SUNY at Stony Brook

NSF Awards · FY 2024 · 2024-10

Recent years have seen the emergence of a variety of resource-intensive distributed compute loads in context of machine learning model training and streaming data analytics. These compute loads often consist of graph-based jobs with its nodes being computation tasks, and edges being communication-based dependencies between these tasks. Due to high communication costs or privacy concerns, such a graph-based job is preferred to be performed in a distributed manner at wireless edge cloud. To achieve this, the computation tasks of a job that require computation and communication resources must be mapped to physical servers in wireless edge cloud. However, most existing wireless network scheduling algorithms rarely account for logical relationships between computation tasks of a graph-based job; and most existing learning algorithms pay little attention to the underlying wireless network constraints, and their successes in practice are further impeded due to the curse-of-dimensionality, and lack of expressiveness and adaptation. This project aims to bridge the gap between prevailing graph-based job services and wireless edge cloud designs via advocating structured learning and optimization solutions with provable performance guarantees. This project will additionally focus on advancing curriculum development, recruitment of students, involvement of undergraduate students in research, K-12 outreach via summer camps, as well as research dissemination via workshops and conferences. The project aims to serve concurrent resource-contention graph-based jobs in the wireless edge cloud. It brings together mathematical methods to develop and analyze structured learning and optimization solutions that holistically exploit the inherent problem structure encoded in classical network models and algorithms to design data features and learning architectures for improved sample efficiency, accelerated learning speed and robust performance. The project addresses the key challenges of doing so via three interdependent thrusts. The first thrust focuses on designing structured reinforcement learning solutions for concurrent graph-based job scheduling at a fast timescale to minimize the job service latency when concurrent graph-based jobs arrive randomly and contend for limited network resources. The second thrust focuses on studying the complementary fast timescale problem of maximizing a job’s output given the allocated resources. The third thrust addresses the pertinent problem of adaptive resource provisioning at a slow timescale to make graph-based job services cost-effective. The project also conducts extensive performance evaluations to validate the developed approaches and algorithms. 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 Condor Array Telescope has been operating in southwest New Mexico since 2021. This observatory combines six off-the-shelf refracting telescopes with six off-the-shelf CMOS cameras and is optimized for very high sensitivity, very rapid time resolution, and a very wide field of view. Continued operations of this telescope in a collaboration between the State University of New York at Stony Brook and the American Museum of Natural History will focus on three specific science topics: imaging the “cosmic web” of intergalactic gas, searching for shells from ancient stellar novae, and making deep images of our nearest galactic neighbors. Under this award, the collaboration will replace the current cameras, which will increase the field of view of the array by 70%. A portion of the observing time on Condor will be allocated to provide astronomical telescope access to under-represented students at Stony Brook University, and the telescope will provide faculty, graduate students, and undergraduate students at the respective institutions with access to a world-class astronomical observing facility with unique observational capabilities. The research team will use Condor to obtain, analyze, and interpret a variety of observations of the Northern Hemisphere sky, focusing on several important science topics: (1) Condor will image huge portions of the extremely faint and distant filaments of the “cosmic web” of intergalactic gas that stretches between the galaxies, seeking to determine its structure on very large spatial scales. (2) The team will explore extended regions surrounding 25 cataclysmic variable stars of a variety of different types, searching for ancient nova shells that may provide clues as to how the types are related to one another. (3) Condor will monitor wide regions of the sky toward two nearby groups of galaxies minute by minute and night by night, gradually building up deep images of our nearest galaxy neighbors while searching for flaring novae and other evidence of stars that have been expelled from the galaxies and are now floating freely in intergalactic space. These same observations are also likely to find Earth-like planets orbiting in the habitable zones of white dwarf stars and tenuous dusty clouds of interstellar gas that obscure our view of the distant Universe. 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 extratropical storm track is responsible for the poleward transport of momentum, heat, and moisture across the midlatitudes. Storm track eddies and fronts are responsible for much of the high impact weather in the midlatitudes including inland flooding, strong wind, and coastal inundation events that threaten both life and property in these regions, as well as result in significant economic losses. Despite the significance of the extratropical storm track to the climate system, it remains poorly represented by current climate models. In particular, while the magnitude of the biases has improved over time, the spatial pattern of the biases remains, suggesting an ongoing gap in our of understanding of the storm track and its variability. This collaborative work aims to bridge this gap in knowledge by first demonstrating a link between tropical deep convection, planetary scale stationary wave activity, variations in the zonal mean equator to pole temperature gradient, and extratropical storm track activity. The study will also work to identify the physical mechanisms acting to establish these links and explore the extent to which these processes are represented in climate models and subseasonal to seasonal (S2S) prediction models. The study will train two PhD students in meteorology, one at each of the principal investigators home institutions, preparing the next generation of the STEM workforce. The principal investigators will additionally expand an existing partnership between Penn State and a local area rural high school to two high schools in New York with a predominance of economically disadvantaged students. These students will participate in a hybrid workshop in which the students will share their experiences with the learning materials and listen to guest speakers share career advice and opportunities. The main hypothesis to be tested by this study is that tropical heating is linked to extratropical storm track activity through modulation of planetary scale stationary wave activity. The study will also consider the impacts of the resulting storm track variability back onto the planetary stationary waves. Using gridded data sets, local and global mechanisms will be explored that may play a role in linking tropical heating to extratropical storm track activity. The nature of the tropical heating most effective at changing mid-latitude stationary wave amplitude in a way that most influences zonal mean available potential energy and storm track activity will also be explored, as will other mechanisms for exciting stationary waves that go on to impact storm track activity and their relative importance over the tropical heating mechanism. Causal relationships between stationary waves and storm track activity, latent heating and storm track activity and latent heating and stationary waves in observations will be established using lead-lag single value decomposition (SVD) analysis. Idealized modeling studies will also be carried out using a dry global circulation model to understand the causal relationships of the observed fields. The study will then explore how well CMIP6 and subseasonal to seasonal predictive models capture the links between tropical heating, planetary stationary waves, zonal mean available potential energy and extratropical storm track activity identified in the observations. The study will be carried out by the two graduate students supported by the project under the supervision of the principal investigators. These students will benefit not only from the research experience offered by the project, but also from the mentee experience offered through the outreach activity with the high school students in Pennsylvania and New York. 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

