Trustees of Boston University

NSF Awards · FY 2024 · 2024-09

Earthquakes are powerful and unpredictable forces of nature, capable of causing immense destruction and loss of life. Despite advances in understanding the Earth's movements, predicting earthquakes remains a challenge. This project aims to revolutionize the field of earthquake science by using artificial intelligence (AI) to unravel the mysteries hidden within seismic data – the vibrations caused by earthquakes. Imagine an AI system that can "read" the unique signals from each earthquake, much like a detective deciphers clues. This AI, trained on massive amounts of data, will learn to recognize patterns in seismic waves, revealing details about the earthquake's source and the path the waves took through the Earth. By gaining a deeper understanding of these patterns, scientists can develop more accurate tools for earthquake monitoring and potentially even predict earthquakes with greater accuracy. This advancement will be crucial for preparing communities, improving early warning systems, and ultimately saving lives. The knowledge gained from this project could also be applied to other natural hazards, such as volcanic eruptions and landslides, further enhancing our ability to understand and prepare for these events. This research project proposes the development of a foundational AI model for advanced seismic data analysis. The model will be trained on a vast archive of seismic data to identify and characterize earthquake signals, utilizing cutting-edge AI techniques like transformer models. This will involve the development of specialized neural network modules optimized for seismic data and the implementation of a modular and sparse multi-path framework for efficient waveform analysis. The project will focus on improving earthquake detection, localization, and characterization, with potential for broader applications in geophysics. By fostering collaboration between AI experts and geoscientists, the project will contribute to the training of a new generation of interdisciplinary researchers. Importantly, the findings, tools, and computational resources from this project will be made available to the scientific community through open-source platforms, promoting transparency and facilitating further research and innovation in the field of earthquake science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

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

NSF Awards · FY 2024 · 2024-09

Rising ocean temperatures threaten coral reefs worldwide by causing coral bleaching—a phenomenon where corals expel their symbiotic algae due to stress. However, recent field studies and experimental evidence suggest that corals from environments with higher diel thermal variability (DTV) exhibit greater thermal tolerance and increased potential for physiological flexibility. This project addresses the urgent need to understand the mechanisms by which DTV can potentially mitigate coral bleaching by enhancing resilience and thermal tolerance. By integrating field and experimental data in a resilient reef-building coral species, this research establishes how DTV influences traits critical to coral survival, including coral growth, thermal performance, and physiological flexibility across varying thermal environments in Bocas del Toro, Panamá. Such insights are crucial for predicting coral bleaching under future climate scenarios and inform strategies for reef conservation and restoration efforts worldwide. This project includes the mentorship of a postdoc, two graduate students, three Panamanian interns, undergraduate students, and high school students from local Boston, MA schools. Outreach initiatives enhance public understanding of coral reef ecosystems through an interdisciplinary bilingual hybrid concert combining music and science in live performance, which are live-streamed to English and Spanish speaking audiences in the US and Panamá. This project is determining the mechanisms underlying how diel thermal variability (DTV) influences coral thermal physiology and performance. The central hypothesis of this research posits that DTV enhances coral resilience by promoting physiological plasticity, thereby improving responses to thermal challenges that can cause coral bleaching and mortality. To address this hypothesis, this project quantifies how differences in DTV influence the plasticity of coral thermal physiology and molecular function, focusing on a tractable study species (Siderastrea siderea) across six reef sites in Bocas del Toro, Panamá. The project's specific goals include: (1) establishing long-term baselines of environmental variability across sites and seasons, (2) determining if differences in DTV lead to increased plasticity in thermal physiology, and (3) testing how DTV around different mean temperatures within the context of a coral's thermal performance curve (TPC) impacts growth and physiology. By addressing these objectives, the research advances fundamental knowledge of coral thermal biology, with implications for predicting coral reef persistence and guiding effective conservation strategies amidst accelerating climate change. This project is jointly funded by Biological Oceanography and Integrative Ecological Physiology. 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

Number theory has a rich history of long-standing questions that are surprisingly easy to state but notoriously difficult to answer -- for example which sums of perfect powers equal another perfect power (a generalization of the famous Fermat's Last Theorem that remains unanswered). The central paradigm in arithmetic geometry is that the geometry of polynomial equations has a strong bearing on the geography of whole number solutions. In the last 20 years, the quadratic Chabauty method has emerged as a powerful new technique for locating whole number solutions (i.e. rational points) on curves that were impervious to all previous methods. In practice, one makes several simplifying assumptions on the curves in question to use this method. The PI will continue work with collaborators in the area of arithmetic geometry: exploring a new theoretical framework for the quadratic Chabauty method; explicitly computing invariants measuring the complexity of reduction types of curves; and introducing new computational tools to study the Ceresa cycle, a fundamental invariant associated to an algebraic curve with close ties to its geometry and arithmetic. Additionally, the PI will organize events intended to support and showcase the work of junior mathematicians at the institutional, regional, and national levels. The proposed research will explore three aspects of the arithmetic and geometry of curves. One goal is to explicitly describe good models for solvable covers of the projective line over p-adic fields, and use them to extract various arithmetic invariants of these curves, building on past work by the PI for cyclic covers. Another goal is to build new algorithms for computing various constants appearing in quadratic Chabauty method, using a new framework at bad primes jointly developed with her collaborators, utilizing recent advances in the comparison of p-adic integration theories for curves with bad reduction. The third goal is to use techniques from p-adic integration and the geometry of curves in characteristic p to provide new methods for establishing the nontriviality of the Ceresa cycle, a canonical one dimensional algebraic cycle associated to a curve. 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

