems on the hydro-mechanical performance of existing tailings dams, and on designs for new storage facilities. 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 · 2024-09

PROJECT SUMMARY/ABSTRACT Chronic kidney disease (CKD) is an urgent health problem in the U.S., afflicting ~15% of Americans and costing the healthcare system $120 billion/year. Despite the availability of glucose-normalizing drugs, diabetes remains the leading cause of renal failure. Current treatments depend on glucose and hypertension control that cannot completely prevent diabetic nephropathy and progressive renal dysfunction. A major feature of diabetic nephropathy is inflammation, driven by the upregulation of adhesion proteins in renal endothelium and subsequent recruitment of inflammatory immune cells into renal tissue. Previous work has shown that knockout of the adhesion protein ICAM1 attenuates diabetic nephropathy in mouse models of type 1 and 2 diabetes. However, there is a lack of clinically viable technologies that can act on these findings. An FDA-approved lipid nanoparticle (LNP) siRNA drug, patisiran, is capable of safely inducing efficient (>80%) and durable (up to one month) gene knockdown in humans. As LNPs typically accumulate in the liver, this approach has not been broadly applicable to other tissues. This application seeks to develop new LNPs for efficient siRNA delivery to renal endothelium, to test the hypothesis that knockdown of ICAM1 is protective against type 1 and 2 diabetic nephropathy. The goal of Aim 1 (K99 phase) is to identify LNPs capable of efficient siRNA delivery to renal endothelium through high-throughput in vivo screening. A panel of DNA-barcoded LNPs with varying compositions will be screened for functional siRNA delivery to the renal endothelium using a new workflow. Isolated barcodes will be analyzed by deep sequencing to deduce LNP parameters that mediate renal endothelial delivery and inform subsequent, refined screens. Lead LNPs will be individually validated and assessed for knockdown efficiency, duration, and safety. During the R00 phase, LNP efficacy will be tested in multiple models of type 1 and 2 diabetic nephropathy. These models capture human diabetic nephropathy features of ICAM1 upregulation, albuminuria, renal fibrosis, and macrophage infiltration into renal tissue. LNPs carrying ICAM1 siRNA will be tested for efficacy in prophylactic (before disease onset) and therapeutic (after disease onset) models. Successful completion of this work could enable new, precision medicine approaches that target inflammatory drivers of diabetic nephropathy. The PI, Gary Liu, aims to lead a lab that develops new renal therapeutics and arrest disease progression. A major focus will be gene therapies for kidney diseases. To prepare him for this role, this K99/R00 application includes training and coursework in DNA barcoding, deep sequencing, and bioinformatics; nephrology and diabetic nephropathy; LNP technology; and inclusive mentoring. Moreover, this application will enable the PI to disseminate work at conferences, network, and apply for faculty positions. Training will occur at the Koch Institute of MIT in the laboratories of Profs. Robert Langer and Giovanni Traverso, which will provide the resources and intellectual expertise necessary to carry out the work.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Proper gene expression regulation is pivotal for cellular function, and its dysregulation contributes to disease. Transcription factors (TFs) initiate gene expression by identifying binding sites in enhancers and promoters and recruiting the transcriptional machinery. However, the nucleus's crowded nature and the abundance of non-specific sites make it extremely challenging for TFs to efficiently search for and binding correct DNA binding sites. This project addresses the fundamental question: How do TFs efficiently locate specific sites amidst numerous non-specific ones inside a crowded nucleus? Innovatively, this study employs a new advanced microscopy technique that overcomes the low spatiotemporal resolution associated with camera-based single-molecule tracking. This approach enables precise tracking of TFs in live human and mouse cells with unprecedented spatial (~2-4 nm) and temporal (one hundred microseconds) precision. This transformative approach represents a substantial advancement over traditional methods, facilitating the investigation of TF search mechanisms. By comprehensively tracing TFs' 3D diffusion, DNA interactions, and target site discrimination, we will resolve the TF target search mechanism. First, we will optimize and validate the proposed tracking method and develop novel computational methods for handling and analyzing these new types of tracking data. Second, we will apply this technology to understand how TFs involved in pluripotency and genome structure find their target sites and elucidate how individual protein domains affect the target search mechanisms. Third, we will apply this technology to uncover the oncogenic potential of fusion TFs in several cancers. Fourth, we will leverage these studies to understand how “search domains” in TFs regulate the target search mechanism and efficiency towards the rational design of synthetic TFs with tunable search properties. Taken together, this proposal will reveal how TFs find their target sites with applications to synthetic biology and cancer biology.

