University of Minnesota-Twin Cities

NSF Awards · FY 2024 · 2024-10

This project examines how a more accurate curriculum about the diversity of sexes found across species, the role of the environment in sex determination, and the complex relationship between sex and gender can create a more inclusive environment for transgender, non-binary, and gender non-conforming (TNG) students in undergraduate biology courses. Research indicates that rather than emphasizing the diversity of strategies and experiences that organisms have around sex, gender, and orientation, biology courses often inaccurately categorize sex and gender as binary. The oversimplification of sex and gender into binary categories can make biology classrooms particularly challenging for TNG students. Early data suggest that how sex and gender topics are represented in the biology curriculum impacts TNG students’ sense of belonging and interest in biology. Understanding TNG students’ experiences with biology content will support the design of interventions and curriculum inclusive of both TNG and intersex students. This project will also help all biology students develop inclusive and scientifically accurate understandings of sex and gender. Finally, this work will positively impact the career competencies of all biology majors who will need skills and knowledge to work with diverse patients, stakeholders, and teams. Guided by master narrative theory, the goals of this project are to: 1) explore how sex and gender are currently represented in the undergraduate biology content, 2) describe the impact this content has on classroom climate and belonging for TNG students, and 3) characterize the current efforts of biology instructors to create a more inclusive climate for TNG students. Master narrative theory deciphers how messages in the cultural environment become internalized and impact the development of personal identity. The sample will include TNG students with diverse racial/ethnic and social identities along with biology instructors recruited from a variety of institutions. Data collected will include participant interviews (recorded and transcribed), participant baseline demographic surveys, course observations (e.g., video recordings), and course artifacts (e.g., lesson plan, assessment questions). Feminist phenomenology, qualitative content analysis, and document analysis will be used to analyze the data. The anticipated outcomes of this project include (a) identifying aspects of biology content that could influence the sense of belonging of TNG students and impact the career competency of all biology majors, (b) describing factors that can help or hinder instructors as they try to create more inclusive and accurate biology curricula related to sex and gender, and (c) creating professional development materials to support instructors who design lessons around biology topics related to sex and gender. This project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent. 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 · 2024-10

This National Science Foundation Innovations of Graduate Education (IGE) Track 2 award to the University of Minnesota will assess the different ways science graduate students are matched with a faculty advisor. Recent reports suggest that graduate students increasingly struggle with their mental health, work-life balance and sense of belonging. Faculty advisors play a core role in the experiences of science graduate students in the United States. They often have significant control over a student’s stipend, workspace, resources, research project, collaborators and, ultimately, time to graduate. The influential position of the advisor makes the choice of advisor pivotal to graduate students’ progress. This project contributes to knowledge about factors affecting graduate student success by investigating the methods used to pair graduate students with an advisor and the impact those methods may have on graduate students’ sense of belonging, intent to persist in their programs, and satisfaction with their advisors. The researchers will explore the experiences of administrators, faculty, and students with the practices and policies of various recruitment methods to identify what pre-existing elements should be removed or modified. The researchers will investigate the experiences of three stakeholder groups (graduate students, faculty advisors, and graduate program coordinators) with different graduate student recruitment methods (e.g., rotations and direct admission) into life sciences, chemistry, and physics. To achieve this goal, the researchers will carry out four interconnected aims. They will use a national survey of graduate students to test the hypothesis that the method of recruitment impacts students’ experiences with their advisors, interest in completing their graduate training and their sense of belonging to their research group and their program. Interviews with three stakeholder groups will be used to fulfill the three remaining aims. Interviews with faculty advisors will provide insights into how different recruitment methods impact their decisions on which students to accept. Graduate program coordinator interviews will provide information about how recruitment methods are implemented in their programs, the challenges with these approaches, and the reasons why a program uses a particular recruitment method or methods. Finally, interviews with graduate students who switched labs will reveal how students navigate program policies about changing advisors and the effect of recruitment methods on this process. Combined, these stakeholder perspectives will reveal affordances and constraints of different recruitment methods allowing for suggestions about policy recommendations and best practices. This work will contribute to knowledge about factors contributing to graduate student success and provide a foundation for programs to make data-informed decisions about recruitment procedures. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community. 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 · 2024-10