Cardiac contractility modulation (CCM) devices are novel technologies that use electrical stimulation to treat heart disease. Despite their promise, the long-term effects of CCM devices on the heart are not well known. This NSF/FDA Scholars-in-Residence project will develop a regulatory science tool to assess chronic use of electrical stimulation devices for the heart. Engineered heart tissue will be monitored in a high-throughput measurement platform to assess the effectiveness of electrical stimulation and changes in the tissue. The project will support a post-doctoral scholar to work at the FDA. This scholar will be co-mentored by researchers at the FDA and Boston University (BU). Outcomes of the work will help regulatory agencies better understand the effects of new heart treatment devices. This FDA-SiR project will advance the development of in vitro systems that could be used to evaluate safety and effectiveness of medical device technologies affecting cardiac electrophysiology. Non-excitatory electrical stimulations, such as cardiac contractility modulation, have been proposed to aid pathological remodeling in mild to severe heart failure patients. In this research project, engineered cardiac tissues derived from human induced pluripotent stem cells will be used in a high-throughput platform to evaluate CCM stimulation of 96 tissues in parallel, recording both contractility and calcium channel fluorescent signaling. This will be extended to include electrophysiological stimulation and multiplexing with cardiac functional readouts to elucidate the chronic effects of non-excitatory electrical stimulations. These studies will support regulatory assessment of the first medical device tool for chronic in vitro 3D human cardiac electrophysiology. In addition, this work will address two major hurdles of human microphysiological systems: long-term electrical stimulation and high throughput monitoring of phenotype in engineered tissue. 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

In many natural and engineered environments, slender elastic structures (beams and shells) often interact and compete for space with granular matter (sand, dirt, rocks). Examples include the growth of plant roots, the packing of cells in an embryo, and the stabilization of erosion-prone soil with geotextile fabrics. Slender structures respond to these complex “elastogranular” interactions by bending and buckling, while grains may abruptly transition between flowing like a fluid and jamming like a solid. These structures exhibit unprecedented reconfigurability and a range of complex mechanical behaviors. This award supports fundamental research to answer the question as to how localized confinement - like cohesion, adhesion, and packing geometry - dictates the fundamental mechanics of elastogranular structures. This work will directly impact a wide range of engineering problems, including recyclable and sustainable architecture, engineered living materials, morphogenesis and organogenesis, and mechanical metamaterials. In addition to impacting education at the university level, the researchers will distribute a monthly newsletter to communicate ideas and advances in mechanics to the broader public using video, podcasts, news, and current events, and will include graduate students, postdocs, and professors as guest writers. Slender structures commonly exhibit large deformations due to their geometry, and therefore to accomplish the goal of this work, the researchers will consider grains bound to or confined by beams, plates, and shells. The work will target the following specific objectives, through a combination of computation, theory, and experiments. (1) Understanding and controlling the postbuckling response of beams with grains bound to their surface as a function of their spatial distribution. (2) Determining the mechanical response of a 2D elastogranular foam composed of an array of beams surrounded by grains which form a composite system with tunable mechanical properties. (3) Determining the bending and stretching rigidity of plates embedded with patterns of grains, enabling the design of plates that are stiff in response to loading in one or more directions, yet compliant in response to loading in other directions. (4) Creating and characterizing elastogranular shells whose compliance can be tuned in situ from soft to stiff. Each objective relies on understanding how the interplay between local confinement and elastic instabilities enables structures with advanced functionality. 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 global population has skyrocketed from approximately one billion in 1800 to over eight billion in 2024, driving increased demand for vital and interrelated resources like energy, food, and water. Sustainable approaches are required to meet the escalating energy demand, including resource conservation, energy storage enhancements, and global energy resource management. Addressing the research challenges associated with providing clean energy for the world is a multidisciplinary problem that requires scientists and engineers from diverse fields to collaborate closely. However, the current academic research on sustainable energy is primarily concentrated within traditional academic disciplines. Consequently, academic programs may fail to train graduate students who will enter the energy sector workforce with the essential communication skills and technical expertise to confront these profoundly interdisciplinary challenges. This National Science Foundation Research Traineeship award to Boston University (BU) will create an interdisciplinary community of students trained to work collaboratively on sustainable energy research and development. The ENERGIZE project anticipates training 100-125 Ph.D. and master’s students, including 20 funded trainees from a range of disciplines across the university. Trainees will come primarily, but not exclusively, from Ph.D. programs. The technical focus of the ENERGIZE NRT spans research in photochemical conversion, electrochemical conversion, electrochemical storage, and photochemical storage while concurrently providing foundational training for students in physical and computational experimentation and data science. The program has five integrated components: (i) evidence-based experiential learning and interdisciplinary collaborations, (ii) building communications and leadership skills, (iii) career development, (iv) interdisciplinary mentoring, and (v) rigorous program assessment to track learning outcomes. Trainees will develop depth in a particular area of study and breadth across complementary areas of energy conversion, energy storage, sustainability, and data science through coursework, workshops, an internship-based practicum, and mentored laboratory research projects. They will engage in formal activities devoted to professional development, career planning, and skills acquisition through bootcamps, tailored workshops, and leadership opportunities within the NRT community. The project outcomes will include a model for educating scientists and engineers in interdisciplinary research, a research community with technical depth and breadth and a broad understanding of the social impact of sustainable energy and their own research, and educational materials and best practices on research education and professional development. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. 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