NIH Research Projects · FY 2025 · 2024-08

SUMMARY Understanding the complex relationship between brain activity and behavior is one of the most exciting and challenging pursuits in neuroscience. The proposed BBQS AI Resource and Data Coordinating Center (BARD.CC) aims to facilitate innovative research in this area by managing, sharing, and harnessing the power of vast amounts of data and machine learning resources generated by various projects within the BBQS consortium. We will focus on five interrelated aims: 1) Data Management; 2) Data Standards; 3) Machine Learning and Artificial Intelligence (ML/AI) Resources; 4) Data Ecosystem; and 5) Dissemination, Training, and Coordination. The first aim is to serve as a hub for efficient data curation, management, and sharing. We will collaborate with other BBQS projects and coordinate with existing BRAIN data archives to curate and harmonize project data. Data management will be handled by a combination of automated data ingestion and human oversight, transitioning to a fully automated system over time. We will work with scientists and relevant communities to implement robust quality assurance and control solutions. The second aim focuses on establishing data standards for novel sensors and multimodal data integration, as informed by the use of existing standards and best practices from similar efforts. We will aggregate relevant standards for data and metadata, data processing methods, appropriate ontologies, and common data elements, and adapt as needed for evolving methodologies. The third aim involves the development and definition of ML/AI resources for BBQS. We will evaluate and curate relevant ML/AI models and platforms, aggregating datasets, models, and other ML/AI resources from both within and outside the BBQS consortium. These resources will be made available to consortium members, with each resource's origin documented and evaluated for performance and ethical generation and use. Moreover, all models will be made available through public repositories, allowing for widespread access and utilization. The fourth aim involves creating a cloud-based data ecosystem and computational platform. We will collaborate with relevant archives and computing facilities to develop a computational platform in the cloud. This platform will enable access to and processing of even very large data sets with commonly used pipelines and provide a wide range of users, even those with limited resources, with computational capability to analyze and visualize data, models, and model outputs. Finally, the fifth aim is centered around efficient dissemination, training, and coordination of BBQS research resources. We will coordinate data sharing, offer training on relevant topics like neuroinformatics, neuroethics, and ML/AI, and maintain a consortium Web portal. Furthermore, center staff will coordinate consortium activities like meetings, working groups, and policy and ethics discussions, ensuring smooth and effective operation. In summary, BARD.CC aims to catalyze the discovery of valuable insights from intricate relationships between brain activity and behavior, which in turn could advance neuroscience and our understanding of the human mind.

NIH Research Projects · FY 2024 · 2024-08

SUMMARY Understanding the complex relationship between brain activity and behavior is one of the most exciting and challenging pursuits in neuroscience. The proposed BBQS AI Resource and Data Coordinating Center (BARD.CC) aims to facilitate innovative research in this area by managing, sharing, and harnessing the power of vast amounts of data and machine learning resources generated by various projects within the BBQS consortium. We will focus on five interrelated aims: 1) Data Management; 2) Data Standards; 3) Machine Learning and Artificial Intelligence (ML/AI) Resources; 4) Data Ecosystem; and 5) Dissemination, Training, and Coordination. The first aim is to serve as a hub for efficient data curation, management, and sharing. We will collaborate with other BBQS projects and coordinate with existing BRAIN data archives to curate and harmonize project data. Data management will be handled by a combination of automated data ingestion and human oversight, transitioning to a fully automated system over time. We will work with scientists and relevant communities to implement robust quality assurance and control solutions. The second aim focuses on establishing data standards for novel sensors and multimodal data integration, as informed by the use of existing standards and best practices from similar efforts. We will aggregate relevant standards for data and metadata, data processing methods, appropriate ontologies, and common data elements, and adapt as needed for evolving methodologies. The third aim involves the development and definition of ML/AI resources for BBQS. We will evaluate and curate relevant ML/AI models and platforms, aggregating datasets, models, and other ML/AI resources from both within and outside the BBQS consortium. These resources will be made available to consortium members, with each resource's origin documented and evaluated for performance and ethical generation and use. Moreover, all models will be made available through public repositories, allowing for widespread access and utilization. The fourth aim involves creating a cloud-based data ecosystem and computational platform. We will collaborate with relevant archives and computing facilities to develop a computational platform in the cloud. This platform will enable access to and processing of even very large data sets with commonly used pipelines and provide a wide range of users, even those with limited resources, with computational capability to analyze and visualize data, models, and model outputs. Finally, the fifth aim is centered around efficient dissemination, training, and coordination of BBQS research resources. We will coordinate data sharing, offer training on relevant topics like neuroinformatics, neuroethics, and ML/AI, and maintain a consortium Web portal. Furthermore, center staff will coordinate consortium activities like meetings, working groups, and policy and ethics discussions, ensuring smooth and effective operation. In summary, BARD.CC aims to catalyze the discovery of valuable insights from intricate relationships between brain activity and behavior, which in turn could advance neuroscience and our understanding of the human mind.

