Carnegie-Mellon University

NSF Awards · FY 2024 · 2024-09

Foundation models have led to a paradigm shift in the areas of Natural Language Processing (NLP) and Computer Vision (CV). The result is that high-dimensional complex data from those domains can be used directly to realize machine learning (ML) and data science capabilities with very small amounts of labeled data, method development, and effort. This project will realize these benefits for threat detection and large-scale analysis of spatiotemporal data. Doing so will unlock the massive potential of existing data collection efforts, allowing for the fusion of disparate datasets, deeper understanding of complex threat dynamics, and enabling the rapid deployment of plausible analytic solutions to comprehend and handle various types of threats. This project will advance the AI/ML research community’s knowledge of the full range of capabilities of these large multi-task modeling strategies as well as contribute methods for scaling the proposed modeling strategies to very large, complex, multimodal datasets. Finally, the work will also advance knowledge of the dynamics and processes underpinning the evolution of threat in the demonstration applications. If successful, the project will enable new capabilities in urban planning, public safety, and environmental monitoring. It will also inspire and shape the next generation of data scientists and artificial intelligence experts through research mentoring and dissemination of results. This work will develop new foundation models and eventually generative Artificial Intelligence (AI) capabilities for spatiotemporal data, and is structured as three complementary research thrusts. The first will develop multivariate data representations for spatiotemporal processes. The second will focus on scaling models to very large datasets, by exploring specialized neural network architectures and developing highly expressive graph networks with temporal aspects, incorporating irregular multimodal spatiotemporal data. The third will develop methods for integrating and regularizing large models with existing highly scalable spatial detection algorithms such as fast subset-scanning. This will require ensuring that the latent spatiotemporal representations contain correct information and controlling the types of information extracted therefrom. 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

Nontechnical Description Near-infrared light lies beyond the limits of the human eye but is valuable for applications in medicine, telecommunications, and optical computing. Near IR luminescence is especially valuable for medical imaging as longer wavelengths of light penetrate deeper into biological tissues. However, few materials emit light in the near IR with wavelengths between 1 and 1.7 microns. This project explores ultrasmall gold nanoparticles, roughly a nm in diameter and with merely dozens of gold atoms each. Their desirable properties include high stability, biocompatibility, and near IR luminescence. The luminescence of these nanoparticles is very sensitive to the number of constituent atoms, requiring atomically precise control. By precisely controlling the number of atoms per particle, one can tailor the wavelength of emitted near-infrared light. This project aims to achieve high brightness and wavelength tuning of near-IR luminescence from atomically precise, quantum-sized gold nanoparticles though a combination of new synthesis methods and comprehensive optical characterization. The project will provide training opportunities for students to prepare them for future technical careers. The PI will actively work to broaden participation in STEM through outreach and broaden exposure of the research through conference presentations and a focused symposium on the topic of luminescent nanoclusters. Technical Description A myriad of new phenomena emerge in quantum-sized gold nanoparticles. These include quantized energy levels, single-electron transitions, and near-IR luminescence (700 – 1700 nm). However, the current quantum yields of luminescence from gold nanoclusters are still less than 5% and tuning of wavelength to longer wavelengths remains challenging. The vast majority of luminescent gold nanoclusters are still imprecise at the atomic level, and their structures are unknown, which precludes precise structure-property correlations. To tackle these challenges, this project aims to develop an atomically precise approach for gold nanoclusters and further devise effective strategies to enhance their near-infrared luminescence. By combing atomically precise synthesis of gold nanoclusters and spectroscopic analyses, the fundamental mechanism is to be mapped out. This will form the basis for devising strategies for the suppression of non-radiative pathways, thereby enhancing the efficiency of luminescence. Structural control of gold nanoclusters with atomic precision also allows for extending the luminescence wavelength into the second near-infrared window (>1 micron). The project aims to achieve definitive relationship between the atomic-level structure and the optical properties of gold nanoclusters, which could become a paradigm for the studies of other types of electronic and optical materials. The fundamental knowledge on manipulating the electronic excited states in metal nanoclusters may broadly benefit quantum science and technology. 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 (See instructions}: The goal for this project is to develop machine learning (ML) to accelerate the discovery of scalable, interpretable, and personalized preventative interventions for perinatal depression. Approximately 15% of pregnant individuals experience perinatal depression, which can have devastating long-term consequences. Suicide is a leading cause of death among new mothers in the U.S. However, individualized preventive interventions are not routinely offered at present due to lack of routine screening practices and limited resources. Machine learning offers an opportunity to improve mental health services during the perinatal period by identifying patients who would benefit from specific preventative interventions. We will develop fundamental advances in ML techniques for the discovery of personalized interventions as well as advances in the social science of incorporating domain and lived experience into algorithmic systems. Our specific aims bridge prediction with the adaptive experimentation needed to identify personalized interventions. In Aim 1, we will develop methods which use existing historical data to lay the groundwork for a randomized experiment of interventions, including to robustly inform which variables to measure and how to set an initial allocation policy based on those variables. In Aim 2, we will elicit domain expertise from clinicians and lived experience expertise from perinatal individuals via semistructured interviews which will inform both the requirements for a trustworthy and implementable ML system and a structured representation of clinical expertise that can be incorporated to initialize a ML policy together with historical data. Finally, in Aim 3, we will synthesize these products into an integrated framework for online learning to discover personalized preventative interventions. The key component of this framework is continued interaction with patients to provide intermediate feedback and accelerate convergence to a high-quality policy for allocating preventative interventions.