This project investigates strategies that combine both human and machine attention for improving human-machine collaboration for the analysis of large, complex data sets. Open-source platforms for online citizen science, such as the Zooniverse, now provide infrastructure to incorporate machine learning alongside volunteer classifiers enabling human-in-the-loop techniques. Citizen Science is an established method for carrying out distributed analysis of large quantities of data in which online volunteers help with tasks requiring human pattern recognition. Examples include identifying shapes of galaxies in the Galaxy Zoo project, determining animal species in images taken by remote cameras in the Snapshot Serengeti project, or helping transcribe handwritten texts from historical documents such as the Civil War Bluejackets project. However, much larger datasets are looming on the horizon. Designing a human-machine system to accelerate labeling of known classes at the same time as solving the problem of detecting interesting anomalies (suggesting new phenomena) requires answering several crucial research questions about how humans and machines best complement one another. One promising direction is showing what the machine “thought” was anomalous within a given image to volunteers for further inspection. Additional benefits of this project include engaging nearly 3 million members of the public who participate in citizen science through Zooniverse and giving them the opportunity to learn more about how machine learning really works, engaging young women in University of Minnesota computer science coding camps, and providing capstone projects for Data Science Masters program students to engage in real-world research while preparing them for careers in data science. Taking advantage of the exceptionally large labeled datasets available through Zooniverse projects and the fact that the majority of Zooniverse projects are image-based, the research effort will investigate training and deploying Vision Transformers (ViTs). Specifically, the impact of combining human and machine attention and anomaly detectors on a real-world citizen science platform will be explored with the two objectives: (1) Classification Efficiency Studies to optimize the classification efficiency of known-known classes, including sparsely represented classes, across multiple domains and task types; and (2) Systematized Serendipity Studies to increase the efficiency of detecting diverse, scientifically interesting anomalies beyond the usual statistically inferred ones. Both objectives will be carried out through experiments implemented on Correct-a-Machine infrastructure which will enable machine proposals as well as machine attention and anomaly maps to be displayed to volunteers. A Leveling-up Strategy for Volunteers infrastructure will also be deployed to enable a human-driven approach to anomaly detection. The research team will carry out experiments that include simply testing improved classification efficiency with humans correcting implicit machine attention (i.e., are models such as vision transformers just significantly better at object detection and therefore the efficiency gains when coupled in a human-machine system are worth the complexity of implementation and training) through to more complex experiments that explicitly combine machine and human attention (that is, does asking volunteers to correct an explicit machine attention map improve performance of the machine or further, can machine attention and anomaly maps aid in human identification of anomalies in the data?). The test projects implemented through this work will be immediately scientifically useful, but importantly will also underpin the human-machine infrastructure required to enable multiple research disciplines to make best use of the ever-increasing amounts of 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 · 2024-10

This three-year renewal RET Site: Collaborative Research: Research Experiences for Teachers across the National Nanotechnology Coordinated Infrastructure is hosted by the Georgia Institute of Technology, the University of Minnesota, and the University of Nebraska, Lincoln. Nanoscale science and engineering is interdisciplinary and cuts across all science and engineering disciplines. As part of the National Nanotechnology Coordinated Infrastructure (NNCI) this program supports 12 in-service, high school and community college faculty each year. Participants will engage in high-quality, nanoscale science and engineering (NSE) hands-on research in state-of-the-art nanotechnology facilities at NNCI sites for 6 weeks during the summer. Educators will complete a hands-on research project in NSE during the summer with continuing support during the academic year. This RET program spanning three NNCI sites allows participants access to a wider variety of NSE research than would be available at a single-site and exposes participants to the NSE needs of industry and related career opportunities across the nation. Project activities will strengthen participants’ knowledge and understanding about broad educational, industrial, and societal NSE activities and how to motivate their students to explore STEM and NSE fields that may lead and provide them with satisfying and lifelong STEM careers. This three-year renewal RET Site: Collaborative Research: Research Experiences for Teachers across the National Nanotechnology Coordinated Infrastructure (NNCI) is hosted by the Georgia Institute of Technology, the University of Minnesota, and the University of Nebraska, Lincoln. Nanoscale science and engineering is interdisciplinary and cuts across all science and engineering disciplines. The program offers a wide array of topics such as flexible electronics, nanomotors, batteries, environmental filtration, and medical diagnosis of diseases. With support from faculty, mentors, and RET coordinators, the RETs will develop curriculum materials to bring their NSE research back to their classrooms. During the academic year, faculty and mentors will visit the RET classrooms to assist with the implementation and further development of the curriculum modules. This RET program spanning three NNCI sites allows participants access to a wider variety of NSE research than would be available at a single-site and exposes participants to the NSE needs of industry and related career opportunities across the nation. The objectives are to grow a multi-site cohort of educators with research experiences that reflect broad educational, industrial, and societal NSE activities; build and disseminate a library of NSE educational materials; highlight the work of NNCI cohort by attending each sites state science teaching association annual meeting; and encourage RETs to present at professional society meetings. Webinars will be held across all participating NNCI sites to enable teachers to learn about NSE industries and careers as well as discuss their modules. The RET program promotes networking opportunities through participation in on-line presentations and webinars, a Slack group, the yearly state science teacher conferences, professional society conferences, and an in-person convocation. This project is partially supported by the Division of Electrical, Communications, and Cyber Systems, the Established Program to Stimulate Competitive Research (EPSCOR), and the Division of Engineering Education and Research Centers. 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 · 2024-10