NSF Awards · FY 2024 · 2024-08

This project is a highly targeted, urgent and short-term continuation of an existing program which studies the early Universe by deploying instrumentation to remote, radio-quiet locations. This enables sensitive observations with unprecedented absolute calibration. The current observations are being made from a remote location in Australia. The next step is to repeat the Australian measurement from the northern hemisphere, but this is already seriously compromised and may become impossible, due to rapidly strengthening anomalous emission near 63 MHz. It is urgent to deploy equipment during the 2024/25 winter season and take measurements before this interference comes to dominate. As there is evidence the signal comes from satellites in low earth orbit, the PI and team will also be working with the probable operator, to characterize, identify, and mitigate the spurious emission, in close coordination with the NSF Spectrum Management Office. This work is of strong practical utility world-wide, will raise awareness of unintended science impacts from industrial efforts, and forge connections with remote communities, helping scientific use of extraordinary locations. The need for urgent action is a swiftly deteriorating global RFI situation due to satellite mega-constellations, which cannot be avoided by relocating, and which threaten to make this measurement soon impossible from anywhere on the surface of the Earth. The PI has identified a location from which to make the necessary observations: remote Adak island, 1930 km from the nearest substantial concentration of FM radio transmitters, is superior to the site in western Australia, and will provide long winter nights for data-gathering. The proposal describes other characteristics of this impressive site. This study is urgently needed to run these tests before the environmental changes render them impossible. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Topology is a branch of mathematics that is concerned with the shape of a space. Two-dimensional surfaces are topologically characterized by the number of holes within, a simple notion of complexity. Optimizing geometry helps to better understand the underlying topology; for example, flattening a crumpled sheet of paper or untying a balloon animal makes it easier to count their holes. Ricci flow, a geometric optimization technique, searches for optimal geometries called Einstein manifolds, generalizing flat planes and round spheres. This flow provides an algorithmic approach to decomposing three-dimensional geometries and has been used to characterize topologies in dimension three. While challenges in dimension five have been addressed through systematic methods, the final frontier is dimension four, where Einstein manifolds have been extensively studied in physics. This project aims to further study Einstein four-manifolds while developing a new, four-dimensional-specific theory of Ricci flow. It will bring together researchers from analysis, geometry, topology, and physics. The PI will involve undergraduate and graduate students in the project and integrate research questions into teaching and advising. This project aims to understand and construct 4-dimensional Einstein metrics and Ricci flows. The main difficulties are singularities, where topological surgeries occur, specifically "orbifold" singularities, "cusp" formation, and collapsing. The PI will continue studying these degenerations, their stability, and their links to additional structures such as a Kähler one. Most results about Ricci flows are either 3-dimensional or apply to any n-dimension, with few focusing on the specifics of dimension 4, where most topological questions remain open. Through his extensive study of Einstein 4-manifolds, the PI is familiar with the rich set of 4-dimensional techniques developed over decades. He aims to apply these techniques to study Ricci flows and their singularities, focusing on the topological content of the three types of degenerations: orbifold singularities, cusp formation, and collapsing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project is funded by the Environmental Chemical Sciences Program in the Division of Chemistry. The research team (led by Professor Jesse Kroll and Professor William Green) will carry out experimental and modeling studies of the chemical reactions of organic peroxy radicals in atmospheric droplets. The atmosphere includes a wide range of organic (carbon-containing) compounds, emitted from sources such as vehicles, wildfires, and plants, and these undergo chemical reactions during their time in the atmosphere. Nearly all these reactions make organic peroxy radicals, highly reactive chemicals whose subsequent reactions help determine the impact of the organic compounds on air quality and climate. Most past studies of peroxy radicals have focused on their chemistry in the gas phase, but their chemistry in cloud droplets and fine particles is less well understood. Therefore, this project will study the chemical reactions that these radicals will undergo within liquid water. Laboratory studies will measure reaction products under different atmospheric conditions, and computational studies will focus on predicting reaction rates and products. Graduate and