University of Minnesota-Twin Cities

NSF Awards · FY 2024 · 2024-09

This research project focuses on improving data privacy and security within complex networks, a key issue in today's technology-driven society. As technology advances, stronger protections against new threats become crucial for complex systems, such as infrastructure networks, the Internet of Things, and global trade systems. The project outlines a comprehensive strategy to enhance network security: introducing preventive measures to stop data breaches before they happen, setting up advanced monitoring for quick detection of irregularities, and developing specific strategies for responding to security breaches. The project's implications extend far beyond security, poised to influence fields as diverse as social sciences, biological network analysis, and misinformation studies. Importantly, the project is also committed to educational excellence and knowledge dissemination. Integrating the project's findings into the academic curriculum aims to bridge the gap between theoretical research and practical application, nurturing a new generation of experts skilled in the nuances of network security. The project also extends significantly into student development, aiming to foster a nurturing environment for student involvement. It provides valuable research opportunities for both undergraduates and graduates. Through hands-on projects, software development, and data analysis tasks, students will gain practical experience and insights into real-world applications of their studies. Special emphasis is also placed on the involvement of students from traditionally underrepresented groups, which seeks to cultivate a diverse and vibrant research community. Furthermore, the initiative plans to release a suite of open-source software tools and databases, democratizing access to state-of-the-art methods in network analysis and security. These resources, coupled with workshops and tutorials, will empower researchers, practitioners, and policymakers to implement effective security strategies, fostering a safer digital ecosystem for all. Through these multifaceted efforts, the project contributes to the scientific understanding of network protection and champions the cause of equity and inclusiveness, ensuring that the benefits of secure and resilient networks are accessible to a broad swath of society. The project stands out for its innovative integration of statistical modeling, data privacy, deep learning, and optimization techniques, aimed at addressing the multifaceted challenges of securing complex networks. This blend of approaches, unusual in its breadth and depth, sets the endeavor apart in complex network research. First, the project introduces a cutting-edge approach to protect network data against privacy breaches and adversarial attacks, focusing on maintaining data utility. It establishes a novel framework for latent node-level differential privacy, applying distribution-invariant mechanisms to ensure that released network data safeguards both privacy and security without losing its intrinsic value. Second, the project develops flexible yet robust methodologies for timely detection and localization of network anomalies, utilizing nonparametric estimation for dynamic network behavior modeling. It aims to enhance network security monitoring by accurately identifying non-stationary change points and pinpointing anomalies, enabling prompt and effective responses to emerging threats. Third, the project introduces a comprehensive approach to tracking down the sources of misinformation in social networks, utilizing a broad spectrum of methods, including spectral methods, graphical models, and graph neural networks. It aims to efficiently identify misinformation origins and devise strategies to curtail its spread, thereby enhancing the reliability and integrity of information across social platforms. 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

Modern “omics” (e.g., transcriptomics or proteomics) studies often generate data using single-cell or spatially-resolved sequencing technologies. These technologies enable researchers to study, for example, the spatial variation of gene expression across cells or tissues, offering a high-resolution perspective of complex biological dynamics. This perspective allows researchers to better understand disease mechanisms and can lead to the development of novel treatments. However, the data generated by these technologies are high-dimensional and dependent, which can complicate statistical inference. Existing inferential methods are often subjective or unreliable, either requiring user input that may bias or invalidate results, or requiring rigid model assumptions that are frequently violated in practice. This project will address these issues by developing statistical methods that do not rely on user input, and work reliably in more general settings than existing methods. The new methods will be theoretically justified and equipped with fast computational algorithms. Software implementing these methods will be made publicly available, enabling their wide use in academia and industry. The project will also provide training opportunities for both graduate and undergraduate students. This project develops new statistical methods for inference with high-dimensional dependent data, motivated by challenges in analyzing single-cell and spatially-resolved sequencing data. Specific challenges include the failure of traditional inferential methods when the parameter is at or near the boundary of the parameter space; the need to both generate and test hypotheses from the same data without inflating Type I error rates; and insufficient model flexibility and scalability. The investigator will address each of these issues directly by (i) developing a new test procedure that resolves a well-known challenge of constructing confidence regions for variance components (or functions thereof) near zero; (ii) providing a unified approach for valid post-clustering inference with high-dimensional data from a broad class of distributions; and (iii) developing a general class of penalized mixture models that accommodates multiple latent sources of heterogeneity. The methodological developments in this project lay the groundwork for more general methods addressing more broad challenges in inference near the boundary of the parameter space, post-selection inference, and modeling heterogeneous high-dimensional data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