Understanding and predicting our planet's weather and climate have proven to be two of the most challenging endeavors undertaken over the past half century. To make further progress, transformational shifts in the computational and observational infrastructures used to inform understanding and guide model predictions are required to address these challenges. The objective of this award is to design and deploy the Multisensor Agile Adaptive Sampling (MAAS) cyberinfrastructure (CI) for optimized, intelligent, collaborative, adaptive atmospheric experimentation. The MAAS CI's goal is to significantly improve the ability to sample rapidly evolving atmospheric phenomena by providing better control systems across multiple advanced radar systems. By better enabling the real-time, fine-grained, coordinated control of atmospheric observing instruments, the MAAS framework can revolutionize the study of convective storms towards the goal of improving high-resolution simulations of extreme or high-impact storms. The activity brings together a multidisciplinary team of scientists, graduate and undergraduate students engineering, computer science, and atmospheric science. The project offers hands-on experience for professionals and students on advanced sensor technologies and measurements and on autonomous optimized experiments. The MAAS cyberinfrastructure will provide the opportunity for the current and next generation of U.S. practitioners to be competitive in a global research environment. Through the development and deployment of an innovative distributed computing platform that integrates centralized high performance computing systems, edge computing systems co-deployed with atmospheric observing facilities, and automated as well as human-controlled software applications, the MAAS cyberinfrastructure will greatly enhance the dynamic, real-time operational capabilities of existing radar-focused instrument facilities for observing convective cloud systems. MAAS CI is also key for integrating future observing technologies like drones and phased-array radars and for optimizing atmospheric experimentation. Data-driven, adaptive observations enabled by MAAS can lead to a substantial increase in spatiotemporal resolution of convective cloud systems. Such observations can for the first time provide new insights into rapidly evolving atmospheric phenomena associated with boundary layer processes and convective storms. The MAAS CI is a Java based control server that handles all communications and edge computations across the network. In addition, a MAAS Nowcasting and Guidance Python-based software package residing on a central high performance computing system handles real-time situational awareness of atmospheric states and the identification of atmospheric features of interest based on rules provided by the MAAS CI users. MAAS CI greatly enhances the operational capabilities of existing NSF radar-focused Community Instrument and Facilities (CIFs), and it is also key for integrating future observing technologies, such as drones and phased-array radars and for optimizing atmospheric experimentation. The integration of the MAAS CI in field experiments and analysis is expected to provide unique, high spatiotemporal resolution microphysical and dynamical observations in clear and cloudy atmospheres. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the National Discovery Cloud for Climate initiative within the Directorate for Computer and Information Science and Engineering and by Geosciences Directorate’s Research, Innovation, Synergies, and Education and Atmospheric and Geospace Sciences divisions. 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 collaborative research project will enable new capabilities for robots to perceive and manipulate soft and fragile objects in applications such as surgery. A central innovation is a thin gel coating that changes its optical properties directly in response to external forces. These optical changes reveal the distribution of forces across the coating in colorful fringe patterns that appear on the surface, even when the applied forces are too small to significantly deform the coating shape. Specifically, this project will create tactile-based robots that integrate the color-changing gel into a force-interpreting optical system, giving the robot the capability to perceive mechanical and physical properties of soft and fragile objects and manipulate these objects without damage. This advancement will surpass existing tactile robots in areas such as medical robotics, assistive technologies, and mixed and virtual reality. In addition, this project will establish a unique platform for workforce development through educational and training activities in robotics and provide an inclusive avenue for engaging underrepresented groups in STEM disciplines. This project will integrate fatigue-resistant photoelastic gel into a stress-interpreting optical system for high-performance vision-based tactile gel-robots that can obtain multi-physical perception and execute ultra-gentle manipulation of soft and fragile objects. Specifically, this project will leverage the molecular design of fatigue-resistant photoelastic gels, the mechanical design of a stress-interpreting photometry system, and the algorithm design of physics-informed machine learning to perceive, visualize, and interpret robot-object interactions. Finally, this project will integrate material design, mechanical design, and algorithm design to build a physics-empowered, vision-based tactile gel-robot, and demonstrate robotic multi-physical perception and ultra-gentle handling capabilities previously unattainable (for example collecting fragile jellies for study in marine biology or cutting and manipulating foods like custards in assistive robotics). 