NSF Awards · FY 2024 · 2024-09

In this work, the PI will improve our understanding of aerosol concentrations and composition globally by leveraging ongoing air quality measurements at U.S. Embassies, which use Beta Attenuation Monitor (BAM) filter tapes to measure aerosol concentrations, with a focus on under-sampled regions of the globe (i.e., the Global South, which consists of Africa, Latin America, and developing countries in Asia). The BAM filter tapes will be collected and analyzed using photographic and laboratory-based measurement techniques. There are four main research questions to be addressed in this work: (1) What is the aerosol composition across the Global South? (2) How does aerosol mass and composition change over time across the Global South? (3) What are the major sources of aerosol in these regions, and do emission profiles differ significantly from those in the Global North? (4) How do models and satellite products compare to these ground-based datasets? These new datasets will enable better decision-making for affected communities around the globe. In addition, this work will develop a global network of stakeholders, including U.S. citizens working at embassies worldwide and air quality practitioners in multiple countries. The PI in this project will develop a new high temporal resolution, ground-based global dataset of aerosols from an under-sampled region of the globe utilizing existing datasets. Specific objectives of the work are: (1) Build a global network of embassy partners and local stakeholders, including atmospheric scientists and data users, to enable data analysis and interpretation worldwide; (2) Quantify concentrations and temporal trends of major inorganic ions (sulfate, nitrate, ammonium, chloride), black carbon, and organic aerosol at >15 embassy locations worldwide; (3) Determine aerosol sources at embassy locations and compare source profiles to those determined in the Global North; and (4) Evaluate performance of global models and satellite products using our new dataset. The methods will be validated using filter samples collected in both the laboratory and ambient air from a Pittsburgh, Pennsylvania air quality surface station. This work also will support and train undergraduate and graduate students in sampling techniques and data analysis. 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

Our brain's ability to instantly recognize an object within a visual scene is almost effortless, yet obtaining this ability in artificial visual systems has taken decades. This is because the brain's computations that transform a visual scene into a neural code remain hidden among the billions of neurons and synaptic connections that make up the human visual system. Identifying and understanding these computations is the first step in providing clinical diagnoses and treatments for diseases and disorders disrupting visual processing, ranging from transient motion sickness to neurodegenerative disorders such as posterior cortical atrophy. Such treatments may involve visual prostheses to replace or bypass damaged computations (e.g., those involved in motion processing or face detection). Decades of experiments and modeling have uncovered fundamental computations in early visual cortex (retina, LGN, V1), but our knowledge of spatial feature processing (shapes, textures, colors) and temporal processing (motion, changing perspective) in higher-order visual cortex (e.g., areas V4 and IT) remains limited. This proposed research program aims to characterize the neural computations involved in how visual cortical area V4 neurons respond to dynamic video clips. We will build a computational model that accurately predicts temporal V4 responses and interrogate this model to isolate the model circuits that govern the temporal integration of visual features. To optimize the parameters of our deep neural network model, we will combine data collection and model training in a closed loop: We train our model after each recording and choose the next video clips to present based on the model's uncertainty. In other words, we keep refining our working hypothesis---a deep neural network model---through model-guided data collection. The result of this procedure will be a large-scale dataset of temporal V4 responses to natural video clips as well as a highly-predictive computational model. We will use this model to test whether feature attention dynamically modulates V4 responses, linking temporal feature integration to behavior. Overall, this innovative closed-loop approach, requiring close interdisciplinary collaboration between experimental and computational researchers, promises to unlock the neural computations involved in spatial and temporal feature processing in higher-order visual cortex.

NSF Awards · FY 2024 · 2024-09

Electrically charged objects move when exposed to an electric field: this phenomenon is termed electrophoresis. Most research in this field has focused on electrophoretic motion in small-amplitude electric fields, where the velocity of the object is linearly proportional to the magnitude of the electric field. In contrast, this project will quantify the nonlinear electrophoretic motion of microscopic-sized charged particles immersed in an electrolyte solution under a large-amplitude field. A novel technique called Non-Antiperiodic Nonlinear Electrophoresis (NANEP) is proposed to enable net electrophoretic motion of such particles under an alternating (ac) field. Experimental measurements and computational modeling will be employed to provide proof-of-concept for NANEP. The ability to affect net electrophoretic particle movement through NANEP could lead to new methods for hierarchical assembly of particles. Further, it is envisioned that NANEP will provide the scientific foundation for new separation schemes for biomolecules in lab-on-a-chip microfluidic devices. The project will also have broader educational impacts including course development, undergraduate research experiences, and outreach activities. The project will quantify the Non-Antiperiodic Nonlinear Electrophoresis (NANEP) of micro-scale colloidal particles via experiments and computations. The first objective is to predict NANEP via numerical solution of the nonlinear electrokinetic equations governing fluid flow, ion transport, and electrostatic fields in electrophoresis. The numerical scheme will employ a custom spectral element code with adaptive time stepping, which will enable efficient, accurate computation of NANEP across a wide range of the experimentally relevant parameter space. The aim is to determine which regions of this space result in the maximum net particle movement under NANEP. The second objective is to experimentally measure electrophoretic particle migration during NANEP. This will be accomplished by observing particle motion in a microfluidic channel under a non-antiperiodic field. Importantly, the results from these two objectives can be directly compared in an essentially parameter-free manner. The proposed research will provide the first computational predictions and experimental measurements for NANEP of colloidal 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 · 2024-09