Increasing seawater temperatures are destabilizing the relationship between coral hosts and their algal symbionts, causing coral bleaching, and endangering coral reefs worldwide. Though coral responses to thermal challenges have been extensively studied, little research has explored sex-based differences in distributions, reproductive investment, functional variation, and, ultimately, coral thermal tolerance. Many coral species have separate sexes with males producing sperm and females producing eggs for sexual reproduction. Sex-specific differences in growth and tissue thickness have been previously observed in some coral species, with females enduring tradeoffs associated with more energetically costly egg production. This project will investigate how sex-specific differences in coral physiology influence distribution patterns across environments and thermal tolerance traits. This project will also include the mentoring of undergraduate students and it will support a local high school student to engage in the research process, providing valuable training and experience in molecular biology. In collaboration with a climate change focused concert series, a public educational outreach booth will be developed that is focused on climate impacts on marine ecosystems. This project will assess shifts in sex distributions and sex-specific physiological tradeoffs across marginal and non-marginal habitats by integrating field and mesocosm experiments with genomic tools. PI Gantt will explore differences in sex ratios across marginal and non-marginal environments by leveraging environmental (i.e., temperature, turbidity) and histological (i.e., tissue sections) data. Next, functional differences between coral sexes will be assessed using molecular (i.e., gene expression), microbial (i.e., ITS2 amplicon sequencing), physiological (i.e., host and symbiont energy reserves, symbiont cell densities, chlorophyll pigmentation, host tissue thickness, photosynthesis/respiration rates), and trophic (i.e., stable isotopes) measurements. Finally, to test whether differences in thermal tolerance are observed between male and female corals, PI Gantt will conduct a thermal challenge experiment and measure host and symbiont physiology (i.e., Fv/Fm, symbiont cell densities, chlorophyll pigmentation). While considerable research has focused on coral responses to thermal stress, there remains a significant gap in understanding sex-based variation in thermal tolerance and reproductive investment. By examining these factors across a gradient of habitats, this proposal not only promises insights into coral resilience to climate change but also emphasizes the importance of considering sex-specific differences in conservation and restoration efforts. 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 will address a decades-long challenge in the field of ribonucleic acid (RNA). In cells, RNA is responsible for producing proteins, which perform various functions from structural support and cell movement to signaling. Delivering RNA to a cell is a way to replace or supplement a natural protein. However, RNA is short-lived and, thus, minimal protein is produced over a short time. RNA that self-amplifies produces more protein and for a longer time, but self-amplifying RNA (saRNA) degrades quickly inside a cell. The team has discovered a method to modify saRNA so it lasts longer inside a cell, which advances RNA technology and opens previously unattainable opportunities across diverse industries, from farming to medicine. The senior personnel on this project will lead a diverse research team to educate, train, and mentor graduate and high school students and postdoctoral fellows, as well as to engage with the community through outreach and communication. They will share their findings through publications, presentations, and workshops helping to train a new generation of engineers with unique skills that will advance our economy and global standing in RNA engineering. Ribonucleic acid (RNA) technologies, such as self-amplifying RNA (saRNA), will fundamentally alter the genetic engineering paradigm in eukaryotes and offer substantial promise over other genetic tools, such as messenger ribonucleic acid (mRNA) and deoxyribonucleic acids (DNA). Unlike mRNA, saRNA replicates with longer durability and greater protein expression. Yet, in contrast to DNA, saRNA does not need to enter the nucleus to function, greatly simplifying the delivery and eliminating genome integration concerns. While promising, significant hurdles remain for performance optimization to improve its short half-life in cells. Building upon the team’s discovery that incorporation of modified nucleoside triphosphates (modNTPs) in saRNA extends the cellular half-life, the team will: 1) synthesize a diverse library of new modified nucleoside triphosphates (modNTPs) and perform in vitro transcription to prepare modified self-amplifying ribonucleic acid (saRNA); 2) evaluate the performance of the modified saRNA in functional protein expression screens; 3) determine the performance of saRNA derived from other viruses and optimize protein expression and duration using AI-assisted methodologies; and, 4) build polycistronic gene expression and logic gates into saRNA to enable greater engineering control. The detailed and systematic experimental study described herein will create a powerful synthetic RNA genome platform for engineers and scientists to use across the disciplines. 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

Many marine organisms, from corals to fishes, have complex life cycles with relatively sedentary adults and dispersive larvae. The larval phase remains one of the big unknowns in marine ecology. It involves a number of complex questions. How far do larvae disperse from their parents? What causes variation in larval dispersal distance? What are the consequences of variation in larval dispersal distance? These types of questions are being addressed using clown anemonefish (a.k.a. Nemo), using a combination of laboratory experiments, field experiments, molecular genetics, and mathematical modeling. The clown anemonefish has become a model system for investigations in marine science due to its tractability in the laboratory and in the field. The research objectives are integrated with multiple broader impact activities: undergraduates and graduate students will be trained in the field of marine ecology and are learning transferable skills in experimental design, data collection, data management, statistical modeling, and scientific communication; high school students are being provided opportunities to participate in all aspects of the scientific process, so that they might consider STEM more seriously as a career choice; a book is being written, targeting a teenage audience and presenting marine ecology research and profiling marine ecology researchers, so that the field can be better understood by the general public; and the research is to be published in popular science magazines in English and Spanish. Insights from this research may also ultimately inform the creation of marine protected areas and better fishing regulations. Understanding the patterns and causes of marine larval dispersal is central to understanding marine metapopulation dynamics. In recent years, great advances have been made in measuring larval dispersal, using genotyping and parentage analysis to document self-recruitment and export and provide quantitative estimates of dispersal kernels. This prior work revealed that there is substantial intraspecific variation in larval dispersal distances. One of the most plausible explanations -- the testing of which is the focus of this project - is that there is plasticity in larval dispersal traits and distances in response to variation in the quality of parental environments. The investigators are integrating laboratory experiments, field experiments, and theoretical modeling and using the clownfish (Amphiprion percula) as a model system. First, the hypothesis that parents in high- and low-quality environments will produce larvae that differ in morphology, behavior, physiology, and gene expression is being tested. Second, the hypothesis that parents in high- and low-quality environments will produce larvae that differ in their dispersal distance distributions is being tested. Third, the generality of the results and their broader implications is being investigated using theoretical modeling to evaluate the evolutionary causes and ecological consequences of dispersal plasticity. 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 award supports a research project that introduces a novel approach that robots can use to navigate (individually or as part of a team) in unknown environments in a way that is closer to how humans approach the same task. The main project novelty is represented by the high-level reasoning used by the robots (which uses rooms and doors instead of simple points and planes as in previous approaches) for both keeping track of the environment during exploration, and for deciding how to maneuver. The project’s impacts are twofold: 1) the methods will make robotic systems more robust, as they will not rely on specific details of an environment but on a more “general” understanding of it, and 2) the techniques and code developed will be the basis for a new modular software teaching platform to be used in the Robotics and Autonomous Systems Teaching and Innovation Center (RASTIC) to educate the next generations of workers in robotics and engineering. On a technical level, the project is organized along three components: 1) Compact, interpretable, high-level representations of the environment based on structured elements that are: directly extracted from measurements using machine learning model, compared using Signed Distance Functions, and optimized over time using piecewise linear optimization. 2) Local and global navigation strategies that synthesize nonlinear controllers via linear programming, and stitch them together using Q functions that take into account uncertainty. 3) Coordination approaches for robot teams that reduce sensing requirements to a barebone set of measurements (contact sensors, single-beam rangefinders, compass), and avoid computation during navigation by reusing previous experience. Overall, the goal is to provide new approaches for mapping and control synthesis that do not require detailed bottom-up representations of the environment and that use a fraction of the computational requirements of previous techniques. 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