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 Major Research Instrumentation (MRI) award supports the acquisition of a laboratory-based X-ray absorption spectroscopy (XAS) instrument for spectroscopic studies to probe the chemical state, electronic structure, and local coordination environment of atoms that underpins research questions spanning materials science, chemistry, geosciences, environmental sciences, chemical engineering, and condensed matter physics including in catalysis, functional materials, metallurgy, and metals in medicine. This laboratory-based instrument overcomes limitations associated with traditional XAS measurements at large synchrotron facilities. Regular instrument access allows feedback for material synthesis efforts, and long duration studies of transformations that occur over months. As needed, measurements can be coordinated with specialized sample preparation facilities at Stony Brook University and can be integrated with advanced sample environments and controls. The instrument facilitates training of the next generation of instrumentalists, with its virtual beamline layout providing trainees the opportunity to design, test, and optimize new experiments and instrumentation—activities that are difficult to accommodate at synchrotron beamlines. The instrument broadens participation in science and engineering research by providing a research capability, that could previously only be accessed through travel to synchrotrons, which may be inaccessible to some individuals. Undergraduate trainees at Stony Brook University and the nearby Farmingdale State College are introduced to XAS as a tool for advanced characterization measurements. The XAS instrument operates as a multi-user facility housed within the Chemistry Department at Stony Brook University. High quality spectra can be acquired for elements from Ti to Nb, and I to Am, using a high-powered X-ray tube coupled to modern X-ray optics and detectors. Acquisition of multiple spectra in short time frames allows for high-throughput sample characterization, and real-time tracking of reaction processes to enable operando studies of catalysis, to understand functional materials, to resolve open questions in metallurgy, and to interrogate redox states fundamental to geosciences and environmental science. The utility and utilization of the instrument is optimized through use of custom multi-sample changers, specific to different sample formats and sample environments and reactors are optimized for studies of dynamic states and reactions. Key projects enabled addresses the local structure of mono and heterometallic molecular catalysts, the mechanism for capacity loss in battery electrodes, and the progressively degradation of selective gas-binding in porous sorbents and catalysts over months-years of operation. 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 project aims to enhance how computers understand and recognize shapes in images and 3D data. While current advanced computer systems are powerful, they struggle to perceive objects as humans do—as whole shapes made up of interconnected parts. This project seeks to teach computers to focus on the critical parts of an image that constitute an object, disregarding the background and other distractions. The project is also developing methods for computers to comprehend the relationships between different parts of an object, akin to recognizing a chair by identifying its seat, legs, and back in a specific arrangement. This will be achieved within recent deep learning frameworks without explicitly referencing such parts. The improved visual perception capabilities could help computers better to perform tasks like object recognition, 3D position estimation, and understanding shapes from various viewpoints. The potential applications of this research are vast, including advancements in medical imaging, dental radiology, augmented reality, assisted driving technologies, and robotics. Additionally, the project will involve students from high school to doctoral programs in research, with a focus on including underrepresented groups. The project creates a framework for deep learning models to learn configural (or holistic) shape representation and arrangements of object parts, demonstrating their applicability to various computer vision tasks. It will augment the learning framework of vision transformers to learn how to restrict the attention of image patches. This is achieved by adding a new branch that computes an auxiliary loss called object-focused attention, which limits the attention to patches belonging to the same object. This allows transformers to gain a better understanding of configural object shapes by largely ignoring the background and other objects. Additionally, the project will develop a novel graph-transformer-based shape configuration framework named ShapeGT for generic shape understanding tasks. ShapeGT will include several new techniques and applications, including (1) novel between-edge attention and edge-to-node attention modules, (2) joint graph learning and matching algorithms, (3) view encoding techniques for multi-view analysis, and (4) cross-modal fusion mechanisms for capturing 3D-to-2D (shape-to-image) interactions. Due to the wide range of applications of shape analysis, the project is expected to have broader impacts on fields beyond computer vision, such as dental maxillofacial radiology, augmented reality, molecular biology, assistive driving, medical image analysis, and robotics. 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 learn language is a fundamental part of being human, but researchers do not yet understand the mechanisms that underlie this ability. This research focuses on two aspects of this mystery: how humans learn the words of their language (morphology), and how they acquire the systematic variation in the pronunciation of these words (phonology). For example, English speakers can recognize that the words "atomize" (AE-tuh-mahyz) and "atomic" (uh-TOM-ik) are both based on the root "atom," even though the sounds in that root are pronounced differently in the two words. An influential hypothesis is that people store mental representations of the pronunciations of words that abstract away from some finer aspects of their pronunciation. This research aims to make progress towards answering the question of how these mental representations are formed, and how speakers learn the language-specific patterns governing the systematic variation in the pronunciation of related words. The proposed research informs hypotheses regarding the organization of mental representations of words and phonological patterns in children, provides interpretable algorithms and software which can be used for analysis and processing of low-resource languages, and provides insights into sequential processing and learning more generally, which has applications in other fields such as bioinformatics and planning and control systems in robotics. The leading idea guiding this research is that linguistic generalizations are not arbitrary, and are in fact guided by computational and structural restrictions related to memory and perception. It is well-known that such restrictions can be expressed with particular (called subregular) forms of logic and automata. This research applies a modular, interpretable, and small-data approach to tackle the problem of simultaneously learning the mental representations of words and their morphological and phonological patterning. This research is based on foundational results connecting the computational complexity of morphological and phonological patterns to learning procedures which are specific to subregular logics and automata. Computational analysis of the principles that guide morpho-phonological analysis are combined with algorithms based on subregular properties of morphological and phonological patterning, which are (1) suitably generalized to a variety of morpho-phonological representations and (2) made robust to optionality, variation, and exceptions. The goal is to better understand, qualitatively and computationally, the mechanisms which underlie the human capacities for both constructing mental representations of words and learning the morpho-phonological patterns governing them. Research activities also provide the resulting algorithms as open-source software and evaluate them empirically and quantitatively, with a focus on low-resource languages. 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