With the rapid growth of cloud computing, artificial intelligence (AI), machine learning (ML), and scientific computing, a massive amount of unstructured data is being created. To store and access unstructured data, Log-Structured-Merge tree-based Key-Value Stores (LSM-KVS) have become essential data storage systems, widely deployed by most of the large IT service companies. As application workloads vary and continuously scale up, existing LSM-KVS systems, designed based on monolithic servers and shared-nothing architectures, face numerous issues, including resource inefficiency, difficulty in load balancing, low scalability, and poor elasticity. This project aims to develop and optimize LSM-KVS-based systems within disaggregated infrastructure environments, consisting of multiple compute servers and heterogeneous memory and storage farms connected via fast networks. The project will address several fundamental issues, including heavy network traffic between resource pools caused by compaction and shard-migration, memory limitations of read and write buffers, tightly coupled control of otherwise decoupled resource-intensive modules by LSM-KVS, and more frequent and complex transient errors. The goal of this project is to redesign and optimize an LSM-KVS architecture for disaggregated infrastructure, called Decoupled-LSM, to achieve higher performance, better resource utilization, and improved management. The Decoupled-LSM project will devise new techniques that decouple data-intensive modules from the control of LSM-KVS, execute the modules in different resource pools efficiently, and attain high performance, better resource utilization, and greater flexibility in disaggregated environments. Decoupled-LSM will lay the foundation for a new LSM-KVS architecture optimized over disaggregated infrastructure for many critical applications, such as cloud computing, AI, ML, and scientific computing, which impact daily life. Overall, this project can help to better store, use, and manage extremely large-scale unstructured data, used by government, industry, and individuals. The proposed methodologies, system designs, and implemented components will benefit the storage and networking research communities in further developing storage systems for disaggregated infrastructure and cloud computing. The project will also involve students from underrepresented groups and outreach to high schools, along with collaboration with industry partners. 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

Human trafficking is a human rights abuse that negatively affects individuals, families and communities worldwide, including within the United States. Yet, despite continued efforts by researchers and practitioners in the field, there remain substantial gaps in knowledge on how to effectively prevent, disrupt, and dismantle human trafficking and remediate the harms human trafficking causes. What the field needs is large-scale and nuanced research on the interconnections between individuals, trafficking operations, the wide range of commercial sex market segments, community contexts, and root causes. This planning grant brings together a diverse group, including researchers from multiple disciplines, human trafficking survivor-leaders, and other stakeholders (e.g., service providers and law enforcement), to develop a national-level action research center and build a transdisciplinary team capable of carrying out this long-term research agenda. Participants will co-create a plan for building, organizing, and sustaining a new research center called “Human-Centered Action Research to Disrupt Trafficking (HART).” The planning approach for the HART Center uses a novel scientific approach to design a research agenda by converging social sciences, health sciences, and computational modeling with lived expertise from survivors of trafficking and other key stakeholders. The transdisciplinary team has a national scope with expertise to capture the realities and nuances of a broad range of trafficking contexts ethically and accurately and to translate that research to practice. This deep collaboration provides a realistic ground-truth for the direction and scale of research questions. It also enables identification and avoidance of unintended negative consequences that too often arise from human trafficking research and prevention and intervention efforts. Harms to avoid include, among other things, re-traumatizing survivors through invasive research surveys and interviews, over-focusing on some contexts leading to skewed results, inadvertently arresting victims in law enforcement interventions, and wasting resources on well-meaning, but ultimately ineffective strategies. The planning approach equalizes the playing field among participants with a carefully managed process that attends to power differentials, builds trust, and fosters shared understanding among people with diverse experience and perspectives. Methods for planning use cutting-edge strategies for team building, appreciative inquiry, and participatory collaboration through a series of interactive remote meetings culminating in an in-person convening. The results of the planning process are threefold: 1) develop shared values and research philosophy for the HART center; 2) identify key human trafficking research thrusts; and 3) build a team and project plan to address these thrusts that focus on the complex social, legal, economic, and human rights challenges of human trafficking. 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

Expert visual review of patient data is widespread in healthcare, which not only contributes to physician burnout but also introduces errors in clinical decisions. This is particularly emphasized in neurology where experts spend a substantial amount of time visually reviewing lengthy multi-channel time series of brain activity, called electroencephalography (EEG). Machine learning (ML) has emerged as a potential solution to ease this burden and create reliable and scalable solutions. However, most EEG ML models, which are based on supervised learning, do not yield meaningful EEG features because of labeling inconsistencies. In addition, these models have not been rigorously tested in out-of-sample settings and therefore can exhibit performance deficits during deployment leading to incorrect diagnoses or decisions. As such, there is a compelling need to develop more reliable, reproducible, and robust ML approaches for EEG review. The goal of this proposal is to develop a trustworthy ML framework to augment clinical EEG review and demonstrate its utility in real-world clinical applications. Our research will significantly improve the diagnostic capabilities of EEG while reducing physician workload. We will demonstrate the framework’s ability to augment EEG review by working closely with domain experts at the Mayo Clinic and Cleveland Clinic. We will also enable research opportunities for undergraduate and K-12 students, especially underrepresented minorities, and engage students with epilepsy in focused research projects. Finally, we will leverage the outcomes of this research to develop courses in engineering and medicine. This research will develop a suite of novel ML methods to realize a trustworthy ML framework to augment EEG review. We will undertake the following strategies to ensure trust in EEG ML: a) developing domain-guided backbone architectures for EEG representation learning, b) leveraging self and weak supervision, instead of label-hungry and error-prone supervised learning, to scale up available training data, and c) performing model diagnostics to identify and rectify failure scenarios. The core of the proposed framework will be a domain-guided foundation model for EEG data that addresses the current limitations of EEG ML. Our proposed work includes a) development of an attention-based domain-guided architecture to capture EEG spatiotemporal dynamics; b) designing domain-guided self- and weak-supervision tasks to address labeled-data scarcity; c) development of model diagnostics and adversarially robust training to handle distribution shifts; and d) real-world validation of the framework in epilepsy subtype classification and treatment outcome prediction, and further evaluation in out-of-sample settings. 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