Part 1 Under what conditions do states surveil and censor their citizens? How are the two tactics related to each other and other forms of repression and control? To what extent have states concealed their use of these tactics? States have increasingly wielded surveillance and censorship, both digital and physical, as tools of political influence at home and abroad. Yet, there exist scant theoretical and empirical advances to help understand these phenomena. Consequently, scholars know little of how and when states employ these levers and how their use has evolved with technological advancements. To address this critical knowledge gap, the investigators produce, analyze, and disseminate a novel dataset, the Global Surveillance and Censorship Scores (GSCS) database. The project utilizes mixed methods to collate quantitative and qualitative historical and contemporary data, including a diverse set of existing human rights reports. The resulting dataset allows academics, practitioners, and policymakers to advance the study of human rights and repression. The project has key implications for American national security and policy, ranging from finance and healthcare to human and drug trafficking, which have been affected by surveillance and censorship practices. Part 2 Surveillance and censorship are key levers of power to control information. States have increasingly wielded them, digitally and physically, to compete for political influence at home and abroad. Yet, scant theoretical and empirical advances exist to help understand the phenomena. Consequently, we know little of how and when actors employ these levers, and how their use as repressive techniques evolve with technological advancements in the 21st century. The investigators use mixed-methods to collect quantitative and qualitative historical and contemporary data to develop the Global Surveillance and Censorship Scores (GSCS) database. Information is extracted on surveillance and censorship from a diverse set of existing human rights reports and other documents. Given the clandestine nature of surveillance and censorship, a latent variable models developed to address missing information and to assess the sensitivity of the model estimates to understand the extent to which states conceal the use of such tools. This Bayesian latent variable model is able to predict cases of missing information and aggregate information into country-year estimates. To further address bias in the reports, the investigators also conduct case studies using expert information, interviews, and archival research to validate the data. The project will allow researchers to generate new theories and empirical evidence to advance the study of human rights, censorship, and surveillance. The data and analytic deliverables will serve as a public good for the community of academics, practitioners, and policymakers. Surveillance and censorship increasingly shape geopolitics by controlling and manipulating information; understanding the evolution of censorship and surveillance can thus contribute to the development of sound foreign and security policies. 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 Foundational Research in Robotics (FRR) project will develop the world’s smallest functional bipedal robot, multi-legged robot, and wheeled robot. It will be a hundred times smaller by mass than the current state of the art, developed by studying the scale-dependent differences in actuation, mechanics, and contact forces. Specifically, this new family of robot designs will include the first bipedal robots capable of walking, turning, and stepping onto obstacles at sizes below 1 gram. It will also be the first fully controllable multilegged robots at the 10mg scale, and the smallest externally actuated wheeled robot at the 1mg scale. Small mobile robots are useful for accessing tight, confined spaces, including for industrial inspection, micromanufacturing, and exploring rubble. However, existing small robots have not been able to locomote nearly as well as larger robots. The sensing, actuation, computation, and manufacturing techniques available are more limited than at larger scales. Small systems have relatively higher friction, damping, and other losses that they must overcome. These differences and limitations will affect different robot morphologies in different ways, and so it is not clear that the same locomotion strategies that work well for large robots will scale down to smaller ones. A learning module called the "Robot Zoo" will take advantage of the wide variety of robot shapes, sizes, and locomotion schemes used in this study. This module will be integrated into existing K-12 outreach programs at Carnegie Mellon University. In order to achieve new robot designs and capabilities, the project will develop a set of scaling laws that describe how components and systems behave at different size scales, with a focus on actuation, joint losses, and contact properties. These scaling laws will be used to determine how designs will have to change at smaller sizes, e.g. how large the actuators must be to overcome the additional losses, as well as what design strategies will be most efficient, e.g. comparing different joint structures. These results will be used to make new, smaller designs for bipedal, multilegged, and wheeled robots with improved capabilities in terms of speed, efficiency, and maneuverability. The project will also develop metrics to quantify the relative performance of different robot designs across a range of properties. These metrics will be used for comparative morphology to better understand the relative tradeoffs between different designs and, ultimately, answer the central question of this project: What is the best way to move at small scales? 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

Computer science education is increasingly critical for preparing well-trained professionals for the national economy and building a competitive workforce of the future. The emergence of generative AI provides an opportunity to improve computer science education by adapting the learning process to the needs and knowledge of individual learners. University of Pittsburgh, Carnegie-Mellon University, University of Massachusetts, and North Carolina State University will develop and evaluate a comprehensive personalized programming practice environment (C-3PE) that utilizes artificial intelligence (AI ) to enhance learning experiences. This project capitalizes on the power of generative AI and progress in learning science research to provide personalized learning experiences for computer science students. C-3PE recommends the most suitable learning activities for each student according to their current knowledge level and offers personalized feedback to support their progress. By conducting long-term classroom studies, the project team will assess the impact of AI-based personalization approaches and identify the most effective types of learning activities and feedback messages for students with different competency levels. Leveraging advances in AI-driven learning technologies and theoretical frameworks in learning sciences, C-3PE will deliver engaging computer science learning experiences. It will provide personalized practice support and detailed feedback for individual learners based on their practice history and current knowledge state. C-3PE will dynamically model the state of learner knowledge using context-aware deep-learning knowledge tracing models. Furthermore, the project team will develop a nested personalization approach with an outer loop and an inner loop. For the outer loop, the project will develop new, large language model (LLM)-powered adaptive testing algorithms that select the most informative next practice question/worked example for each student. For the inner loop, they will use preference optimization to align LLM-driven feedback generation with student learning outcomes. A sequence of experiments will lead to a better understanding of the kinds of practice opportunities (i.e., worked examples vs problems) and types of feedback messages that are most effective to each student. Utilizing an iterative design process to integrate insights from studies into the learning environment, the project will evaluate C-3PE in various introductory programming classrooms across diverse institutions. The project will enhance education through personalized recommendations and feedback, disseminating findings and tools through academic conferences and platforms, and sharing C-3PE via a GitHub repository for computer science instructors. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. 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