Terrestrial land surfaces rise above the ocean surface because they are underlain by crustal rocks, a layer of material with relatively low density sitting on top of the higher density mantle rocks. The thickness of the crust is important for investigations of earthquake hazards, and the growth and evolution of continents through time. Energetic waves caused by earthquakes travel faster through mantle rocks than crustal rocks. Seismologists typically observe a sharp change in wave velocities at a boundary named Mohorovičić discontinuity, abbreviated to Moho, and has been interpreted as an abrupt boundary between the crust and the mantle. Since the crust-mantle boundary is too deep to be observed directly via methods like drilling, such seismic imaging is the primary method to observe it. However, rare surface exposures of crust-mantle boundaries and fragments of rock brought up in volcanic eruptions suggest that the transition from mantle to crustal rocks may be more gradual than suggested by the sharpness of the Moho. This could mean that the seismically observed Moho may sometimes instead be a boundary within the crust and does not represent the crust-mantle transition. This project combines several types of seismic waves to characterize the seismic wavespeeds above and below the seismic Moho and compares them to calculated seismic wavespeeds for different rock compositions to assess the sharpness of the Moho and its relationship to the crust-mantle boundary. The investigation is carried out using already collected seismic recordings across the contiguous U.S. and Tibet, which provide contrasting geological settings. A more accurate characterization of the boundary provides clues to the evolution of continents and distribution of critical elements as well as improved constraints for calculations requiring crustal thickness. Anticipated results are of interdisciplinary interest for tectonic processes of continental crust formation and evolution and will be disseminated in peer-reviewed journals, at conferences, and via an interactive map and calculation tool product. Funding supports a graduate student and all-early career and/or soft money PIs; funds and mentoring for an undergraduate student at CU Boulder who will present their research project at AGU, a high school intern working on the project through the Simons Summer Research Program and a summer undergraduate intern, both at Stony Brook University. The crust-mantle boundary is seismically defined by the Mohorovičić discontinuity (Moho), interpreted as separating shallower seismic velocities representative of continental crust lithologies and higher velocities typical of ultramafic mantle lithologies. Only in the idealized case does the seismic Moho correspond to a petrological Moho juxtaposing crustal against mantle lithologies. Recent research produces paradoxical features such as a brighter Moho indicative of a large velocity contrast in hotter areas where one expects a smaller velocity contrast. Furthermore, sub-Moho velocities beneath large continental portions are significantly slower than expected. These observations could be explained by anomalously fast lower crust, crustal and mantle compositional effects such as hydration, or a diffuse petrological Moho. The proposed research aims to test these hypotheses by interpreting these paradoxical observations through a systematic combination of surface wave tomography, receiver function analysis, and petrophysical modeling. Accurately constraining the depth, width, and physicochemical state of the crust-mantle boundary is important to the transfer of stress from the mantle to the surface, topographic support, inversion constraints in seismology, and the composition and structure of the crust. The proposed work is to combine surface waves and receiver functions with complementary sensitivities to determine 1. Shear velocity contrast at the Moho; 2. Absolute shear velocities in the lower crust and uppermost mantle; 3. Moho depth and character, all at existing seismic stations with available data under the contiguous U.S. and Tibet. Method development includes modeling of absolute receiver function Moho amplitudes with near-surface corrections and reduction of the nonuniqueness of gradients near the Moho in surface wave inversions by adding receiver function constraints. The scientific product from this proposal will be an interactive map and tool to calculate and display tradeoffs between shear wave velocity, temperature, Moho depth, and compositional structure, in agreement with geophysical data beneath each analyzed seismic station in the contiguous US and Tibet. This product will elucidate the tectonic evolution of continental lithosphere from the Archean to the present as well as provide improved constraints and parameterization necessary for future geoscience studies on the continental lithosphere. 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 internal dynamics of molecules after absorbing light plays a fundamental role in many aspects of our lives, including vision, solar energy harvesting (e.g., photosynthesis and solar cells) and in the protection of DNA from ultraviolet radiation (UV photoprotection). These dynamics consist of a complicated interplay between the electrons and the nuclei, whose motions are strongly coupled and cannot be treated independently. As the dynamics are governed by the rules of quantum mechanics and involve many coupled particles, they are very difficult to calculate accurately. Furthermore, because they take place on ultrafast time scales [10^(−15) s], and on very short length scales [10^(−10) m], they are very difficult to observe and follow experimentally. In this work, the PI and graduate students carrying out the research will develop experimental approaches to follow the ultrafast dynamics of electrons and nuclei in small molecules following the absorption of light. They will use temporally shaped ultrafast pulses, and advanced charged particle detection techniques, as an ultrafast quantum camera to take “pictures” of the molecules and produce movies of the coupled electron-nuclear dynamics following the absorption of light. The work will contribute to the training of the next generation of scientists, and ties directly to the 2023 Nobel prize in physics for the development attosecond pulses and the study of electron dynamics. Octave spanning ultra-broadband laser pulses will be generated using nonlinear optical techniques (self-phase modulation in stretched hollow-core fibers) and shaped using an acousto-optic, modulator-based, frequency-domain pulse shaper. This approach to pulse shaping allows the PI and graduate students carrying out the measurements to produce pulses which are short enough to capture the dynamics as they unfold and to be sensitive to the detailed motion of the electrons. By measuring the energy and direction of electrons and ions produced by the interaction between the molecules and pairs of shaped laser pulses, the PI and graduate students can gain detailed insight into the evolution of the molecule—i.e., how the electrons and nuclei move after the molecule absorbs light. The measurements will be compared in detail with approximate calculations of the dynamics in order to develop better models and a more comprehensive understanding, which will ultimately lead to the development of improved light harvesting and energy conversion technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Giant Molecular Clouds (GMCs) are huge concentrations of gas that are formed via the attractive force of gravity. Throughout cosmic time, the gas in GMCs has been converted into stars. This process is still under way, and we can see ongoing star formation within GMCs in the Milky Way and nearby galaxies. This research program investigates the evolution of molecular gas and GMCs in twelve nearby galaxies using the Atacama Large Millimeter/submillimeter Array (ALMA). It will study how the GMCs form and evolve and how they start forming stars in the galaxies. This program also integrates research and education by involving graduate and undergraduate students in the research activities, with a focus on recruiting students who discover a passion for science later in their academic careers. This program is built on the ALMA-FACTS survey (FundAmental CO 1-0 Transition