The objective of this project is to support research on multimodal transit services, combining fixed-route public transit with shared mobility services, while accounting for systematic uncertainties, rider preferences, and inherent complexity of different transportation modes. Car-less households face challenges accessing jobs and services mainly due to difficulty traveling between transit hubs and origins/destinations. At the same time, uncertainties in travel time, demand, and rider choices significantly impact design and operations of transit services. The research team investigates service planning and network design for fixed-route transit, as well as fleet sizing, routing, and relocation for shared mobility. Successful implementation is expected to (i) advance theories and computations in transportation and network problems under uncertainties, and (ii) enhance the potential of multimodal transit services to reduce private vehicle ownership, lower greenhouse gas emissions, and alleviate urban traffic congestion, while providing affordable transportation services for underserved groups. The team also contributes to curriculum development at the University of Minnesota and the University of Iowa, promotes diversity in STEM fields, enhances undergraduate research, and engages in K-12 outreach activities. The research focuses on developing a hierarchical, data-driven optimization framework that incorporates user behaviors for planning and operating multimodal transit systems under systematic uncertainties. Demand response to multimodal transit services is characterized through a hierarchical process to accommodate diverse user adoption preferences. Corresponding decision-making is modeled as sequential resource planning and allocation processes. The models and methodologies are based on stochastic optimization with single- and multi-stage dynamics. The primary outcomes include (1) an integrated hierarchical optimization framework to capture user behaviors; (2) data-driven methods to learn use preferences in transportation systems; (3) distribution-free approaches to accommodate unknown uncertainties in network design; and (4) efficient computational methods to enable practical application. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Estimating Power, Performance, and Area (PPA) earlier in the electronic design automation (EDA) flow would improve the Quality of Results (QoR) and reliability in chip design. The classical analytical or heuristic methods can be challenging to fine-tune, especially for complex problems. Machine learning (ML) methods have proven to be effective in addressing these problems. Graph Neural Networks (GNNs) have gained popularity since they are among the most natural ways to represent the fundamental objects in the EDA flow. However, with increased design complexity and chip capacity, an increasing performance gap exists between the extremely large graphs in EDA and the insufficient support from general-purpose hardware, such as mainstream graphics processing units (GPUs). This project aims to expedite the large graph machine learning on various EDA tasks, through a full-fledged development of efficient and scalable computing paradigms. This project's novelties are EDA domain knowledge-aware graph machine learning, training acceleration, and algorithm-hardware co-design and optimization. The project's broader significance and importance include: (1) to advance the field of machine learning in chip design, highlighted in National Artificial Intelligence Initiative; (2) to deepen the understanding of interactions among EDA domain knowledge, graph learning, and GPU acceleration; (3) to enrich the computer engineering curriculum and promote participation from undergraduates, underrepresented groups, and K-12 students in STEM fields through relevant programs. The project will develop a design paradigm for efficient, scalable and practical algorithm-hardware co-optimized solutions to significantly accelerate large graph machine learning on EDA tasks using a single GPU. This project consists of three coherent research thrusts: (1) to develop an algorithm-hardware co-optimized paradigm, focusing on restudying EDA graph features, introducing partitioning and selective re-growth methods, and tailoring GPU kernels for unified graph machine learning on EDA tasks using a single GPU; (2) to speed up single GPU for large circuit Graph Neural Network (GNN) training by implementing a tiled reversible architecture for low-memory training, and designing a maxK nonlinearity function to reduce computation costs; (3) to jointly integrate EDA domain knowledge, graph learning, and hardware optimizations to co-search for the appropriate hardware primitives and GNN compression strategies, as well as closely leverage the unique properties of circuit graphs. 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 · 2024-10