Abandoned mines release very acidic water called acid mine drainage (AMD) that is rich in metals like iron, copper, aluminum, and arsenic. Bioremediation – using life, especially bacteria to remove metals and other toxins from AMD streams is more cost effective than other active treatment methods. In AMD bioremediation, bacteria take iron dissolved in water and turn it into rust. The rust then scrubs other heavy metals from the system. The success of bioremediation relies on how quickly and effectively these organisms can remove iron as rust. However, we don’t know what species remove iron the fastest or how or if iron is redissolved once it is buried. The overarching goal of this research is to unravel what controls the rate at which iron is removed, whether processes in the subsurface can undercut these processes by re-dissolving iron, and whether we can generate an environmental “probiotic” to increase iron removal in AMD sites. We do not know the species or the geochemical conditions that promote rapid Fe(II) oxidation. Therefore, the researchers will systematically link geochemistry and microbial metabolic potential to iron oxidation rate. Iron reduction in subsurface environments can undermine bioremediation efforts but little is known about biogeochemistry in the AMD subsurface. Therefore, the researchers will use porewater geochemistry and microbial communities to examine the biogeochemical processes occurring in the AMD subsurface. Seeding microbial communities is a promising strategy for enhancing bioremediation efficacy. However, these efforts can by stymied by complex interactions between geochemistry, ecology, and dispersal. Therefore, the researchers will perform a field scale AMD seeding experiment using constructed AMD ecosystems to determine if seeding is feasible. The researchers will also make significant contributions to undergraduate science education by developing a course-based undergraduate research experience, a data-rich module for undergraduate courses and offering a summer research opportunity for 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 · 2024-09

This EArly-Concept Grant for Exploratory Research (EAGER) project will identify gaps in translating traffic control theoretical research into practical traffic controls (such as traffic signals, ramp meters, and congestion pricing) in real-world settings and will attempt to discover scientific reasons for these gaps. Although academics have developed complex new controls built on models that are supposed to be more accurate, the traffic controls used in practice rely on theories and models that are at least two decades out of date, with no plans to implement the new methods that already exist from research. This issue leads to a fundamental research question: can controls based on more advanced theories perform better than legacy theories in practice? A positive answer will provide a tighter connection between research and practitioners to justify use of new research, and a negative answer will identify causes for performance differences that will inform future research and prepare for next-generation design of transportation and traffic control systems. The societal benefits could include reduced traffic congestion and lower travel times, which improves the well-being of travelers. This project also advances the field by improving the likelihood that traffic control research in general will become useful to practice. Results will be disseminated through conferences, curriculum redesign and development, as well as collaborations with industry and community partners through various educational and outreach activities. The technical approach to the research is based on studying differences between widely accepted traffic flow models and real traffic from public vehicle trajectory data in the new transportation era. Kinematic wave theory is the most common macroscopic flow model, but how useful is the continuous partial differential equation for describing discrete vehicle traffic? How does heterogeneity in vehicle types and driver behaviors affect model accuracy, which is well-known as the variance in the congested side of the flow-density relationship? How does stochasticity in travel demand and route choices affect controls built for the average value? By modifying traffic flow models to include specific characteristics (such as time- and space-varying flow-density relationships caused by heterogeneous vehicles), researchers will test the importance of such characteristics on predicting reality. What are the important traffic characteristics to consider for different types of traffic controls? Answering this question might validate that specific existing traffic controls are likely effective in practice, or lead to new traffic flow models that incorporate important traffic behaviors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This research project will enrich and expand the Integrated Public Use Microdata Series (IPUMS). IPUMS is the world's largest population database, with information describing over 2.5 billion individuals drawn from censuses and surveys of 157 countries. To meet the challenges created by rapid demographic, economic, and environmental change, researchers must have full and open access to the best possible information. This project will expand the geographic and chronological coverage of the database, preserve it for future generations, and make the data available to users around the world. IPUMS data already have stimulated new research that transcends national boundaries and static interpretation. The data are broadly used by national and international agencies to inform policy. This project will enhance scientific understanding of critical policy-related issues such as population aging, international migration, and the effects of government programs on economic development and well-being. The project will promote teaching, training, and learning through three mechanisms. IPUMS will provide online data analysis and promote the sharing of curricular materials. Training workshops will be conducted, and the research team will further develop online training modules on how to use the data. The project will continue to employ a diverse group of graduate and undergraduate research assistants, including members of underrepresented groups, who will develop valuable skills in an interdisciplinary environment. This research project will conduct five major activities. First, the research team will obtain and preserve data from international censuses and household surveys, focusing on data from the most recent round of censuses. Second, the research team will clean and process the data, drawing samples, correcting errors, applying confidentiality edits, and coding the data consistently across countries. Third, the investigators will develop comprehensive documentation to guide users on the meaning of census and survey responses and their comparability across time and space. Fourth, the research team will improve geographic identifiers in the data to allow detailed spatial analysis and improved interoperability across IPUMS data collections. Fifth, the project provides education and dissemination activities designed to strengthen relationships with both the research community and with partner statistical agencies and uses machine learning to produce machine-understandable metadata for dissemination to the research community. These activities will multiply the quality, quantity, accessibility, and interoperability of information about the changing human population, creating a transnational resource of unprecedented power for understanding human society on a global scale. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Explosive objects in the distant universe can now be studied by simultaneously combining information from multiple messengers - gravitational waves, particles, and light. The investigators will develop software to deliver new discoveries and physical constraints concerning the nature of explosive objects. The investigators will provide students opportunities for cross-institutional internships and collaborations with amateur astronomers and citizen scientists. The research, methods, and visualizations will be directly included in developing courses at multiple institutions. The work will provide training for students in critical areas for astrophysics and beyond, including robust application of machine learning. The team will partner with the LIGO Science Education Center and The Baton Rouge: Bringing Youth Technology, Education and Success programs to utilize multimessenger astronomy to inspire K-12 students in the state of Louisiana. A 4-year research program led by investigators at the Louisiana State University, Harvard University, University of Minnesota-Twin Cities, and University of Maryland, College Park will improve our understanding of explosive transients. The exotic zoo of explosive transients is still being explored, and the overlap of signals seen at different wavelengths is key to their taxonomy. Explosive transients occur at the extremes of physics, beyond the reach of terrestrial laboratories. Multiwavelength and multimessenger observations of these transients enable advances in areas including gravity, fundamental physics, dense matter, cosmology, and the origin of the elements. The proposed work will enable new discoveries through the power of the Vera Rubin Telescope with concurrent observations provided by high energy and gravitational-wave observatories. The research team will combine observations of compact objects with the Vera C. Rubin Observatory’s Legacy Survey of Space and Time with space-based gamma-ray burst monitors and ground-based gravitational-wave interferometers. Focusing on gamma-ray bursts and supernovae, the team will construct new optical transient classifiers, develop the formalism to associate distinct signals across wavelengths and messengers from the same event, characterize these events through dedicated follow-up, and enable global discovery via public alerts. The result will be an end-to-end multiwavelength and multimessenger discovery machine. 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