There is a large demand for mid-infrared gas sensing technology in a broad range of biological, chemical, environmental, and industrial applications. Methane sensing and leakage control is an exemplary grand challenge in fighting global warming and climate change. Home health monitoring is another opportunity, for example monitoring ammonia in the breath of patients at risk of kidney failure. However, their large-scale adoption depends critically on the sensor’s portability, high resolution, low power consumption, and versatility. To date, there is no known solution that can deliver these performance metrics simultaneously. This project aims to develop a solution based on chip-scale quantum spectroscopy to solving the dilemma in mid-IR gas sensing, which exploits non-classical correlation in signal-idler photon pairs to enjoy high sensitivity and selectivity offered by the mid-IR spectrum while utilizing key resources in the near-infrared/visible bands for photon generation and detection. Specifically, the idler photon, which is sent to the sample for spectroscopy probing, is designed to be in the mid-infrared range while the signal photon, which does not interact with the sample under interrogation, resides in the visible. Because of the strong quantum correlation in such photon pairs, the amplitude and phase information of the idler photon gained from passing through the sample can be extracted by detecting the corresponding signal photon in the visible. As such, we can leverage the existing cost-effective infrastructure on key resources such as near-infrared laser diodes and silicon detectors, while relying upon low-power quantum processes in an integrated photonics platform to deliver superior performance in the mid-infrared sensing. We believe our research will significantly advance mid-infrared quantum spectroscopy, resulting in one of the most portable and versatile solutions available. Further, our CMOS-compatible manufacturing process holds the potential to reduce the cost by orders of magnitude, which is critical to the large-scale implementation of such gas sensors. The proposed work is centered on the development of a transformative technology platform that most effectively enables chip-scale quantum spectroscopy for mid-infrared gas sensing. A key innovation described herein – chip-scale quantum spectroscopy – enables the utilization of well-developed resources including lasers and photodetectors in the visible and near-infrared to attain unprecedented performance in mid-infrared gas sensing. Specifically, we will develop and integrate the following methods and technologies on the same chip: (a) Optimization of a novel silicon-carbide-on-aluminum-nitride integrated photonics platform for low-loss performance from the visible to the mid-infrared spectra; (b) silicon carbide nonlinear optics as an efficient source of quantum correlated mid-infrared and visible photons with large wavelength tunability and low optical power consumption; (c) Implementation of the wavelength multiplexing scheme to probe multiple fingerprints of one single gas or multiple gases; (d) compact gas sensors based on undercut silicon carbide waveguides that deliver high sensitivity and a large dynamic range; and (e) various filters and couplers that are an integral part of the sensing scheme, including pump notch filter, signal add-drop filter, dichroic coupler, and 3-dB (50:50) coupler, etc. Over time, platforms like this might be adapted to other reagent delivery modalities, like microfluidic analysis of chemicals in the liquid phase. This would open up a host of applications in blood chemistry monitoring and inexpensive in-home lab testing. Additional impacts are likely to accrue from the development of the component technologies as they are applied to other systems, including imaging technology, telecommunications, and lab on a chip application. 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 detection of gravitational waves (GW) has opened a new window for astronomy. GW astronomy holds the potential for helping reconstruct a picture of the universe at energy scales much higher (and at times much earlier) than the picture obtained using the cosmic microwave background (CMB). A research program between Carnegie-Mellon University (CMU) and Massachusetts Institute of Technology (MIT) will use gravity as a tool to understand more about high energy physics and cosmology, as well as numerical simulations of magnetohydrodynamic processes in the early universe. The project also includes a broad range of education and outreach activities. There will be training of graduate and undergraduate students at both CMU and MIT, along with a vigorous public outreach program, including programs with local middle and high schools, an astronomy training unit with local disabled American veterans, a summer Teacher Training Program, and direct community engagement. The investigators will continue to invite visual artists, astrophotographers, videographers, game designers, and writers to share their cultural arts network for the promotion of science. The LIGO/Virgo detection of gravitational waves has ignited interest in the future direction of GW astronomy, including the search for intriguing signals of stochastic backgrounds from early-universe physics. The NANOGrav collaboration announced detection of a stochastic GW background that can be understood as possibly including a signal from the early universe, such as GWs from and shortly after inflation, cosmic strings and domain walls, phase transitions, turbulence and magnetic fields. The detection of such GWs is challenging due to their small amplitudes, the specific range of the characteristic frequencies, and astrophysical foregrounds. The focus of this research is a study of the GWs from turbulent sources possibly presented at (or around) the quantum (QCD) energy scale. The team will evaluate detection prospects of these GWs, with particular interest in modeling parity violating (chiral) sources that might explain matter-antimatter asymmetry in the universe and investigating the range of the QCD epoch cosmological parameters that can be tested through the pulsar timing arrays. 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

Enhanced dissipation arises in many situations of physical importance, ranging from micro fluids to oceanography, and is even commonly observed when cream is poured into coffee and it mixes quickly when stirred but very slowly if left alone. This is the phenomenon by which the combination of stirring and diffusion increases the rate of convergence to equilibrium. This project plans to develop a theoretical understanding of enhanced dissipation, including quantifying this effect and producing criterion describing scenarios where enhanced dissipation occurs at the optimal rate. The methods developed will also be used to speed up sampling algorithms and are useful in scientific computation. In addition to enhanced dissipation, the project also involves the study of the formation of Bose—Einstein condensates in situations which are of interest in modern cosmology. Students and post-docs working on this project will be exposed to a broad set of fundamental tools in partial differential equations, probability, and scientific computation, positioning them to contribute to the ever-changing scientific landscape. Advection and diffusion are two fundamental phenomena that arise in a wide variety of applications ranging from micro-fluids to meteorology, and even cosmology. In many situations the interaction between advection and diffusion results in an increased rate of convergence to equilibrium -- a phenomenon known as “enhanced dissipation”. This project involves a quantitative study of enhanced dissipation, obtaining sharp bounds, determining criterion describing scenarios where it occurs at the optimal rate, and investigating its properties. The methods developed can also be used to speed up certain Markov processes and may improve rates of convergence of commonly used Monte Carlo Markov Chain algorithms. In addition to enhanced dissipation, the project will also study the formation of Bose--Einstein condensates in high temperature plasmas. This arises in cosmological applications such as the study of the interaction between matter and radiation in the early universe, the radiation spectra for the accretion disk around black holes. The project aims to classify mechanisms by which condensates form, prove convergence and stability of a numerical scheme, and study condensates in the three-dimensional versions. 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