Survey) of 12 nearby spiral galaxies. FACTS studies the evolution of molecular gas –by means of the systematic variations in the CO J=2-1/1-0 line ratio (R21) –on GMC scales within and among the galaxies. The galaxies are selected from the legacy surveys with a variety of telescopes (ALMA, HST, JWST, Spitzer, and Herschel), which provide rich ancillary data. FACTS utilizes the existing high-resolution CO(2-1) data of the same set of galaxies from ALMA, observing the same regions at the same physical resolution and mass sensitivity. Historically, CO(1-0) has been the yardstick of molecular gas observations, while CO(2-1) is now becoming a new standard due to its easier detectability. FACTS will characterize any systematic effects in R21 and bridge the gap between the historic CO(1-0) observations and the new standard. The main scientific goals are (1) to study systematic variations of R21 (i.e., the evolution of molecular gas) on GMC scales, in arms and interarm regions, bars, and from the centers to outskirts within/among galaxies, (2) to study the environmental conditions for SF at various stages, including GMCs on the verge of SF (i.e., having high R21, but no SF), and (3) to assess the capabilities and limitations of CO(2-1) with respect to CO(1-0), as CO(2-1) is becoming the new standard for tracing bulk molecular gas in galaxies. 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

X-ray absorption spectroscopy (XAS), measured at national Synchrotron user-facilities, is a valuable tool for non-destructively characterizing the chemistry of an element within a geologic or environmental sample, as well as on bulk or in micro- or nano-scale samples. The methods has a broad range of applications including ore mineralogy for critical materials, geochronology and tectonics, geologic carbon cycling, nutrient and contaminant cycling in Earth’s Critical Zone, climate change and paleoclimate research. XAS studies rely heavily on matching resulting spectra from individual sample analyses with those of reference samples, or with systematic studies of spectral features in a suite of materials. However, there is limited availability of reference data and individual experimenters often have to repeat others’ measurements. This project will assemble a large quantity of previously measured XAS analysis results, most of which are unpublished and would otherwise remain inaccessible. Making this library available in an online database will greatly improve efficiency and productivity of future research, particularly given the limited capacity of synchrotron facilities. Broader impacts will be realized by making this database open and available based on FAIR (findable, accessible, interoperable and reusable) principles. Database spectra for training and for data mining efforts -- without requiring travel to a synchrotron facility -- will benefit underrepresented and/or disadvantaged communities, as off-line training on XAS techniques and data analysis broadens participation and lowers entry barriers. Remote researchers will also be able to systematically explore new dimensions of XAS data and conduct integrative experimental-theoretical research including approaches utilizing Artificial Intelligence and/or Machine Learning methods. This project will include the curation, calibration and ability to quality-check spectral data (and associated metadata) for a particularly important subset of XAS covering lighter elements Silicon, Phosphorous, Sulfur, Chlorine, Potassium and Calcium. These elements are measured using the “tender” energy range, X-rays of 1.8 to 5 keV delivered by only a few specialized synchrotron facilities worldwide. The team has specific expertise in this area, coupled with direct access to unpublished measurements made during commissioning and operation of some of these facilities. Much of this data would be lost to the community without intervention. The project will also lead to the creation of an open online database of spectra of these elements in order to make them available to the full Earth and Environmental sciences research and education community. Spectral data format and well-defined metadata parameters are ideally suited for machine-searchable databases. Additionally, the project will make possible the development and publishing of systematic and comparative studies that are enabled by the database. Finally, this project will allow for the long-term continuation and potential expansion of this resource beyond the term and scope of the pilot project, through partnership with larger organizations such as SEES (Synchrotron Earth and Environmental Sciences), EarthChem (hosted at the Lamont-Doherty Earth Observatory) and MSA (Mineralogical Society of America, whose Mineral Structure Database is an extremely successful online resource). 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

Large and robust quantum computers have the power to considerably alter many aspects of society and economy, ranging from science, medicine, and engineering to manufacturing, logistics, and finance. Such quantum systems appear plausible only as a network of smaller quantum computers. Quantum Networks also enable secure and privacy-preserving long-distance communication, unlike classical systems. This project aims to build a 10-node quantum network called SCY-QNet, connecting quantum computers at Stony Brook, Columbia, Yale, and Brookhaven National Laboratory. SCY-QNet is intended as a user-configurable shared infrastructure for scientists and engineers to develop and validate research and experiments in quantum communication and computing. The key scientific challenges in building SCY-QNet include developing robust quantum processing units and quantum memories, and quantum communication infrastructure, including free-space optical quantum links. The project aims to demonstrate the unique capabilities of quantum systems in secret key sharing, and quantum communication, simulation, and computation. The goal of the workforce development activities in the project is to create a diverse quantum-smart workforce capable of propelling quantum-driven economic development. Success in this project will pave the way for the construction of quantum computing platforms that can enable applications infeasible with classical systems. The key scientific challenges in building SCY-QNet include developing robust heralded quantum memories, efficient quantum repeater systems, and QPUs with efficient in-out qubit-photon coupling. The project has six research components: (i) Developing banks of heralded quantum memories with sufficient storage times and retrieval efficiency. (ii) Developing robust quantum repeater systems, including high-rate entanglement sources and efficient swapping devices. (iii) Developing quantum processing units (QPUs) with atom-based qubits that facilitate high entanglement rates with atom-based memories. The project aims to develop these by integrating compact optical cavities that enable efficient coupling of photons. (iv) Developing high-efficiency quantum frequency conversion units to bridge the frequency difference between quantum memory banks and QPUs, as well as converting the infrared photons from memories to telecom photons to transmit entangled photons. (v) Developing quantum network communication infrastructure, including the development of free-space optical links for transmitting qubits over long-distance, and developing a network stack with software-defined control modules that orchestrate the generation and distribution of entanglements. (vi) Demonstration of quantum advantage via a series of experiments, including secret-key sharing protocols, remote entanglement distribution, distributed simulation of quantum physical systems, and distributed computation of quantum algorithms that are known to have quantum advantage. 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.