Shape memory alloys, such as nickel-titanium (also known as nitinol), are a type of “smart” material widely used in medical devices. Nitinol is a metal with remarkable properties including super-elasticity, which is the ability to stretch like rubber and recover the original shape after unloading, and the shape-memory effect, in which a bar bent to a “permanent” shape like a paper clip, recovers its original straight shape after heating. These characteristics are exploited in a variety of important biomedical devices, such as cardiac stents. However, repeated loading of these devices after they are implanted in a patient, for example expansion and contraction due to blood flow driven by a beating heart, can cause them to break at microscopic material defects, such as non-metallic inclusions. This NSF/FDA Scholar-in-Residence at FDA (NSF FDA SiR) research project seeks to improve the ability of engineers to design nitinol devices that are less likely to fail by developing a specialized multiscale computational method that uses artificial intelligence (AI) to model the behavior of the nitinol atoms in the small volume near a defect. The method will be validated against specially designed fracture experiments to ensure its correctness. This approach will speed development of new medical devices, improve regulatory pathways to market, and reduce the risk to patients. This research approach is based on the three-dimensional quasicontinuum method (QC3D), which is concurrent multiscale method that dramatically reduces the computational cost relative to fully-atomistic methods through a coarse graining approach. Full atomistic resolution is retained in regions where “interesting” phenomena is occurring, such as phase transformations or bond breaking near a defect, whereas the rest of the device is modeled using a nonlinear finite element approximation employing Cauchy-Born kinematics. QC3D is a systematic approximation to the exact fully-atomistic result, which converges with mesh resolution. However, agreement with reality depends on the accuracy of the interatomic potential (IP) used to model the atomic interactions in the atomistic regions and as the basis of the Cauchy-Born constitutive response. To validate the QC3D approach, predictions using existing physics-based IPs and new AI-based IPs will be compared with fracture toughness experiments on single crystal and/or polycrystal nitinol samples. Experiments will include standard 3-point bending experiments as well as a novel fracture gap test that can be used to explore the effect of compressive stress on phase transformations at the crack tip. The validated QC3D method will be applied to study the effect of non-metallic inclusions on nitinol fracture. 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

Climate change stresses ecosystems in new and often alarming ways. Beaver-based restoration is gaining momentum as a nature-based climate solution in North America. However, there is a need for greater collaboration and coordination amongst researchers, managers, and policymakers. This Research Coordination Network (RCN) will synthesize existing research on the relationship between beaver management practices, beavers, and beaver-related ecosystem services in a way that reflects local and regional factors across the continent. It will identify variability in legal constraints on beaver management across political and ecosystem boundaries. It will facilitate knowledge transfer between management practitioners and research communities at local, regional, and continental scales. It will then assemble a summary of the "state of the science" that identifies key knowledge gaps and opportunities for progress in both basic and applied beaver science. This RCN will ensure that policy and land management decisions are grounded in data to maximize the potential for the beaver management as a nature-based solution. Co-production of key material will also open the door for voices that have been historically minimized, to be heard. To achieve these goals, the network will collate information from peer-reviewed and grey literature to identify knowledge gaps and misalignment between science and practice. The results of this literature will be archived in databases of geographically referenced beaver research findings, management practices, and policies that are set up for use in future meta-analyses. Four regional workshops and one continental-scale workshop will be convened to further explore these issues. Through the workshops and webinars, the reach and efficacy of existing professional networks within the beaver science and management communities will be extended. The RCN will thereby support collaborative, multidisciplinary discussions within and between geographic regions on how to best move the beaver science and management community towards effectively fostering climate resilience. 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