Computing systems' ability to efficiently and timely process large amounts of data is a key enabler in the modern landscape of data-driven applications. To bridge the widening gap between memory technology and processors, computing systems continue to rely heavily on complex multi-level cache hierarchies. Caches can prevent costly accesses to downstream memory if the processed data items exhibit good spatiotemporal locality. Unfortunately, locality does not always emerge naturally in complex data processing pipelines. Platform-specific algorithmic optimizations are often necessary to rearrange the algorithm’s memory access pattern for better locality while striving to maintain the original semantics. When operating on high-dimensional objects (e.g., tensors), data locality unlocks crucial performance gains, but it becomes harder to achieve. This project proposes a novel class of architectural data transformation units to be interposed between memory and compute, for example Central Processing Units (CPUs) and Graphics Processing Units (GPUs). By relying on knowledge of the data access pattern followed by the algorithmic semantics, they decouple the in-memory geometry of data items from the access sequence required by the computational logic. As such, they make data items requested sequentially appear to the processing unit—and cache hierarchy—as if they were stored sequentially without data duplication through on-the-fly transformations. This enables spatiotemporal locality to be achieved effortlessly, i.e., without the need for heavy algorithmic re-engineering. The findings will be integrated into undergraduate and graduate courses at Boston University and the University of Kansas, enhancing topics such as data systems, system performance evaluation, embedded real-time systems, and operating systems. The project will support underrepresented populations across educational levels and foster strong industry connections. This project explores the theory and practice concerning the formulation, design, and implementation of architectural on-the-fly Data Transformation Units (DTUs). It does so by thrusting along three interconnected research avenues. First, the investigators focus on developing a foundational science of on-the-fly data transformation. A key stepping stone is formulating an access pattern specification language that is both expressive and efficiently interpretable in hardware. In the second thrust, two alternative architectural paradigms are explored, namely (1) the integration of DTUs as a component logically placed on the memory bus and (2) the integration of a DTU directly into the memory controller. Doing so places data transformation as close as possible to the memory cells to exploit their inherent parallelism while supporting unmodified commercial memory modules. The third thrust explores which programming models can best empower application designers to use DTUs via a combination of instruction-set architecture extensions, operating system-level support, and user-space libraries. Finally, the fourth thrust aims at identifying widely adopted data processing pipelines that can greatly benefit from using DTUs, specifically focusing on relational databases and machine learning. These will be used to concretely showcase the potential of the proposed on-the-fly data transformation approach. 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 investigators will carry out theoretical analysis and computer simulations of solar observations to test the hypothesis that heating in the coolest parts of the Sun’s atmosphere is caused by small-scale (meter-sized) slow-moving plasma processes referred to as the Thermal Farley-Buneman Instability. This may also occur in the atmospheres of other stars and planets, including the Earth. The researchers will train graduate, undergraduate, and high school students in methods of computational plasma physics. The student groups will include women and people from historically excluded populations. The team will constrain their models using data from the Interface Region Imaging Spectrograph (IRIS) on the NASA Solar Observation Satellite using the Mg II spectral lines in the NUV passband. They will use full-disk scans performed once month to capture a large variety of targets. They will also use Fe I 617.3 nm data with 1 arcsecond resolution obtained with the NASA Solar Dynamics Observatory’s Helioseismic and Magnetic Imager (SDO/HMI) instrument for measurements of the photospheric magnetic field. The work will include a series of multifluid and kinetic simulations to explore the nonlinear and thermal properties of the resulting turbulence and incorporate the electron heating into larger radiative magnetohydrodynamic codes. The models will also help understand observations from NSF’s new DKIST solar observatory. 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