Nontechnical Description: Artificial light with special properties plays a critical role in a number of technologies. Modern communication systems, for example, benefit from the special properties of laser light, which permit low-loss, high-bandwidth, long-distance transmission of information. Ordinary illumination benefits from use of light-emitting diodes in lamps that are brighter and more efficient. This project investigates yet another quest for special properties of light sources: single photon emission. It will investigate deliberately engineered defects in wide bandgap semiconductors; specifically, how to create them so that they behave as isolated single atoms capable of emitting single particles of light at certain chosen wavelengths. Such structures are useful for design of future single-photon emitters. The discrete or particulate nature of light endows it with unique quantum properties that make it useful as a building block for construction of certain classes of quantum computing devices, and for developing special cryptographic hardware for future ultra-secure communication and computing networks. In addition to contributing new knowledge, the project will serve as a training vehicle for a new generation of highly interdisciplinary engineers in quantum information science and technology. Technical Description: This effort proposes deterministic creation of atom-like defects in SiC and Al_x Ga_(1-x) N semiconductor films by focused-ion beam implantation at lithographically defined spatial locations. The objective is to implant elements such as vanadium, erbium, and others to form optically active defects that emit at the two low-loss telecommunication wavelengths of 1.3 and1.55 microns. Following post-implantation annealing, the samples will be characterized by rocking curve x-ray diffraction to assess crystallinity. Additional characterization will include spatially resolved micro-photoluminescence. Evidence for quantum light production will be derived from second-order autocorrelation of light emitted by the photoexcited defects. Preliminary feasibility experiments for electrical excitation in specially designed structures with defects in them will also be performed. 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

Partial differential equations (PDEs) are a ubiquitous modeling and analysis tool in both pure and applied mathematics and are used in biology, chemistry, quantum mechanics, and many other areas. In the recent few years, the synergy between PDEs and machine learning has dramatically strengthened. On one hand, machine learning methods, specifically neural networks, have been shown to be very useful for improving the process of solving PDEs---at both the level of representing the solutions of individual PDEs, and by capturing the mapping from a PDE to a solution. On the other hand, with the advent of diffusion models as the dominant approach to generative AI, stable, efficient and parallelizable solvers for PDEs are ever more important for training large-scale AI systems. This project will build mathematical foundations for several key questions pertaining to both the use of machine learning for PDE solving, and the use of PDEs as a tool for generative modeling. It will explore issues around the representational power of different neural architectures, their inductive biases, their statistical complexity, and their numerical stability. It will also aim to further elucidate the relative tradeoffs of different PDE-based generative models. The investigators will leverage their joint expertise in mathematical foundations of PDEs and generative modeling, as well as numerical aspects of optimization to fruitfully mine the rich overlap between PDEs and machine learning. 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