After three days (17–20 cm) of rainfall flooded the Blue Earth River, the river avulsed around the western edge of the ~114 year old Rapidan Dam in the early morning of 24 June 2024. Rapid incision and lateral migration shifted the bank ~100 m westward in three days. This event, which the investigators have termed an “avulsive dam failure”, provides a brief time window to study the fundamental coupled fluvial and hillslope processes that result from rapid base-level fall, including knickpoint development and retreat coupled with lateral erosion alongside mass-wasting processes associated with sudden valley incision. Climate and land-use change combine to generate larger floods whose erosion then destabilizes the surrounding landscape, thereby producing cascading hazards. The investigators hypothesize that rapid incision and channel migration should immediately follow avulsive dam failure, similarly to basin integration following spillover when a new hydrogeomorphic system is established after breaching a sill. This is followed by relaxation of the river’s longitudinal profile as knickpoints evolve and the river adjusts to a new local base level. The investigators will capture and analyze these geomorphic phenomena through time with repeated collection of unmanned aerial systems (UAS) imagery, supplemented by community-collected data. They will quantify volumes of erosion and deposition, flow velocities, and channel-migration rates using data products derived from structure-from-motion photogrammetry and aerial footage. Aging dam infrastructure across the United States, combined with increased magnitude and frequency of precipitation, will likely drive continued dam failures into the future. The data resulting from this work could inform future management strategies, especially as our national dam infrastructure continues to age. This project will provide high-impact research experiences for both undergraduate and graduate students, while serving to mentor them under a collaborative, multi-institutional environment with broad expertise in geospatial and geoscientific disciplines. Rapid data collection following this avulsive dam failure will help to answer core geomorphic questions about basin integration and subsequent knickzone evolution. Physical models demonstrate that much of the incision from spillover processes occurs during and shortly after the event. Attempts to quantify these changes in a real-world setting must effectively capture these early stages that record the most rapid change within the fluvial system. Additionally, longer-term data will capture knickpoint-retreat rates, including whether the knickpoint remains coherent or diffuses, across stratigraphic units with varied mechanical properties (that is, glacial lake sediments to sandstone bedrock). Over the shorter term, the results of this project may help to explain and predict rapid bluff retreat along the Blue Earth and other rivers, attributed to anthropogenic climate and land-use change. Furthermore, the investigators will quantify downstream sediment dynamics in response to erosion and mobilization of reservoir deposits. 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

Forecasting how our environment will change into the future requires the scientific community to understand the processes that shape Earth's surface environments through time. To do this, geoscientists collect images of Earth with satellites, run simulations, and test hypotheses with laboratory experiments. All of these methods improve our understanding of landscape change, but scientists using each of these tools struggle to bring their research together to make new insights. This project establishes a framework of interoperable hardware and software tools, called sandpiper, that enables research products from different teams and approaches to integrate with one another more easily than ever before. Major efforts of the project team include (1) designing and implementing an affordable open-source hardware-firmware system for data acquisition, (2) forging a community-backed data standard, (3) developing a flexible and interoperable data-analysis software library, and (4) establishing a sustainable community of practice. The project team is also advancing science and technology education by creating science museum exhibits that demonstrate fundamental principles in geomorphology and reach a wide audience through an interactive web interface. Recent strides in geomorphology have been fueled by widely available satellite imagery, powerful numerical modeling toolkits, and decades of physical laboratory experiments. Customized algorithms lie at the heart of the discipline because raster data—e.g., photographs, topography—form a fundamental bridge between these complementary modes of inquiry. Transformative insights can arise when researchers apply tools from one mode of inquiry to data from another. However, most innovation at the forefront of geomorphology currently proceeds in silos via ad-hoc algorithms that accumulate “mutations” as they traverse laboratories and graduate-student generations. The problem is particularly acute for experimental geomorphology, where technological barriers have prevented FAIR (Findable, Accessible, Interoperable, Reusable) and OS (open-source) principles from integration into the research process. At present, there is no unifying framework to support collaboration between modelers, observationalists, and experimentalists. The team for this project is creating such a cyberinfrastructure framework and solving these problems at every level. (1) To break down experimental silos, the project team is designing and implementing a modular and extensible open-source hardware–firmware system to affordably and uniformly make measurements and generate reproducible data products in labs across the world. (2) To promote and simplify data sharing, the project team is organizing a community effort to forge a data standard. (3) To mitigate algorithm drift, the project team is developing a flexible analysis library that integrates with this data standard. (4) To establish a community of practice, the project leaders are engaging researchers in their own laboratories and computing environments to facilitate reusing and contributing algorithms to the library. This acquisition-to-analysis toolchain, called sandpiper, will enable the next generation of collaborative research in geomorphology, sedimentology, and stratigraphy; advances could also influence seemingly unrelated fields like dendrochronology, hydrology, and seismology. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the National Discovery Cloud for Climate initiative within the Directorate for Computer and Information Science and Engineering and by the Geosciences Directorate’s Research, Innovation, Synergies, and Education and Earth Sciences divisions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The Collaborative Research in Computational Neuroscience (CRCNS) program supports a broad spectrum of investigators advancing computational understanding of nervous system structure and function, mechanisms underlying nervous system disorders, and computational strategies used by the nervous system. The goal of this meeting of CRCNS Principal Investigators is to foster interaction and collaboration across this vibrant community, highlighting the intellectual advances and broader impacts of CRCNS awardees. The meeting, scheduled for Aug. 20-21, 2024 in Minneapolis, Minnesota, is hosted by the University of Minnesota and includes poster presentations, talks, and plenary lectures, covering all areas of computational neuroscience represented by funded projects in the program. The meeting will include projects involving the United States, France, Germany, Israel, Japan, and Spain, sponsored by NSF and eight other partner agencies. This international meeting should have a significant impact on the participants and the future of the CRCNS program. The meeting results are likely to include new research directions that will be publicized to the research community through publications and the meeting website. The results dissemination should inform and the CRCNS research community in their transdisciplinary collaboration, educational activities, and spur other innovative research directions. The broader impacts of the meeting are to facilitate progress in the field and stimulate conversations, connections, and collaborations that will lead toward better informed and effective CRCNS research and resulting technologies for the broadest possible user populations. 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 and the Office of Strategic Initiatives of the Directorate of Mathematical and Physical Sciences, Professor Marc A. Hillmyer of the University of Minnesota and his team will carry out a fundamental research project aimed at the discovery and development of new polymers critical for sustainable future. They will implement groundbreaking approaches to the generation of new rigid plastics and flexible rubbery polymers based on biobased polyesters. These polymeric compounds will be designed to achieve outstanding physical properties and to be industrially composted at the end of use. Solutions that will be explored in this work promote a practical circular plastics economy. The team will use the modern tools of polymer synthesis to optimize the molecular features of these new polymers to promote sustainability. The work will benefit the well-being of individuals given the urgent need to solve our pressing plastics predicament: modern society depends on these polymers and continually expects increased performance but suffers dire consequences from the associated pollution. The work will also promote a globally competitive workforce through training of diverse researchers in area of polymer chemistry and sustainable polymers. The work described in this proposal will advance our understanding of how architectural control, stereocomplex formation, blend compatibilization, and morphological design in a class of promising aliphatic polyester block polymers can be harnessed in ways that optimize and valorize a class of materials that will positively impact the field of sustainable polymers. Accessible tools of modern polymer chemistry will be implemented to determine the scope and adaptability of numerous strategies to generate high-performance polymers in aliphatic polyester block polymers. The interplay between molecular structure and polymer self-assembly will play a central role in the research activities. The work will build a strong foundational base for researchers to implement designer approaches to other classes of sustainable polymers and demonstrate the range of possibilities to elevate their development going forward. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