undergraduate students will be involved in this research, and the team will participate in MIT-wide research opportunity programs aimed at students from traditionally underrepresented groups. The aim of this project is to gain a predictive, mechanistic understanding of the chemistry of organic peroxy (RO2) radicals in the atmospheric aqueous phase (cloud droplets and deliquesced aerosol particles). The project will examine aqueous-phase RO2 chemistry in detail, via both laboratory experiments (to measure reaction product distributions) and computational studies (to predict reaction kinetics, mechanisms, and solvent effects). Laboratory work will center on the OH oxidation of multifunctional, water-soluble species, with careful control of reaction conditions (chemical environment, RO2 co-reactants) and the use of online mass spectrometry to measure reaction products in real time. Studies will be carried out first in the dilute bulk aqueous phase, then in the concentrated (high ionic strength, low pH) bulk aqueous phase, and finally in deliquesced particles. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project aims to advance the understanding of statistical analysis within a broader environment with multiple stakeholders. In particular, the results of statistical hypothesis tests are often used to make yes/no decisions (e.g., whether to release a drug candidate to the public) that affect multiple parties. The decision materially impacts the party who developed the drug and public health generally, but in different ways, and this may impact how the stakeholders interact (e.g. when they choose to release data). We seek to develop statistical protocols that are robust to the behavior of different stakeholders who may have different aims. Progress on this front will lead to more reliable conclusions from data that are collected by multiple parties, contributing to the trustworthiness of the scientific enterprise. Moreover, the project is interdisciplinary in nature, bringing together ideas from statistics and also economics and social science. Fostering such connectivity is helpful for both fields, and such links are also useful educationally. The project also provides research training opportunities for students and postdocs. The technical goal of this proposal is to study statistical inference in settings that involve both incentive structures because actions and statistical decisions affect the behavior of other parties, and information asymmetry, as one party may have private information not available to others. In order to bring sharp focus to the core issues, this project tackles this challenge within a particular model of interaction known as the principal-agent model. As opposed to standard game-theoretic analyses of such interactions, the PIs study a statistical version of the principal-agent model, wherein the statistician plays the role of the principal. The principal has the goal of carrying out some form of statistical inference. In order to do so, the principal can interact with one or more agents that can provide statistically relevant information (e.g., a drug to test in a clinical trial, a feature set, datasets of varying and uncertain quality). Viewed as a two-person game, the principal moves first by specifying a statistical protocol, along with some kind of payment structure associated with it. The agent then makes its decision, which the PIs model it in terms of expected utility maximization. Our primary goal is to develop methodology and theory for hypothesis testing in this interactive setting. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project focuses on symplectic manifolds, which are crucial objects in understanding the mathematics behind many physical phenomena, such as the movement of planets and the behavior of particles. The main goal is to find different ways to identify and count surfaces within these spaces. Understanding these two-dimensional objects can help us comprehend the more abstract, high-dimensional spaces they exist in. By analyzing this detailed geometric information, the project aims to tackle theoretical mathematical problems inspired by physics, such as those seen in Hamiltonian mechanics and string theory. Solving these theoretical problems can enhance our understanding of complex systems, potentially resulting in advancements in technology, healthcare, and general knowledge of nature. In addition, this project will provide research training opportunities for students. The counts of pseudo-holomorphic maps into symplectic manifolds are usually rational-valued due to the presence of nontrivial automorphisms. The project aims to answer questions in Hamiltonian dynamics and mathematical physics by developing curve counts with coefficients beyond rational numbers, including integers, complex K-theory, and complex cobordism. New curve-counting invariants inspired by cohomological operations and homotopy-theoretic enhancement of Floer theory will be developed along the way. The research topics include global Kuranishi charts for operations in the integral Hamiltonian Floer theory, Adams operations in enumerative geometry, Floer homotopy types over complex cobordism, and the study of periodic points of Hamiltonian diffeomorphisms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