Whistler-mode waves, commonly observed electromagnetic emissions, have a significant influence on the movement of energetic electrons within our solar system’s plasma. This project aims to address a crucial knowledge gap in our understanding by evaluating how whistler-mode waves scatter energetic electrons into the atmospheres of Earth and Jupiter. The scientific findings will be critical for understanding the fundamental science questions about wave-particle interactions and the resulting electron precipitation driven by whistler-mode waves, not only on Earth but also on Jupiter and potentially in other space plasma environments throughout the solar system. This project has the potential to significantly improve future models of energetic electron dynamics, marking a pivotal step toward improved space weather prediction, which becomes increasingly important for our technologically reliant society. The project involves researchers at various career stages, including a female faculty member, three early-career Co-PIs, a postdoc, graduate students, and undergraduate students. Moreover, the team plans to develop educational materials specifically designed for K-12 students and actively engage in various outreach activities. The active participation of the diverse team in robust research endeavors, along with effective mentorship and outreach initiatives, will play an important role in fostering the growth of a diverse and globally competitive STEM workforce. The overarching science objective of this project is to conduct a comparative assessment of energetic electron precipitation driven by whistler-mode waves on Earth and Jupiter. Specifically, this project aims to achieve the following objectives at Earth and Jupiter: (1) systematically characterize the typical properties of whistler-mode waves (both chorus and hiss) and analyze the occurrence of large-amplitude whistler-mode waves; (2) evaluate the properties of energetic electron precipitation with the focus on intense precipitation; (3) examine the importance of nonlinear effects on energetic electron precipitation due to whistler-mode waves; and (4) quantify the effects of whistler-mode waves on electron precipitation across various energies in different regions. To attain the research objectives, the project team will leverage unprecedented multi-satellite observations, including data from missions such as Van Allen Probes, THEMIS, and ELFIN for Earth, along with Juno’s observations of Jupiter, as well as theory and modeling. The scientific findings of this project will contribute to a more profound comprehension of the fundamental physical processes governing plasma wave and particle dynamics throughout the 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 · 2024-08

With the support of the Chemistry of Life Processes (CLP) program in the Division of Chemistry, Professor Pinghua Liu of Boston University is studying the structure-function relationship of C-S bond formation using enzymes in ovothiol biosynthesis as the model system. Sulfur represents ~1% of the cell dry weight and sulfur-containing molecules play important roles in biological systems. Biosyntheses of sulfur-containing natural products, especially by selective C-H activation, are at the frontiers of mechanistic enzymology and biocatalysis. The proposed structure-function relationship studies of ovothiol biosynthetic enzymes are important addition to this field. The proposed work integrates tools from organic/bio-synthesis, structural biology, kinetics, spectroscopies, and enzyme engineering, which is an excellent platform to train STEM students. Reactions studied in this work may also be applied to improve ovothiol industrial production processes due to its biological activities. Ovothiol biosynthetic enzymes (OvoA) were selected in this study due to several of their unique features: 1) crystal structures have been obtained; 2) some key intermediates have been trapped; and 3) they are flexible in substrate selectivity. The C-S bond formation reactions performed by OvoA are distinct from current literature examples. With this award, the proposed structure-function relationship studies seek to provide mechanistic understanding on factors modulating the partitions among several different reaction pathways in these enzymes. These studies include characterizing factors governing the C-S bond formation regioselectivity. Moreover, pre-steady state and spectroscopic characterization of the OvoA enzyme, especially factors that might affect the kinetics and properties of the S = 1 Fe(IV) species will be carried out. Finally, biochemical, kinetic, and spectroscopic characterization of OvoA’s cysteine dioxygenase activity will be performed. The central educational objective of this proposal is to train our students using an interdisciplinary approach and then apply knowledge gained in basic research to solve real world 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

There is considerable scientific and societal interest in better understanding the terrestrial carbon cycle: how carbon dioxide is taken out of the air by plants, moves through ecosystems (i.e., fluxes), is stored in different plant and soil pools, and is released back to the atmosphere. Researchers need to better understand the variability and predictability of the cycle over the short-term (for carbon inventory monitoring, reporting, and verification), medium-term (for developing natural climate solutions), and long-term (to understand climate stabilizing feedbacks), and across spatial scales from individual sites to continents. This project aims to better understand carbon variability and predictability through the generation and analysis of a new North American terrestrial carbon cycle data tool that harmonizes information coming from an unprecedented volume and variety of on-the-ground measurements, data from satellites, and mathematical models of how the carbon cycle works. These analyses will provide new insights into long-standing questions such as: (1) How do different carbon pools and fluxes vary across space, time, and in response to environmental variables like temperature, precipitation, land use, and topography? (2) Under what conditions are mathematical models most/least reliable? (3) Where are the gaps in existing data-collection networks? (4) How far into the future are different carbon pools/fluxes predictable and which sources of uncertainty most limit predictability? To facilitate uptake this team of researchers will work with the US Forest Service (USFS) to incorporate these data products into federal carbon accounting efforts and seek certification of these open-source technologies for use in the voluntary carbon markets. Finally, this project will contribute to the training of two graduate students and four undergraduates, with the latter recruited through an environmental data science program focused on increasing American Indian/Alaskan Native involvement in STEM. This project will produce a carbon cycle “reanalysis” product based on the iterative model–data assimilation approaches commonly employed in numerical weather forecasting to harmonize process-based mathematical models with new observations. Specifically, the PEcAn terrestrial carbon data assimilation and forecasting system will be expanded to integrate twelve new bottom-up field data constraints from the National Ecological Observatory Network (NEON), five data constraints from the USFS Forest Inventory, and Ameriflux eddy-covariance tower and ancillary data. These bottom-up constraints will “anchor” PEcAn’s existing assimilation, which is based on optical, lidar, and microwave remote sensing. To support this, existing data assimilation approaches will be refined to not only jointly estimate pools and fluxes, but to also capture spatiotemporal variability in model parameters and employ hybrid machine learning approaches to understand the variability in model residual error. Finally, the variability in the continental-scale reanalysis product will be re-analyzed against a range of explanatory variables and across multiple time scales, spatial scales, and prediction lead times. Integrated into the research project will be training opportunities for graduate and undergraduate students. 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