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

NSF Awards · FY 2024 · 2024-08

This project focuses on one of the most pressing questions in the study of human evolution, the replacement of Neanderthals by modern humans. Neanderthals, who evolved in Europe and were established there for over 200,000 years, vanished from the fossil record shortly after modern humans entered the area approximately 50,000 years ago. Current explanations for Neanderthals' disappearance center on their inability to compete with modern humans, who had symbolic culture and more advanced technologies. Yet, genetic evidence shows that the two populations made contact and interbred. What was the nature of this contact? Archaeological evidence to answer this question is present at one archaeological site. Fossilized bones of Neanderthals and modern humans have been found in the same stratigraphic layer, meaning that the two populations were contemporaneous. However, some evidence suggests that the modern human fossils may be younger and accidentally became incorporated into an older layer. The focus of this project is to apply numerous scientific techniques to test whether the fossils were deposited at the same time or not. If contemporaneity is confirmed, this site will be the first to provide direct archaeological evidence of the period of interaction when modern humans and Neanderthals met. In addition to answering the scientific questions, the project will train students in cutting-edge methods of archaeological excavation and analysis and provide field as well as lab-based research opportunities for undergraduate and graduate students. Career-building opportunities abound. Undergraduate students needing fieldwork to get jobs or get into graduate school gain valuable skills through their participation in the project. Graduate students' theses benefit from their access to first-class research materials, and networking opportunities with scientists from the U.S. and other countries will expand their job prospects. This project helps to clarify a crucial period within human evolution. The site is likely one of the earliest sites where such contact occurred. In order to test the contemporaneity of the Neanderthal and modern human fossils, specialists examine the preservation of the bones, radiocarbon date them, and attempt to extract DNA from them. The geological stratigraphy of the site is studied using microscopic and chemical analytical techniques. All of these analyses are necessary to confirm the rare discovery that modern humans and Neanderthals were present at the same time, in the same rockshelter. 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

While forward optimization methods seek to calculate the optimal values of decision variables for given values of model parameters, the goal of inverse optimization is to infer parameters that render given values of decision variables optimal, i.e., prescribing needed actions or inputs to achieve an optimal result. This grant will contribute to the advancement of national health, prosperity, and welfare by developing a computational framework to efficiently solve a large class of inverse optimization models. The methodology will be applied to system identification problems in cancer radiotherapy to help validate current treatment protocols. The PI will mentor doctoral students on this research topic throughout the project. Results will be incorporated into a graduate-level course and two new books that the PI is drafting, as well as workshops and seminars on applications of optimization for underrepresented students in STEM. The current inverse optimization literature focuses almost entirely on imputing objective function parameters. There has been little work on imputing constraint parameters because these inverse optimization models are nonconvex, bilinear and hence difficult to solve. The project will pursue two approaches to solve these models: (1) conversion into equivalent convex problems via a variable transformation, if possible; and (2) a suite of tailored approximation algorithms that solve a sequence of convex problems, if not. The researched methods will be evaluated computationally against classic branch-and-bound algorithms using several publicly available data sets, together with an in-depth case study in cancer radiotherapy. 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 rapid expansion of workforce development for future computing technologies is needed to meet the demands of the semiconductor industry in creating next-generation computing and memory devices. Despite its crucial role in contemporary technology, the semiconductor industry lacks the visibility of more glamorous sectors such as internet and software technologies. The semiconductor workforce requires a unique and complex set of skills across diverse fields, which typically necessitates specialized training through advanced undergraduate and post-graduate programs. This team is dedicated to educating the next generation of skilled technical professionals, focusing on materials and device co-design approaches through innovative immersive experiences. Summer school programs at the college level can effectively raise awareness about workforce needs in the country, the exciting deep technologies, vast career opportunities, networking prospects, and the enjoyable science involved in this work. This summer school, with its multi-pronged, holistic approach that incorporates academic and industry participation, aims to provide students with training and experience in "Quantum + Chips." Additionally, the goal is to inspire students by demonstrating the fun aspects of the physics underlying these technologies. The planned summer school will be held at the University of Minnesota from July 29th to August 9th, 2024. This 2-week immersive summer experience is designed for undergraduate students, from freshmen to seniors, to expose them to a wide range of computing technologies and paradigms. The first week includes curated lectures focusing on physics and computing fundamentals, computer labs, experimental labs, and demonstrations. Topics covered include fundamental concepts of quantum mechanics, semiconductor physics, carrier statistics, quantum transport, transistors, spintronics, and quantum computing, among others. The second week features company visits and talks by industry and academic experts on the latest advancements in computing devices and technologies. Technical talks will cover topics such as transistors, optical computing, spintronics, Ising computing, and quantum computing. Speakers from participating semiconductor and quantum technology companies will provide technical and career talks, along with company tours. Student lodging will be provided for the summer school participants. Surveys indicate that students enjoy the dormitory experience and the networking opportunities with their peers in other institutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