Society is increasingly dependent on software applications, systems, and platforms, as functionality in all aspects of business, government, and everyday life is becoming implemented in software-intensive systems. Whereas people and enterprises have been held accountable to laws, regulations, and societal goals and norms, when implemented through software, now software systems must also work in a way consistent with accountability goals. Software systems need to be designed with legal and regulatory compliance and social norms in mind. The U.S. National Science Foundation (NSF) Designing Accountable Software Systems (DASS) program is supporting research investigating the issues in achieving accountable software systems. Researchers on DASS proposals are a mix of Software Engineering researchers paired with PIs from the social and legal science. The purpose of the DASS workshop is to assess the state of the DASS program and the state of related research at large. In addition to reflecting on the successes and challenges of DASS-related research from the past, the workshop will focus on the emergence and expansion of DASS-like issues in the context of established research fields, such as safety in cyber-physical systems, fairness in artificial intelligence (AI) systems, and privacy and security in cyberspace. The workshop will help grow the DASS community of researchers and provide valuable insights into existing research challenges and future research directions in the field. By improving accountability in software design, the success of building this community can improve how members of society from across socio-cultural and economic backgrounds engage with services across banking, education, healthcare, and transportation, among others. Designing accountable software systems relies on theory and scientific principles from across the social and behavioral sciences, law, and computer science, including how individuals and groups make decisions, how bias affects decision-making, and how incentives motivate individuals. Legal theory that differentiates compliance from liability and legal processes that produce laws and adjudicate whether individuals and groups are compliant with law all affect how we define accountability. Finally, the technical means to support decision-makers and implement processes must be demonstrably correct and reliable. The software development processes used in practice must take these advances into account. A key challenge is how to nurture collaboration across these varied disciplines. The workshop will create an environment where ideas are presented, developed, discussed and synthesized to orient this cross-disciplinary community around a shared set of scientific objectives, each of which is originated and developed by one or more disciplines and supported by others. 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 serve the national interest by enhancing undergraduate student success in General, Organic, and Biological (GOB) Chemistry, which is an important gateway course for health-related careers such as nursing and dietetics. Improving student success in GOB Chemistry would help address predicted shortages of healthcare workers in the future and broaden access to these STEM careers. The project team will develop GOB course materials for both online and in-class active learning. The project will also grow a Community of Practice, including GOB instructors and allied health instructors, that will use the educational materials and contribute to their development. This will help insure that the materials and activities are coupled to allied health contexts and provide knowledge for student success in health professions. The online portion of the courseware replaces current textbooks and online homework systems with an integrated system that combines instruction with practice opportunities and checkpoint quizzes. Extensive hints and feedback will help students learn in an efficient manner. In-class instruction will be supported through a collection of active-learning materials set in allied health contexts. Data regarding student use of the materials developed will be used to drive revisions to the content that improve student learning and success. Improved student success in GOB chemistry courses may differentially support students from groups underrepresented in STEM since research shows these students are more likely to persist in STEM when they earn grades of C or better. The collaborative project team from Carnegie Mellon University and Mount San Antonio College will develop, implement, and evaluate online courseware and evidence-based materials and practices for General, Organic, and Biochemistry courses. The materials will include an emphasis on applications to careers in healthcare to maximize their relevance for students. The project team will also cultivate an instructor Community of Practice (CoP) to develop in-class course materials and iteratively refine the online courseware. The project team will assess the extent to which the online courseware engages and supports students' success in GOB courses, including the retention of students from groups underrepresented in STEM. Key questions include: What factors are most predictive of student learning and course success in GOB Chemistry and how malleable are these factors? To what extent do modifications to courseware based on learning data lead to demonstrable improvements in student learning and course success? How does situating GOB Chemistry in allied health contexts alter student learning and course success? To address the research questions, the project team will leverage the detailed data on student interactions gathered by the online courseware system. This includes "learning curves," constructed for each student and chemistry knowledge component, that describe students' progress with respect to each knowledge component. Such data, collected throughout the learning process, provide more detail than pre- and post-tests that assess learning at only two time points. In addition, data gathered throughout the entire semester enable analyses that examine student learning across a broad range of topics. These rich data will be combined with survey data describing student characteristics and classroom practices to address research questions related to the factors that most influence student learning and course success and the impacts of situating learning in allied health contexts. These data will also provide an evidence base that the CoP and courseware developers will use to drive iterative improvements to learning resources. Further, summaries of these data are shared with faculty members to inform their instruction, including in-class time and their one-on-one interactions with students. The courseware developed will be made available free for independent learners and about $25 per student when used (with additional supports) in academic courses. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Girls interact with advanced technologies every day, and yet only 20 percent of computer science degrees in the United States are awarded to women. This number has further decreased over the years, and when broken out by ethnicity, is much lower for racially minoritized girls. This issue is not solely due to lack of access, but due to the approaches used in computing education technology and pedagogy. These approaches can limit creative and joyful learning opportunities that are meaningful to the learning context in ways that would empower girls to understand and express their own identities with technology. This project focuses on robotics education for middle school girls from backgrounds which have been historically excluded in the field of computer science, with the goal of developing girls’ skills to use and create new technologies, and construct positive STEM identities. The project extends culturally-responsive computing pedagogies through exploration of co-creation, where youth are empowered as creators of a robotics technology, and simultaneously engage in reflective dialogue with the AI-enhanced robots they have created. Learners will be able to directly ideate on the societal implications of their technological creation and develop their skills to positively impact their community. Researchers will carry out design-based research, including three iterations of co-design and program deployment with a total of 140 girls, in collaboration with two community partners, one serves a majority Black population, and one a more racially diverse group. This project will contribute: 1) Co-creative technology design principles and how design choices can facilitate Culturally Responsive Computing, 2) Sensing and modeling of the dynamic collaborative context, and 3) An understanding of how the resulting principles, outcomes, and implementation guidelines might differ depending on the cultural backgrounds of the participants. A mixed-method design will be employed, utilizing the co-design and deployment data, and pairing quantitative learning analytics of log and interaction data with a qualitative coding of participant artifacts and interviews. Curriculum, technologies, and professional development materials from this project will be made available for public use, and workshops will be conducted that both provide specific instruction on the program and facilitate general knowledge sharing on how to create and sustain empowering informal learning programs. This Integrating Research and Practice project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences. This project is also partially funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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

In this project the PI will study various properties of random graphs/networks. Such objects arise frequently in economics, such as transport networks, as models of social media platforms and even as the relations between proteins in animal cells. These networks are highly complex and are best modelled as if they have been randomly constructed. Typically, there are computational problems associated with such structures. For example in routing trucks one is faced with the problem of finding routes that minimize some objective. The PI will study such problems within a stochastic framework. Graduate students and postdocs will be involved in the project. The PI will study the typical structure of combinatorial objects. He will study random graphs and hypergraphs and determine thresholds for the existence of various properties. There are still many unanswered questions about Matchings, Hamilton cycles and Spanning Trees in this context and the PI will seek to answer some of them. This will involve questions where the edges have weights and colors. The PI will also consider the algorithmic questions that arise if one wants to find algorithms that work well in the average case. In some sense, this is an attempt to explain why NP-completeness is not necessarily a barrier to obtaining results in practice. In particular, the PI will study the expected performance of branch and bound algorithms, one of the most successful general approaches to hard problems. Finally, he will study combinatorial games that arise: usually Maker-Breaker games where Breaker tries to foil Maker's attempts at achieving some objective. 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

Light-matter interaction plays a critical role in modern technologies, including solar cells, photodetection, and light-emitting devices. This interaction takes a new form in the atomically thin semiconductors, in which new particles combining positive and negative charges are created by light. Understanding and manipulating these particles could improve devices and even realize new functions that are not currently possible, such as power-efficient memory devices and quantum computing. Stacking different layered semiconductors together and tuning the layer-layer interaction could further engineer these particles and lead to new properties not feasible in conventional materials. The main objectives of this CAREER project are to explore and investigate the unique light-matter interaction and emerging properties in individual and stacked atomically thin semiconductors. The gained understanding can shed light on how to exploit this new light-matter interaction in confined space for future optoelectronics with better efficiency, faster speed, or even novel functions. The integrated education component trains the next generation workforce for science and engineering at the nanometer scale through research opportunities, curriculum development, and outreach activities, with a focus on encouraging the participation of women and underrepresented groups. Both existing programs at Rensselaer Polytechnic Institute and newly developed outreach programs will be utilized to encourage K-12 students to study in the field of advanced optical science and nanoscale technology. The emergence of two-dimensional semiconductors, especially monolayer transition metal dichalcogenides (TMDCs), ushers in unprecedented opportunities in exploiting the excitonic physics for quantum optoelectronics, while the understanding of intrinsic properties of the exciton is often hindered by the sample quality. By fabricating high-quality monolayer TMDC devices, this CAREER project aims to employ advanced optical spectroscopy techniques to study the unique light-matter interaction in monolayer TMDCs, with a focus on many-body physics that is critical for the exciton properties. The device and measurement configurations enable the control of doping, electrical field, and magnetic field, which provide additional tuning knobs for the spectroscopy study. Van der Waals heterostructure TMDCs devices with clean interfaces will also be constructed to investigate fascinating interlayer excitons, with the electron and hole residing in different layers. In addition, the twist angle of the hetero-bilayer TMDCs will be controlled to create a Moiré potential to further engineer interlayer excitons for emerging quantum states. The closely integrated research and education components provide training opportunities for graduate, undergraduate, and K-12 students on advanced optical spectroscopy, nanoscale device fabrication, and quantum materials. 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