NSF Awards · FY 2024 · 2024-08

Silent speech interfaces are systems that do not rely on voiced speech signals for speech communications. Such interfaces have long been pursued to help people with voice disorders caused by diseases, laryngectomy, accidents, vocal abuse, or aging. Existing silent speech systems face several challenges, including bulkiness, obtrusiveness, immobility, and poor robustness against interferences. The lack of a truly efficient and unobtrusive system has precluded its continuous use and wide-scale deployment. This project aims to develop an efficient, skin-friendly, mechanically/visually unobtrusive, and personalized silent speech interface. Skin-attachable sensors are designed to track muscle activities induced by speech. Novel machine learning methods are developed for converting the sensed signals to spoken speech and for customized silent speech recognition. The developed hardware-software platform will impact broad fields, such as rehabilitation, healthcare, motion tracking, robot control, and human-machine interactions. For example, the developed system can help restore spoken communication for people with voice disorders. The system provides a more natural input method than touch and gesture to interact with smart machines, such as cellphones, computers, prosthetics, robots, and virtual/augmented reality. The system is particularly useful when private information delivery is needed, a quiet environment is desired, or voice-based speech signals are compromised by underwater or noisy environments. In addition, emerging techniques from this project will be adopted to develop education modules, demonstrations, or lab components to enrich classroom teaching, capstone design projects, and K-12 outreach activities. The development of the proposed customizable and robust platform that captures and interprets speech-relevant muscle activities for silent speech recognition is carried out through three specific research tasks. First, unobtrusive electromyogram sensing electrodes for tracking speech-relevant muscle activities will be designed and characterized. Second, machine learning algorithms based on deep Gaussian process networks will be explored to decode sensing signals for speech recognition. Finally, machine learning methods will be developed to assist personalization and optimization of the sensing array. This project will yield new knowledge about the design of biopotential sensors with good sensing capability while addressing the issues of user-friendliness. The developed machine learning methodology is general and can easily be extended to other domains beyond speech recognition. The optimization of sensor layouts for each user will allow for customized systems that accommodate the unique muscle movement patterns of each user and maximize speech recognition accuracy. 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 award provides travel support for undergraduate student participation in the Symposium on Undergraduate Research. This Symposium will be held at the annual meeting of the American Physical Society Division of Laser Science (DLS), in conjunction with the annual Frontiers in Optics (FiO) meeting of Optica. The conference program includes many of the research topics central to Atomic, Molecular, and Optical Physics. The support of students through this award makes a substantial contribution to the education and training of future scientists. Students who graduate with a background in laser science acquire a broad range of knowledge and skills that enable them to contribute to progress in many areas of science and technology. The meeting is scheduled to be held in Denver, CO on September 23, 2024. It offers an opportunity for undergraduate students to present their research results and to interact with senior scientists primarily from the United States, but also the broader international community. Support is provided only for US students (students enrolled in US 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.