A potential cause of persistent racial disparities in health outcomes in the US may be a lack of representation in medical personnel and medical research. This Award will fund a research project that will investigate the effects of the Civil Rights Movement (CRM) on the supply of Black doctors and the conduct of medical research. The research project will focus on two questions: (i) What effect did the Civil Rights Act of 1964 have on the representation of African Americans in medical education, and how did this affect the supply of Black physicians, especially in underserved communities? (ii) How did the CRM impact the focus of medical research on racial minorities? The researchers will build a large, detailed data set on historical racial composition of medical school admissions, racial composition of medical doctors, federal grants for medical research, and the racial composition of recruitment into biomedical research. This impressive data collection will allow the researchers to answer the questions they set out to investigate. The results of this innovative research will help policy makers design efficient policies to reduce racial health disparities in the US. This Award will fund a research project to answer two inter-related questions: The effects of the Civil Rights Movements on the supply of Black physicians and the inclusion of racial minorities in medical research. The PIs do so by building a novel dataset that combines historical: (i) student records from medical universities, (ii) physician directories, (iii) scientific publications, (iv) population health outcomes, and (v) federal funding for medical research. The core data on medical graduates between 1955 and 1980 will be collected from primary sources. The PIs will use a continuous difference-in-differences strategy to identify the impact of Title VI of the 1964 Civil Rights Act on medical schools. The exogenous nature of this policy change will allow the PI’s to establish causality. This research will provide evidence of the lasting influence of the CRM on medical education, the supply of physicians to under-served groups, physician career trajectories, and medical research. The results of this innovative research will help policy makers design efficient policies to reduce racial health disparities in the US. 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 grant seeks funds to partially fund AGU Chapman conference (https://www.agu.org/chapman-particle-precipitation). This activity will bring together experimentalists, modelers, mission designers, and industry partners with the goal of bolstering cross-field communication and collaboration within the atmospheric, ionospheric, and magnetospheric disciplines. Energetic particle precipitation (EPP) occurs when electrons and ions from the sun or the terrestrial magnetosphere enter the atmosphere and collide with atmospheric particles, depositing energy in the atmospheric system. EPP is one of the main drivers of space weather and has important implications in the interconnected atmosphere-ionosphere-magnetosphere (AIM) system. The resulting space weather can disrupt communication and power systems, present a radiation hazard to astronauts and at aviation altitudes, and increase satellite drag leading to orbital decay. AIM dynamics are highly complex and remain poorly understood and constrained, limiting the ability for models to provide understanding and accurate predictions. The conference will establish a forum for cross-community discussion and knowledge exchanges, identify possible funding sources for future collaborations, and give students and early-career scientists a strong voice in the future of this community. EPP is one of the fundamental drivers of space weather the coupled atmosphere-ionosphere-magnetosphere (AIM) system. EPP has been recognized as an important component of climate (World Meteorological Organization (WMO), 2018) via its ability to indirectly destroy ozone, modifying local radiative balance in the middle and upper atmosphere. Measurements from our current observational fleet are not able to fully capture EPP-driven AIM dynamics. This Chapman conference will bring together participants from the AIM communities to focus on the four following themes: Theme 1 - View from the bottom: Dynamics of middle/upper atmosphere coupling driven by EPP. Theme 2 - View from the top: Dynamics of solar and magnetospheric forcing of the atmosphere/ionosphere via EPP. Theme 3 - How can modeling and observations bridge gaps in knowledge of regional coupling? Theme 4 - Future: What is the potential role of existing/upcoming observations or new techniques to allow models to better capture coupling physics and make predictions? The activity is jointly funded by Aeronomy and Space Weather programs within the NSF’s Division of Atmospheric and Geospace 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 · 2024-08