According to the National Academies’ 2020 Decadal Survey on Astronomy and Astrophysics "Gravitational wave astrophysics is one of the most exciting frontiers in science” and a next-generation gravitational-wave observatory in the US is “central to achieving the science vision laid out in the survey’s road map”. Current generation gravitational-wave detectors NSF's Advanced LIGO and Advanced Virgo have opened the era of gravitational wave astrophysics with the first gravitational wave detections from mergers of binary black hole, binary neutron star, and black hole-neutron star systems, and have triggered a broad range of studies including novel tests of General Relativity, understanding constraints on the interior of neutron stars, and new measurements of the Hubble constant describing the expansion of the universe. Cosmic Explorer, the next-generation ground-based gravitational wave observatory in the US, will transform and accelerate the field of gravitational wave astrophysics, enabling investigations of the farthest reaches of our universe and opening new collaboration pathways. This work will help ensure Cosmic Explorer reaches design sensitivity at the lowest frequencies by reducing the impact of disturbances in the local gravitational field around the detectors. This low-frequency sensitivity improvement will enable Cosmic Explorer to observe interesting heavy astrophysical objects such as intermediate-mass black holes and increase early warning capabilities that enable electromagnetic telescopes to view the moment of mergers of compact binary objects. The award will also train students and postdocs in STEM areas. Gravitational wave detectors are responsive to the gravitational forces, as described by Newton’s Law of Universal Gravitation, induced by any mass that is in close proximity to the instrument. Fluctuations in mass density due to propagating seismic waves create a limit to the instrument’s sensitivity. This work will develop techniques for assessing local gravity disturbances based on simulations and analysis of future measurements of the environment at proposed locations of Cosmic Explorer observatories and will help determine the viability of these candidate locations. The team will develop techniques for measuring and mitigating Newtonian noise using a series of simulations of seismic and other vibrational noise. This work will feed into the conceptual design of the Cosmic Explorer facilities and the local topology surrounding them to minimize the local gravity disturbances near the detector. It will also provide designs of instrument arrays necessary for measuring and inferring Newtonian noise that will be capable of mitigating the influence of those disturbances on the gravitational-wave data stream, and provide preliminary cost estimates for Newtonian noise mitigation. These efforts will enable the 20 dB of seismic Rayleigh wave mitigation required to meet Cosmic Explorer’s low-frequency sensitivity target. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

A central challenge of our time is demonstrating a practical quantum advantage for problems relevant to science and engineering. To address this challenge, the team envisions building an advanced quantum computing laboratory that enables external users to propose and demonstrate quantum information science and engineering (QISE) advances that map these problems to many-body quantum states in neutral atoms. By building a robust infrastructure to create and fault-tolerantly measure quantum many-body states, quantum advantage may be realized across problems in chemistry, materials, and physics, discovering fundamental truths inaccessible with existing classical techniques. The program will simultaneously advance critical hardware, software, and architecture for general-purpose quantum computing as well as other quantum technologies including quantum networks and sensing. This project will make significant advances in atomic physics, quantum error correction, and quantum compilation to meet the requirements of the logical quantum processing unit (LQU) and the analog quantum simulator (AQU), which represent capabilities far beyond the state-of-the-art for any quantum science platform. The project will also develop next-generation integrated electronics and photonics to control large-scale quantum systems at high speeds. These technologies will benefit all quantum science platforms as well as applications limited by today’s optical devices, such as LiDAR and optical networking. Our approach capitalizes on recent breakthroughs in programmable quantum simulators and neutral atom computing to develop an open quantum testbed. This consists of an error-corrected digital quantum processing unit (LQU) with over 100 fault-tolerant logical qubits connected to an analog quantum simulator (AQU) comprising over 1000 physical qubits, and teleportation-based interfaces for loading arbitrary quantum states into the AQU from external devices. By making the quantum computer broadly accessible, the project will enable many research teams to test their algorithms and hardware, overcoming existing barriers to developing or acquiring cutting-edge quantum technology: large capital, labor investments and domain expertise in multiple fields. This NQVL quantum science and technology demonstrator project (QSTD) will democratize access and enable a greater than 10-fold increase in quantum builders and users by 2030 through hardware models ("digital twins"), open-source software, end-to-end system engineering, shared facilities for validation and benchmarking, and community engagement. This interdisciplinary initiative will advance multiple academic fields, enable new hardware innovations and practical quantum advantages through modular software and architecture prototypes. Key beneficiaries are quantum hardware and software developers who will gain access to a centralized infrastructure for component testing and executing quantum algorithms. This project advances the objectives of Quantum Information Science and Engineering at NSF in response to the National Quantum Initiative Act for the continued leadership of the United States in QIS and its technology applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.