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry Professors Reinhard and Dennis from Boston University and Northeastern University will investigate charge and energy transfer between metal nanostructures and semiconductor nanocrystals through single particle spectroscopy. The chosen metal (gold and silver) and semiconductor (chalcopyrite, CuFeS2) nanomaterials both support collective charge oscillations that provide opportunities for very efficient coupling between them under resonant conditions. The lineshape of the scattering spectra of individual hybrid structures containing both metal nanoparticles and semiconductor nanocrystals will be analyzed to characterize direct charge and energy transfer between the building blocks. Optimization of these transfer processes has the potential to result in enhanced photocatalytic activity for the hybrid nanomaterials, which will be tested experimentally. Improved photocatalytic materials have important societal relevance, for instance in solar energy conversion and waste water remediation. The research of this project will be enriched by educational and outreach components. For instance, a Nano Workshop (Boston University) and a Quantum Dot Bootcamp (Northeastern University) will be developed to introduce interested high school teachers and inner-city high school students to the concepts and science underlying this research project. Plasmon dephasing in noble metal nanostructures generates hot charge carriers that are of interest in a wide range of applications, including photoconversion and photocatalysis. Unfortunately, hot electrons and holes recombine rapidly in noble metal nanostructures, severely limiting their potential for applications. Hybrid structures comprising noble metal nanoparticles and semiconductor nanocrystals may increase the lifetime of the reactive charge carriers by charge separation, but extraction of the hot charge carriers competes with their rapid thermalization, limiting the efficiency of the process. Hybrid nanostructures that produce excited charge centers in the semiconductor through direct energy and/or charge transfer without a priori generation of hot charge carriers in the metal hold great potential to increase the generation of long-lived reactive species. Chalcopyrite nanocrystals sustain quasi-static resonances in the visible, which provides unique opportunities for enhancing direct charge and energy transfer in hybrid structures in which noble metal and chalcopyrite building blocks are resonantly coupled. This project will use single particle spectroscopy to quantify interfacial plasmon dephasing as a measure of direct excitation transfer in metal/chalcopyrite hybrid systems with correlated electron microscopy to elucidate the composite structure/function relationship on a single-particle scale. Hybrid systems containing building blocks whose collective resonances show different degrees of energetic overlap will be used to test the hypothesis that resonant coupling between the building blocks enhances direct excitation transfer. 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 support to U.S. researchers participating in a project competitively selected by a 55-country initiative on global change research through the Belmont Forum. The Belmont Forum is a consortium of research funding organizations focused on support for transdisciplinary approaches to global environmental change challenges and opportunities. It aims to accelerate delivery of the international research most urgently needed to remove critical barriers to sustainability by aligning and mobilizing international resources. Each partner country provides funding for their researchers within a consortium to alleviate the need for funds to cross international borders. This approach facilitates effective leveraging of national resources to support excellent research on topics of global relevance best tackled through a multinational approach, recognizing that global challenges need global solutions. This award provides support for the U.S. researchers to cooperate in consortia that consist of partners from at least three of the participating countries. The teams will develop transdisciplinary and convergent research approaches on cultural heritage and climate change, foster collaboration among the research community across several regions, and contribute to knowledge advances at the global level. The project seeks to explore the role of fire in the development of local cultures, heritage and landscapes. The project will characterize the risk of fire regime change to lifeways, landscapes and landmarks; and quantify the direct and indirect costs to cultural heritage resulting from changing wildfire frequency, intensity and extent. In addition, the project will assess the sustainability of traditional fire management and identify best practices; develop strategies for wildfire resilient heritage governance and facilitate transdisciplinary and cross-regional knowledge exchange for heritage conservation across currently and soon-to-be fire prone regions. The project will consist of four case study sites of heritage-rich landscapes in Ireland, Kenya, Turkey, and Italy, purposefully selected to represent a range of biomes, levels of current and projected fire risk, and traditions of land use management. 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 project will advance a fundamentally new control framework, utilizing streams of heterogeneous data to optimize the behavior of complex and dynamic networked systems with pervasive sensing and computing capabilities, operating in uncertain and changing environments. Existing workhorse control and optimization methodologies assume a large separation of time scales, sufficient to justify complete decoupling of the optimization and control tasks. However, this assumption is increasingly invalid for modern critical infrastructure and social platforms. This project represents a new approach for optimal and reliable decision-making on time scales comparable to the dynamics of the underlying physical and logistic systems, by using new mathematical principles of analysis and synthesis to control the collective behavior of agents and the underlying physical dynamics. The key concept is to continuously drive the dynamical system towards solution trajectories of optimization problems that have costs, constraints, and inputs which change over time. In the context of future transportation networks, the approach is well-aligned with the objective of moving people and cargo efficiently and sustainably, and with the integration of connected and autonomous vehicles. Similar application opportunities occur in areas such as energy, robotics, and autonomous systems, with the common feature of interconnected cooperative and non-cooperative agents interacting via multiple heterogeneous physical and virtual networks. The project will also impact undergraduate and graduate engineering students, and K-12 students through a comprehensive outreach and educational plan that includes STEM camps, engaging activities to promote the recruitment of female students and students from under-served communities and minority schools into the STEM pipeline, and curriculum enhancement initiatives. Traditional decision-making architectures in networked systems and critical infrastructures are grounded on explicit spatio-temporal boundaries between model-based network-level optimization (producing setpoints in a feed-forward fashion) and local closed-loop control (regulating the dynamical system to the setpoints while rejecting disturbances). The modus operandi of these traditional architectures has worked well in settings where the underlying dynamics of the physical systems are slower than the solution time required by network-level optimization tasks, network models and data structures are available, and problem inputs can be pervasively collected in a timely and reliable manner. Such assumptions, however, are becoming increasingly inadequate in dynamic settings where batch approaches fail to solve the underlying optimization problems on a time scale that matches the dynamics of the networked physical systems, physical models (embedded into the optimization task) are difficult to estimate accurately, and (unknown) disturbances evolve rapidly and unpredictably. This project will generate new mathematical principles for the synthesis and analysis of online data-based algorithms that drive the collective behavior of agents and physical dynamics to desired operational points. In particular, the desired equilibrium points coincide with solution trajectories of time-varying optimization problems formalizing performance metrics and operational constraints associated with the dynamical system. The interconnected-system framework under study compresses the time scales between control and optimization tasks to continuously drive the dynamic behavior of physical systems to network-optimal and stable points. The research seeks to expand the class of problems to which this project vision can be applied, develop predictive controllers with information streams, and synthesize novel distributed algorithmic solutions for interconnected systems. The technical approach focuses on networked transportation systems as the arena to materialize the theoretical and algorithmic advances and provide innovative control and optimization strategies. Beyond transportation, benefits are expected to propagate in the broader optimization and control communities, with applications in multiple domains including control of epidemics, robotic networks, social networks, and energy infrastructures. 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