NSF Awards · FY 2024 · 2024-08

Protons are one of the basic building blocks of all matter around us. Even 100 years after their discovery, they are still not fully understood. In fact, even the size of the proton has been a puzzle for over a decade now, after an extremely precise measurement in 2010 found a value much smaller than and in strong disagreement with previous determinations. The research supported by this grant aims to resolve this “Proton Radius puzzle”, which has found widespread interest beyond the physics community. The award supports the PI’s activity in the Muon Proton Scattering Experiment (MUSE) at the Paul Scherrer Institute in Villigen, Switzerland, which will provide crucial data to resolve the puzzle, and to determine the shape of the proton. The experiment will check if electrons and their heavier cousins, muons, interact in the same way, and will test our understanding of the scattering process by comparing electron and positron scattering. The work of undergraduate and graduate students as well as postdoctoral researchers is central to these efforts, and they will gain knowledge and experience in precise nuclear physics experiments, modern detector and accelerator techniques, as well as in simulation and analysis algorithms. This international research project will expose them to researchers from all over the world, fostering cultural exchange and giving them the opportunity to develop their communication and leadership skills. The award further supports the group’s involvement in various outreach programs with a focus on high-schools and underrepresented groups. MUSE will make use of the worldwide unique beam available at the Paul Scherrer Institute to measure the lepton-proton cross section. It is the first measurement of muon-proton scattering that is precise enough to address the 4% effect at the heart of the proton radius puzzle. Through the measurement of muons of both charges, as well as electrons and positrons over a wide kinematic range, MUSE will provide crucial data to verify the existing e-p scattering results, test radiative corrections including two-photon-exchange by comparison of the two charges, and search for a violation of lepton universality by comparison of the two lepton families. Dr. Bernauer’s group will play a crucial role in the development of the software and the analysis of the data and support the data taking efforts with shift work. 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 will produce new fundamental knowledge to understand and control the physical adhesion and aggregation of nanomaterials in liquid by considering the effects of so-called solvent-induced interactions between nanoparticles and surfaces with nanoscale topography. Solvent-induced interactions are a class of nanoscale interactions that occur because liquid molecules rearrange themselves when confined in nanoscopic spaces between solid surfaces. These interactions significantly influence how nanomaterials cluster together and adhere to surfaces. This research will improve our ability to predict and control these behaviors, which is crucial for creating accurate models for the toxicity and environmental fate of engineered nanomaterials. The sought knowledge is also vital for advancing technologies of strategic importance such as environmental remediation, colloidal filtration and water treatment, nanomaterial self-assembly, and energy storage. A new educational initiative named “The Green Nanotechnology Forum” will be created for this project as a hybrid (in-person/virtual) workshop and a forum with an online open-access repository to inform the public and research community on the potential toxicity and environmental fate of nanomaterials commonly employed by different industries, and emerging green nanotechnologies for environmental sustainability. The research and educational activities in this project are tightly integrated and have a strong focus on broadening the participation of underrepresented groups and workforce development in STEM. Although solvent-induced interactions are known to dominate at nanoscale separations, current standard approaches for adhesion and aggregation solely consider classical Derjaguin-Landau-Verwey-Overbeek interactions (i.e., van der Waals and electrostatic forces) in a perfectly uniform liquid and between sharp and smooth interfaces. The primary goal of this project is to address this significant limitation by formulating and validating a material-agnostic approach to predict and control the physical adhesion of general nanoparticles smaller than 100 nm dispersed in liquid by considering the role of solvent-induced interactions such as the oscillatory structural and hydration force. A secondary objective is to test the mechanistic hypothesis that surface features such as the nanoparticle facet/crystallite size and nanostructure contact area control the magnitude of solvent-induced interactions and thus can prevent (or promote) adhesion and aggregation by inducing (or suppressing) kinetic trapping at secondary energy minima for which the nanomaterial mobility is retained. These objectives are pursued through two interconnected aims: (1) Theoretical formulation of a mean-field model for nanoparticle adhesion/aggregation, and (2) Experimental characterization of nanoparticle adhesion on surfaces with a well-characterized interfacial energy and physical nanostructure. The research plan combines theoretical work, molecular dynamics simulations, and hypothesis-driven experimental analyses, to characterize and rationalize solvent-induced interactions and adhesion rates in aqueous media, primarily for metal oxide nanoparticles and nanoplastics on hydrophilic/hydrophobic surfaces with natural or synthetic nanostructures with controlled geometry and dimensions ranging from 10 to 100 nm. Metal oxide nanoparticles and nanoplastics are of primary interest because they are among the most common nanomaterials employed in industrial applications and released into the environment, although their fate in aqueous media remains poorly understood. Undergraduate and graduate students supported by this project will create research reports and video presentations for The Green Nanotechnology Forum that will be created for this project to inform the general public on the environmental faith of common nanomaterials and support the selection of specific nanomaterials to be studied for this project. 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

The purpose of this project is to study several problems in general relativity, using the tools and techniques of differential geometry and mathematical analysis. General relativity is a geometric theory of gravity proposed by A. Einstein over a century ago. It is fundamental to our understanding of the large scale structure of the universe, and has many practical applications such as to the fine tuning of global positioning system (GPS) technology. Within this theory, singularities are known to generically form in the evolution of spacetime. A central conjecture due to R. Penrose, which is tied to the predictive power of general relativity, asserts that these singularities must be hidden behind the horizons of black holes. One of the main objectives of this project is to investigate this conjecture, known as weak cosmic censorship, by establishing a range of geometric inequalities relating total mass of a spacetime to properties of the black holes it contains. This project will also involve a significant impact in the educational arena at all levels, through the training of at least five Ph.D. students under the PI's supervision to the development of STEM talent in high schools by mentoring local students' research projects and science competition entries. The PI will develop ideas surrounding a new proof of the positive mass theorem based on level sets of spacetime harmonic functions. This new quantitative approach suggests methods to complete the stability program, and establish more general mass lower bounds. With collaborators, the PI will continue investigations of the harmonic maps with prescribed singularities which naturally arise from the stationary Einstein equations under symmetry conditions, and use this theory to find new black hole solutions with exotic topologies. This work also suggests a novel method to construct Riemannian Einstein metrics, as well as a possible classification for stationary axisymmetric black holes. Recently, together with Alaee and Yau the PI has introduced a new quasi-local mass with advantageous attributes, and will fully explore its properties with the goal of applying it to the trapped surface/hoop conjecture associated with gravitational collapse, as well as the proposed Bekenstein bounds concerning the entropy/information within a relativistic body which has implications from thermodynamics to computer science. Additionally, this project will advance new interactions, and encourage interdisciplinary efforts between pure mathematics and 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 2024 · 2024-07