With the support of the Chemistry of Life Processes (CLP) program in the Division of Chemistry, Professors Yisong Guo of Carnegie Mellon University and Wei-chen Chang of North Carolina State University will use biochemical and spectroscopic tools to investigate a group of four enzymes, which utilize a single iron and molecular oxygen (O2) to catalyze diverse while synthetically challenging chemical reactions to make valuable small molecules. These reactions have the potential to: (1) convert precursors to the building blocks of antibiotics or potential antimalarial agents; and 2) generate molecules that exhibit physiological functions in vision, light harvesting, and pathogen virulence, and some of them are linked to cardiovascular disease. The goal of this research program is to elucidate the fundamental knowledge on how these enzymes utilize iron and O2 to achieve diverse reaction outcomes. The obtained results will set the foundation for developing biocatalytic platforms to access these important chemical transformations to benefit modern society. The research goals will be integrated into educational activities to disseminate cutting-edge scientific results and research skills to graduate level, undergraduate level, and K-12 level students via course and outreach program development. Over the past decades, the mechanistic understanding of co-substrate-dependent (e.g., 2-oxoglutarate-dependent and pterin-dependent) non-heme mononuclear iron-containing (NHM-Fe) enzymes has been greatly improved. One key factor empowering these progresses is the identification and characterization of the common reactive intermediate (e.g., the S = 2 oxoiron(IV) intermediate) in co-substrate-dependent enzymes. However, key reactive intermediate(s) for co-substrate-independent NHM-Fe enzymes have not been experimentally verified, and it is still unclear regarding how O2 activation takes place without the involvement of a co-substrate. In this research program, the Guo and the Chang groups will work as a team to reveal detailed reaction mechanisms of four newly discovered co-substrate-independent NHM-Fe enzymes via the identification and characterization of iron-based reactive intermediates and reaction outcome analysis. These enzymes exhibit diverse catalytic activities including carbon-carbon bond formation and breakdown, and carbon-nitrogen bond formation. To achieve the research goals, an integrated research approach, consisting of substrate probe design and synthesis, transient enzyme kinetics, electron paramagnetic resonance (EPR), Mössbauer, and X-ray absorption spectroscopy (XAS) will be used. The use of this suite of tools could lead to a comprehensive understanding of the factors controlling O2 activation and reaction outcomes at a molecular level, which would fill the crucial knowledge gap in the understanding of NHM-Fe enzyme catalysis beyond the co-substrate dependent enzymes. 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 · 2024-08

Project Summary/Abstract Naturalistic contexts provide the opportunity to study the brain and behavior in response to the ethological problems an animal is evolutionarily designed to solve. We seek to expand the capabilities of our current behavioral segmentation approaches to provide a more precise and comprehensive account of behavior. By incorporating recent innovations in machine learning, segmentation approaches that can account for behavioral dynamics at multiple timescales, and increased breadth in the sampling modalities used to classify behaviors, we will create a toolkit that our team and others can make use of to quantify complex, spontaneous behaviors. We will implement an analysis pipeline to capture and make use of patterns of mouse body position, vocalizations, and arousal states. We also aim to capitalize on recent insights into the role of the gut-brain axis in shaping behavior. After validating our acquisition and analytical approaches, we will monitor these outputs in response to controlled, parametric environmental manipulations in two distinct, ethologically-relevant contexts: intruder response to resident urine signals and limited access to water. The exploratory data collected in these experiments will be vital to validating our algorithmic advances and for piloting future grant proposals. The foundation of this work is a diverse team approach. Our team, comprised of experts in social behavior ethology, microbiota research information theory, and data-driven computational modeling, will take an end-to end approach in executing this proposal. By starting with experimental design informed by all parties, we will ensure that the resulting pipeline possesses sufficient structure and richness for meaningful analysis. the team will help guide long-term research avenues that are both ethologically appropriate and computationally rigorous. Lastly, we recognize that open access will greatly accelerate the validation and adoption of these technologies, a stated aim of this RFA. Dissemination and access to our deliverables will benefit substantially from ongoing relationships with the Pittsburgh Supercomputing Center and OpenBehavior. The partnered hardware and software advances of Aim 1a and 1b represent the overarching goal of this proposal, an advanced and comprehensive behavior segmentation platform. Aim 2 will interrogate temporally- dynamic urine protein signals and Aim 3 will study how progressively increasing thirst induced through water- restriction affect neurobehavioral measures. These contexts will be used to benchmark the broad applicability of Aim 1 – as well as to explore the potential to address targeted research questions within these frameworks.