Liquid-liquid phase separation (LLPS) of intrinsically disordered proteins (IDPs) is a hot topic at the interface between biology, physical chemistry, and biophysics. Of particular interest is the possible role played by LLPS in the pathological aggregation of proteins into amyloid fibrils. This award aims to elucidate how protein-rich droplets, known to play important roles in normal cellular function, can, under certain conditions, promote the pathological aggregation of proteins associated with neurodegenerative diseases. At this time, the basic properties of the protein droplets have been scarcely characterized, and the processes leading to droplet formation promoting protein aggregation are unknown. Using coordinated experimental and computational research, carried out by a team of scientist in the US and France, this project will explore factors governing droplet formation and aggregation of two proteins: tau and alpha-synuclein. The research will generate fundamental knowledge in protein science and provide new insight into the molecular processes that are at the origin of protein aggregation diseases. Support will enable the exchange of junior (graduate student and postdoctoral researchers) between institutions, furthering the education and professional development of those young scientists. The research conducted as part of this award will explore the role of LLPS in the aggregation of the IDPs tau and alpha-synuclein. The research will employ an innovative combination of computational and experimental techniques, namely electron paramagnetic resonance (EPR), Overhauser Dynamics Nuclear Polarization (ODNP), neutron scattering (NS) and multiscale computer simulations. Simulations will employ a unique combination of molecular dynamics and field theory approaches to gain novel insights into protein conformations and hydration water dynamics, and to map phase behavior of the IDPs. The consortium is composed of three French and two American partners that form a multidisciplinary team of structural biologists and biochemists, biophysicists, physicists, and computational chemists with computational and experimental expertise in protein sciences. The contributions of all five partners are essential to create the synergy necessary to tackle the challenging objectives. Besides generating fundamental knowledge in protein science, this project will provide new insight into the molecular processes that are at the origin of protein aggregation diseases. This award will enable the exchange of junior (graduate student and postdoctoral researchers) between institutions, furthering the education and professional development of those young scientists. Collaborative activities will include physical exchanges of junior researchers from the US visiting the IBS in Grenoble and University of Bordeaux in France, and junior researchers from France visiting Boston University and UC Santa Barbara in the US. Visits will include active research activities, involvement in group meetings, and presentation of research seminars. This collaborative US/France project is supported by the US National Science Foundation (NSF) and the French Agence Nationale de la Recherche (ANR), where NSF funds the US investigators and ANR funds the partners in France. The US investigators are jointly funded by the Physics of Living Systems program in the Directorate for Mathematical and Physical Sciences and the Molecular Biophysics program/Division of Molecular and Cellular Biosciences in the Directorate for Biological Sciences. 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 CAREER proposal focuses on power grids, and aims to translate foundational theory and algorithms into breakthrough real-time optimization and control approaches for distributed energy resources (DERs). In this context, the overarching goal is to overcome current technological and operational barriers associated with the large-scale integration of DERs, where: (a) the deployment of DERs with business-as-usual practices has decreased power-quality and reliability, (b) existing network optimization approaches may fail to provide solutions at a time scale that matches the dynamics of power systems with DERs, and (c) synthetic models for users’ preferences and comfort may not capture the users’ goals truthfully. The research plan seeks a shift from a paradigm with a time-scale separation between economic optimization and local control – predominant in today's distribution grids, where corrective and localized rules serve as a basis for real-time voltage regulation and ancillary-service provisioning – to operations where DERs actively partake into grid operations and leverage real-time network-level coordination to seek increased efficiency and reliability. DER coordination is engineered so that DERs can learn to maximize users' preferences, while aiding system-level frequency and voltage control. An integrated education and outreach plan will engage middle- and high-school students through a summer Science, Technology, Engineering and Mathematics (STEM) Research Academy and lectures for the Pre-Collegiate Development Program. To bridge research and education, the PI will develop courses on the themes of online optimization for networks and optimization of power systems, and will promote undergraduate student research. A working group on optimization and learning will be created at the University of Colorado Boulder in synergy with the Autonomous Systems Interdisciplinary Research Theme, to bring together faculty and students across the campus and stimulate multi-disciplinary research and education. The proposed research leverages time-varying optimization models for networks operating in dynamic environments, and seeks to develop real-time optimization architectures with tightly-integrated feedback and learning components. The proposed feedback-based online algorithms have the following key attributes: i) Principled algorithmic steps employ measurements from the network to bypass the need for a network model; ii) Algorithms include humans in the loop by learning the users' utility functions from users' feedback during the execution of the online decision algorithm; iii) Algorithms are implemented in closed loop with the power network to acknowledge dynamics and effectively act as feedback controllers; and, iv) Algorithms promote low-complexity, distributed, and scalable architectures. Fundamental tradeoffs between convergence rate, tracking of time-varying optimal solutions, maximum constraint violation, and computational complexity of the algorithms will be offered. 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 project will develop an open-source Large Language Model that will be able to identify, verify, prioritize, summarize and predict outcomes of disease emergence events in humans, other animals, and plants. That model will link detected signals and model outputs to human verification and public health context with the goal of sharing event reports on a publicly available web platform. BEACON, based at Boston University’s Center on Emerging infectious Diseases, is a web-based platform and accompanying public health program which aims to address this need by leveraging advanced AI and a global network of human subject matter experts to rapidly collect, analyze, and disseminate information on emerging disease threats. The current lack of similar free resources for the global community makes this initiative both transformative and timely. The impact of an independent, open-access, disease surveillance platform that utilizes advanced AI with human subject-matter oversight for near real-time reporting and analysis of emerging threats is substantial. BEACON will aid in global capacity strengthening and tailored international and national preparedness and response. It will empower policymakers, public health, and healthcare practitioners, and the public through actionable information, ultimately driving proactive measures to prevent and mitigate the spread of emerging threats. The exploratory approach of integrating AI/LLM into both discovery and assessment of new biological events will create a specialized large language model for processing vast datasets in order to detect potential biothreats. The developing machine learning algorithms will be capable of predicting the epidemic and pandemic potential of any new outbreak by integrating AI findings and predictive intelligence with trusted human expert verification and analysis. The LLM will be used to classify and rank new signals and reports depending on their relevance and importance, provide features (text embeddings) for predictive modeling, and compose/translate reports to be edited/approved by human subject matter experts. It will produce reports and data tools that will provide context for action, and disseminate and share the data and analyses via a user-friendly multilingual web platform that will operate as a global public good. 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