Nucleons are made of quarks and gluons tied together by strong interaction and governed by quantum chromodynamics (QCD). At the hadron scale, quarks are "dressed" with gluons and generate 99% of the nucleon mass. In this regime, only numerical calculations on a lattice can reliably reproduce the nucleon masses, internal distributions of charge and magnetization, and the rate of neutron beta-decay. Based on fundamental theory, lattice QCD predictions will be valid at any scale from hadrons to quarks and vital to our understanding of the internal organization of nuclear matter, in particular the transition from phenomenological hadron models to perturbative QCD at short distances. This transition will be explored in this project in three ways. First,the nucleon electromagnetic form factors will be computed in a wide range of high momentum to allow comparison with recent experiments at JLab/CEBAF. Second, quantum entanglement of gauge degrees of freedom will be examined in a color flux tube, which is the most basic ground state binding a quark and antiquark. Finally, the effect of certain charge-parity-violating interactions on the neutron electric dipole moment will be studied, yielding a window from nuclear physics into new particles and fields. Protons and neutrons, which constitute the nuclei of all elements, are themselves composite objects. It has been firmly established that they are built from strongly-bound elementary quarks and gluons. However, this picture is not complete without understanding how the latter are kept in such compact arrangements, especially surprising because they are nearly free at short distances. This project will use fundamental theory to explore the structure of quark-gluon bound states (hadrons) that are central to nuclear physics. First, electric charge and magnetization distributions in the nucleons will be computed with the highest possible resolution. These calculations will accompany recent measurements at Jefferson Lab and help understand how quark-gluon interaction changes at shorter distances. Second, quantum information and entanglement in gluon fields between a quark and antiquark will be studied. Such gluon fields are known to form a so-called color flux tube emerging when one of the quarks is pulled out from a hadron. Finally, the study of neutron structure is vital to detecting effects of yet undiscovered particles. Some of them may be revealed in measurements of electric dipole moments and further improve our knowledge of elementary particles, which presently lacks explanation for dark matter and neutrino masses. 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 · 2024-07

The DISNET (Disability Inclusion Support NETwork) project establishes a virtual platform to connect NSF-funded teams conducting disability research, STEM students with disabilities, disability organizations, and community members with disabilities. This network seeks to address the persistent underrepresentation of people with disabilities in STEM education and careers nationally. DISNET uses research and insights from surveys, interviews, focus groups, and advisory board recommendations to develop a collaboration portal that supports the network partners and enhances their collective impact. The virtual platform will include materials and tools based on best-practices for the meaningful involvement of students with disabilities in STEM and facilitate networking among researchers and practitioners. The project aims to expand the pool of scholars who conduct disability-related research, and it meaningfully involves people with disabilities in these directing these efforts. DISNET connects NSF-funded projects and their scholarly teams in the State University of New York (SUNY) system into a cohesive network of research labs across five institutions. An important component of the project is the purposeful and intentional collaboration with the broader disability community including leaders from disability organizations to inform the development, implementation, and evaluation of DISNET. The project adopts three strategies for meeting its goals: (1) an on-line portal (information, best practices, mentoring and collaboration tools), (2) workshops (knowledge exchange, skill development, and the refinement of network strategies), and (3) engagement (advisory boards, focus groups and networking). The project advances knowledge by building on research that investigates the underlying factors that motivate the pursuit of disability research. The DISNET model could be expanded beyond SUNY, serving as a model for connecting researchers of other STEM education and workforce topics while ensuring the active involvement of important stakeholders. This project is funded by the NSF Eddie Bernice Johnson Inclusion across the Nation of Communities of Learners of Underrepresented Discoverers in Engineering and Science (INCLUDES) Initiative, which seeks to motivate and accelerate collaborative infrastructure building to advance and sustain systemic change to broaden participation in STEM at scale. 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 · 2024-07

This award provides participant support for the 9th International Clumped Isotope Workshop (ICIW), to be held at Stony Brook University, in Stony Brook, New York from August 25th-28th, 2024. The ICIW is a gathering of stable isotope geochemists connected by a shared interest in the study of clumped isotope geochemistry and other rare stable isotopes. The ICIW will be a forum for discussing consistency and quality of methods between laboratories, calibration using created and natural samples, and new scientific applications. As a smaller meeting than larger, society-organized conferences, the ICIW is held every 1-2 years and alternates between academic host institutions in North America and Eurasia. Participants range from undergraduate students to senior faculty. Research presentations given during the meeting will be a mix of conference-style talks and open format poster sessions focused on work led by graduate students, postdoctoral researchers, and other early career scientists. The 9th ICIW will closely follow the Goldschmidt Conference, organized by the Geochemical Society, to broaden both domestic and international participation. The 9th ICIW will consist of two and half days of technical sessions, with mornings and early afternoons reserved for thematically organized conference-style oral presentation sessions, and one afternoon poster session. A local and northeastern United States regional organizing committee, a majority of whom are early career faculty, are organizing the meeting logistics and designing the scientific programming. Topics to be covered include advances in analytical techniques, clumped isotopes of organic molecules and natural gases, paleoenvironmental reconstruction, diagenesis, and the extension of lessons learned from the clumped isotope community to other, rare isotope research communities (e.g., triple oxygen isotopes). This award will be used to cover conference costs for students and early career researchers. 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.