NIH Research Projects · FY 2025 · 2024-08

For individuals with a broad range of disability, illness, or injury, skin health can be challenging to maintain. One devastating result is the high national incidence and impact of pressure injuries. The annual cost of hospital acquired pressure injuries is estimated in the tens of billions of dollars. These injuries are painful for the individual and result in up to 60,000 US deaths annually. Pressure injuries are largely preventable through timely cleaning and frequent, consistent skin inspection. This serious health issue has received considerable attention across the US, yet the impact of pressure injuries remains largely unchanged. This project proposes advances in the engineering and psychology of human-robot skin contact towards introducing robots into the healthcare system as skin-care assistants with the goals of improving skin health for individuals who are unable to perform bathing and skin care on their own. This project will develop a novel individual representation of skin-care tasks that can be modified by a user with limited motor functions. It will provide the first ever database that captures detailed hand movement and contact pressures as expert caregivers perform skin-care tasks. It will result in novel reasoning and control methods that enable the robotic assistant to interact safely with people. It will provide novel interaction mechanisms for diverse individuals with disabilities and new findings on their preferences for working with the robotic assistant. A unique soft robot hand will be developed to support these efforts. Reducing the incidence and impact of pressure injuries can reduce pain and improve health outcomes for individuals. It can save lives and billions of dollars to be applied to better uses in the health care system. It can elevate and democratize access to skin health through consistency, implementation of best practices, and individualized inspection algorithms. It can relieve burdens on caregivers, increase privacy, allow individuals to live in their homes for a longer time and provide greater independence. RELEVANCE (See instructions): A robotic skin-care assistant can provide consistent skin cleaning and inspection for individuals that require such assistance. Consistent skin cleaning and inspection is the number one means of preventing pressure injuries and lessening their impact. Accomplishing the project goal can save lives, prevent painful injuries, save billions of dollars, and save caregiver time while empowering individuals and democratizing access to good skin care.

NSF Awards · FY 2024 · 2024-08

Despite progress in recent decades, gender gaps in labor market outcomes remain persistent both in the United States and globally, underscoring the need for further research to shape informed policies and interventions. This proposal focuses on an important but understudied source of these gender gaps: self-promotional behaviors. Self-promotion involves whether and how individuals communicate their skills, abilities, and achievements, which significantly influence how they are perceived by their colleagues and employers. Thus, if there are gender differences in self-promotional tendencies, or if self-promotion differentially affects outcomes for men and women, it may have far-reaching effects across various stages of their careers, ranging from education and hiring, to task assignments and promotions. The project entails an interdisciplinary approach that draws from economics and psychology to understand self-promotion both as a supply-side and demand-side driver of gender gaps in labor market outcomes. Leveraging lab experiments, field studies, and observational data, the research aims to address three questions. First, are there gender differences in the propensity and degree of self-promotion, and what are the underlying mechanisms of these decisions? Second, what is the impact of self-promotion on career outcomes, and do men and women equally benefit from self-promotion across different professional contexts? Third, if self-promotion is important for career outcomes, how can we mitigate gender gaps in self-promotional tendencies? The research seeks to provide a comprehensive understanding of not just whether but also when and how self-promotion can exacerbate gender disparities within organizations. The resulting knowledge help in better understanding when and how gender gaps arise, and more importantly, can inform the design of structural interventions that change features of the environment in which men and women make decisions. 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

Automated reasoning based on Boolean Satisfiability (SAT) solving has emerged as a powerful technology to solve a wide range of problems, including verification, planning, and mathematical challenges. Reasoning algorithms for SAT solving are generally categorized into two types: complete search and local search. Complete search, e.g., conflict-driven clause learning (CDCL), systematically explores the entire search space, whereas local search uses randomization and heuristics to guide the search process, e.g., stochastic local search (SLS). The current best solvers use a portfolio approach where multiple different solvers are employed to solve a given SAT instance, but there is no deeper exchange of information between the solvers. This project identifies strategies for switching between solvers when combining solvers. More specifically, the research team innovatively combines local search algorithms for SAT solving to enable their cooperation. The project develops three automated reasoning frameworks. The first framework enhances cooperation within a sequential solver by leveraging the complementary performance and structural commonalities of different algorithms. The second framework extends this cooperation to a parallel level, where a diverse suite of algorithms collaborates towards a common solution. Finally, the project integrates complete search into the cooperation to merge both traditional types of reasoning into a single powerful parallel framework to solve new challenging problems. 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

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Eva Harth at the University of Houston and Professor Krzysztof Matyjaszewski at Carnegie Mellon University aim to develop polymeric materials based on segmented polar-polyolefin copolymers with complex properties. These polymers can then assemble into nanostructures, such as spheres, cylinders or wormlike structures. These polymers are desired to advance energy storage materials and plastic material upcycling. This is made possible by the ability to precisely design and place molecular units along the polymer chains. Some of these are reactive units in non-polar plastic materials and others are for highly activated exchange reactions allowing the precise positioning of activators to further modify the material. Results of this research enhance the knowledge in how to combine normally incompatible polar and non-polar polymer chains to gain access to novel materials. The collaborators are actively engaged in undergraduate training and committed to graduate education, dissemination, and communication of the findings to educate the general public and develop the next generation of polymer scientists. Under this award, Professor Harth and Professor Matyjaszewski and their teams will further advance the unique radical/spin coupling methodology, the polyolefin active ester exchange process and developing novel polyethylene end-capping approaches to yield precision functional polyolefins from mono- and binuclear α-diimine Pd(II) complexes. These methodologies will be used to form di- and triblock architectures as well as star polymers with strategically positioned polyolefin and polyacrylic segments. The anchoring of suitable initiation units for controlled radical polymerization and modern atom transfer radical polymerization (ATRP) techniques such as regenerative ATRP with ppm Cu catalysts and benign reducing agents will be utilized and further developed to expand the range of monomers and techniques for polar poly(meth)acrylate - polyolefin block copolymer synthesis and self-assembly. Polyolefin macromonomers for ATRP will be investigated to form bottle brush architectures and combs. These collaborative approaches will not only make segmented polar polyolefin structures more attainable but also enable the exploration of novel architectures including stars, bottlebrushes, and other tailored nanostructures, which have previously been limited by the unavailability of suitable precursors. 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.