The project focuses on a group of pollutants known as per- and polyfluoroalkyl substances (PFAS). Through their use in numerous applications, these chemicals have made their way into drinking water, thus posing potential risks to public health. The research team will study how PFAS react with other treatment chemicals during water disinfection. While water disinfection protects public health, transformation of PFAs into other potentially toxic chemicals represents an urgent issue. The project explores the reactions between PFAs and common disinfectants such as chlorine and ozone. The research thus addresses a crucial step in developing more effective water treatment methods and contributing to the overall health of communities nationwide. More broadly, the project will enhance public understanding of water treatment processes and promote STEM education, offering long-term benefits to society. The project goal is to determine the transformation mechanisms of PFAS (and their precursors) during conventional drinking water disinfection processes. Given the widespread presence of PFAS in drinking water sources and the potential health risks associated with these pollutants, understanding the behavior and breakdown of PFAS in response to common water-treatment disinfectants such as chlorine, chloramine, bromine, and ozone is paramount. Specifically, the project involves controlled laboratory experiments investigating the oxidation of polyfluorinated substances to more persistent perfluoroalkyl compounds under varied conditions. Effects of bromide ions, natural organic matter, and various disinfectants will be investigated. By integrating experimental results with computational chemistry analyses, the research will unravel the complex interplay of thermodynamic and kinetic factors influencing PFAS precursor transformations. Additionally, the feasibility of sorptive removal techniques for extracting PFAS precursors from water will be examined, potentially offering a practical remediation strategy. The project is poised to significantly advance our understanding of PFAS chemical stability and reactivity, thereby informing the development of more effective drinking water treatment solutions. Moreover, the project will generate valuable resources, such as an open-access database of disinfection byproducts, and contribute to the scientific community's knowledge base, addressing a critical gap in current PFAS research and regulation 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 · 2024-08

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Siepmann's group at the University of Minnesota - Twin Cities is collaborating with Stephanie Schuster at Advanced Materials Technology and Mark Schure at Kroungold Analytical Inc to develop accurate molecular models for chromatographic systems. Chromatography is widely used for the analysis and separation of complex mixtures of molecules and macromolecules in chemical, pharmaceutical, and bio-technology applications. The Siepmann team seeks improved fundamental understanding to better guide the choice of materials (i.e., chromatographic phases) impacting the retention processes that govern the separation. The work extends to include consideration of sustainable mobile phases which can reduce chemical waste. Beyond these technical impacts, the research provides excellent training opportunities for the next generation of researchers, utilizing partnerships with academic and industrial researchers. The lack of molecular-level information for chromatographic retention processes is a bottleneck that hampers the development of novel stationary phases and adoption of more benign mobile phases. The collaborative research team led by Dr. Siepmann combines expertise in molecular simulation, synthesis, and characterization. Complex molecular models, accurate force fields, and efficient simulation algorithms enable high-fidelity predictions of chromatographic retention processes. The general goals are threefold: (i) to predict retention orders, without adjustable parameters, in chromatographic systems; (ii) to provide microscopic-level insight into the processes underlying these separations; and (iii) to utilize this knowledge to guide the design of chromatographic stationary phases and sustainable mobile phases with improved performance. Computational studies are experimentally validated. This integrated research approach is being applied to hydrophilic chromatographic phases (HILIC with bonded polar ligands), hydrophobic phases with limited flexibility (phenyl-hexyl), supercritical carbon dioxide/(water or methanol or ethanol), and hot, compressed water mobile phases. It is also being used to elucidate the influence of pore size/shape/topology and functional groups for superficially porous particles on adsorption isotherms and wettability. This university-industry partnership provides unique opportunities to advance the education and training of undergraduate and graduate students by allowing for extensive interactions with industrial researchers and experiences with real-world applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

One of the key questions in astrophysics today is how to discover and characterize the coalescence of compact binary systems, such as black holes and neutron stars. A crucial aspect is connecting expectations for electromagnetic counterparts to gravitational-wave signals. This award will enhance existing search strategies and improve the analysis of upcoming discoveries by increasing the speed of analysis algorithms and incorporating these expectations into searches for those counterparts. It will also facilitate student exchange between the University of Minnesota and the University of Potsdam in Germany, providing invaluable multicultural research experiences for young researchers. Given the extensive searches for further binary neutron star mergers, there is an urgent need for improvements in the speed and quality of data products provided to the community. For this reason, the award focuses on (i) Extending existing multi-messenger Bayesian inference software to rapidly predict electromagnetic counterparts while minimizing computational costs during gravitational-wave inference; (ii) Using these advancements to improve searches for multi-messenger sources; (iii) Predicting observing scenarios for future observing runs and the third generation of gravitational-wave detectors; (iv) Analyzing ongoing gravitational-wave observing runs. The award will fund training for students at the intersection of multiple fields, offering a unique opportunity to train a new generation of international scientists who can become key players in the near future. 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 2024 Yamabe Memorial Symposium will be held at the School of Mathematics of the University of Minnesota - Twin Cities, from Friday, October 4th to Sunday, October 6th, 2024. The Yamabe Memorial Symposium is a prestigious biennial conference in geometry and topology, established in 1962. It is renowned among geometers and topologists for its high-level, cutting-edge talks, strong support for U.S. graduate students, and its ability to connect leading experts with junior researchers through well-organized events. This year, the symposium will uphold this tradition, offering a comprehensive exploration of various aspects of symplectic and contact geometry in light of recent breakthroughs in these fields. Recent major breakthroughs include advancements in the foundations of Floer theory, such as the development of stable homotopy theory for Floer theory, the introduction of global Kuranishi charts, and their applications to the Arnold conjecture over integers. Additionally, Floer theory has been applied to symplectic topology, including the refutation of the simplicity conjecture. Complementary to Floer theory, significant progress has been made in the study of Hamiltonian torus actions and topological methods in higher-dimensional contact structures. Eight confirmed speakers, comprising leading experts from around the world, will ensure comprehensive coverage of these areas. The webpage for the Yamabe Symposium is https://cse.umn.edu/math/yamabe-memorial-symposium 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.