Many online platforms use notifications to users to advise them when the potential harm of communications is in question, as in the case of false advertising, cyberbullying, scams, or personal threats. The notifications permit users to see the source or context of what they receive, rather than take it at face value. Previous research, however, has shown that these efforts fail to flag most of the fraudulence. This situation stems from the difficulty of automatically flagging online material, forcing service providers to heavily rely on manual verification, a scarce resource that cannot keep up with the number of communications posted every day. In this project, the team is developing tools that can enable the automated identification of fraudulence so users can receive correct notifications. The project is improving the state of the art of automated identification of fraudulent online material. First, the project team is developing robust stance detection techniques powered by recent advances in large language models. These techniques can enable more precise and effective identification of fraudulent material that raises awareness of its context. Second, the team is developing multi-modal techniques that combine the textual and image component of communications and analyze them together, by adapting computer vision techniques like perceptual hashing, optical character recognition, and multi-modal embeddings. Throughout the project, one of the main goals of the project is to develop techniques that are scalable and can operate on vast posted material using limited hardware resources. To this end, the researchers are working on reducing the size of the used machine learning models through model distillation, and on taking advantage of specialized technologies to efficiently handle embeddings like vector databases. 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

Inauthentic accounts are commonly used by adversaries on online platforms to carry out fraudulent activities like false advertising, scams, and personal threats. These accounts appear to belong to real people, but actually portray fictitious personas and are controlled by miscreants through semi-automated means to deliver potentially harmful content. Promptly detecting inauthentic accounts and fraudulent content is important to keep online users safe and prevent harmful and possibly illegal activity to thrive. Existing approaches to flag potentially harmful content either rely on learning behavioral traits of inauthentic accounts or on identifying keywords that are commonly used in fraudulent content. Existing research has, however, shown that adversaries adapt both their behavior and the content they post over time, with the goal of avoiding being flagged. In this project, the research team aims to address this problem by combining the two approaches into an end-to-end automated analysis pipeline. The project is improving the state of the art of automated identification of fraudulent online material. First, the team is developing robust artificial intelligence techniques to identify narratives used by previously identified inauthentic online accounts. These techniques will leverage advances in large language models and multi-modal embeddings to identify content that is posted on multiple platforms, consisting not only of text but also of images and videos. Second, the team is developing machine learning techniques to identify the characteristics of narratives used by adversarial actors, with the goal of identifying future harmful narratives irrespective of the content being shared. Third, the investigators aim to use the identified narratives to flag new inauthentic accounts, and learn their behavioral patterns for more effective detection. Used in conjunction, these three methods will allow researchers to identify the changes in content and behavior of harmful online campaigns, allowing for a more robust identification than what is currently possible. 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

Many countries, including the United States, are simultaneously experiencing two contradictory trends: increasing opportunities for women that result from economic growth and increasing barriers that limit women’s voice and agency. Such barriers are growing across work, social, and political settings in both the Global North and Global South, alongside growth in women’s contributions to economic and political innovation. In some instances, however, political, economic, and social systems effectively foster women’s agency and voice toward gender equality. This project explores the conditions under which gender equality becomes more likely and identifies women’s solidarity as playing a key role. The project seeks answers to these and related questions: When do political, economic, and social systems support women’s solidarity? How, if at all, can women’s solidarity facilitate gender equality and universal societal improvements? This CAREER project advances a path-breaking research and educational program that leverages scientific innovation to facilitate inclusive development, social welfare, and gender equality. This project advances the theoretical and analytic bases for understanding the dynamics of women’s empowerment. The project aims to: 1) harness multi-method data generation to document the scope of gendered agency and women’s solidarity across space and time; 2) develop a theoretical of the dynamics of gendered agency and women’s solidarity to explain global variation in gender equality; 3) leverage “as-if random” changes in women’s rights and initiate field experiments to test theories of gendered agency, solidarity, and levels of equality; and 4) implement a multi-faceted educational program to establish an Open Database Project. The database element includes a replicable system of data collection, processing, digital infrastructure construction, and data analysis that is based on four core study cases. With the aim of deploying data science to analyze geospatial patterns of gendered agency in relation to rights, resources, networks, and power, the Open Database Project coincides with an interdisciplinary educational program that involves students from multiple disciplines (e.g., social science, law, gender studies, and computing and data science) in hands-on research. Through theory-driven data collection and analysis the project both supports and produces path-breaking research on gender and agency to enable welfare-enhancing equality in the United States and globally. 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.