Energy use for computing is ever-increasing, particularly with the growth of artificial intelligence (AI) and machine learning. Modern AI requires large datacenters that house thousands of specialized computers that are used to train AI models and generate responses to queries. These datacenters require huge amounts of electricity, and they generate so much heat that extensive cooling systems are needed to prevent the computers from overheating. Networks of datacenters already exist to support the cloud-based computing that the world has come to rely on for everything from communication to banking to transportation infrastructure. However, the growth of AI will dramatically increase the need for such datacenters. According to a 2024 report from the International Energy Agency, an AI-enabled ChatGPT query uses almost ten times more energy than a standard Google search. Furthermore, the report predicts exponential growth in the AI industry, increasing AI’s electricity demand by at least 10x from 2023 to 2026. This research project will investigate ways to fundamentally redesign the computing systems that support AI, from the semiconductor materials and devices to the computing circuits and architectures. The project aims to improve the energy efficiency of these systems by orders of magnitude. Such dramatic reductions in the energy demand for AI would positively impact society by reducing the strain on the electricity grid and mitigating the impacts of AI on climate change. Furthermore, this project supports an Education and Workforce Development plan that is a collaboration with community colleges and the Micro Nano Technology Education Center, an NSF Advanced Technology Education Center dedicated to increasing the semiconductor workforce. In this project, a team of investigators will pursue interdisciplinary research encompassing materials, devices, circuits, and architecture co-designs to improve the energy efficiency of AI and machine learning computing hardware. Most of the computing resources for machine learning algorithms are used by multiply-and-accumulate (MAC). The regularity and parallelism of MACs make them very suitable for hardware acceleration. However, conventional random-access memory requires row-by-row accesses, and fetching billions of weights in this manner consumes substantial energy. One solution to this bottleneck is eliminating the row-by-row memory access by designing hardware systems where the computing occurs inside the memory, referred to as “in-memory computing”. In-memory computing is achieved by inserting a small computing circuit in each memory cell. A promising new memory element, the “memcapacitor,” has been proposed for this purpose, but few devices have been experimentally demonstrated. The technical aims of this project can be broken into three major areas: 1) Create a vertical memcapacitor device that can be fabricated in the backend of the CMOS process for monolithic 3D integration; 2) Create memcapacitor-based in-memory computing circuits; and 3) Create a deep neural network accelerator with fully analog-datapath and digital-control. 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 · 2024-10

The US has incurred billions of dollars in damage from extreme precipitation events linked to anthropogenic climate change since the 1980s. Increased erosion and sediment yield from these events is likely to damage soils, clog rivers, and cripple hydraulic infrastructure. However, we have little information on the magnitude of the response of our rivers and landscapes to global climate change because these changes occur on timescales difficult to measure in our lifetimes. Therefore, we must look to times in Earth’s past when temperatures and atmospheric CO¬2 concentrations rose rapidly to study landscape response. During the early Eocene, approximately 56 to 52 million years ago, there were repeated intervals known as hyperthermal events where global temperatures rapidly increased due to releases of CO2 over a period of ~20,000 years. These hyperthermals provide one of the best analogs to modern anthropogenic climate change, albeit at a slower rate than today. This project will focus on improving scientific and public understanding of how future climate change will affect our river systems by using analogs from the early Eocene in New Mexico, Wyoming, and North Dakota. The education plan will target a diverse population of students from the University of Houston that will strengthen undergraduate exposure to field geology using virtual field trips. Because climate change can be an abstract and intimidating concept for some groups, collaborations with a world-renowned climate-artist will be used to break down mental barriers and communicate science to the public and low-income and minority students from the Houston area. This project will generate new terrestrial paleoclimate records from three fluvially dominated basins in the western US: 1) San Juan Basin of New Mexico, 2) Wind River Basin of Wyoming, and 3) Williston Basin of North Dakota. It will use a novel method that integrates datasets from both sandstone channel facies and floodplain paleosols to test the hypothesized connection between hyperthermal-driven hydrologic cycle intensification and increased weathering that formed large sand bodies and thick packages of kaolinite. This project will use a multi-proxy approach that includes geochemistry, mineralogy, stable isotopes (δ13C, δ18O, and Δ47), sedimentology, stratigraphy, radiogenic isotope geochronology (40Ar/39Ar, U-Th-Pb), and magnetostratigraphy to reconstruct the paleoclimate and constrain landscape response to the hyperthermal events both spatially and temporally. The resulting dataset will be integrated into quantitative models to test how rapid atmospheric CO2 increases, global warming, and the resulting hydrologic cycle intensification will increase the magnitude of weathering and sediment yield, which has the potential to cause billions of dollars in damage to infrastructure and ecosystems from soil loss, erosion, and increased flooding. 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 · 2024-10

The project addresses directly the heart of algebraic topology: computing invariants like numbers, groups, and rings to understand spaces. The goal of algebraic topology is to systematically build a connection between algebraic objects like numbers and geometric objects like spaces. This connection allows a two-way flow of information, with algebraic invariants distinguishing spaces and topological methods informing algebraic problems. Starting from foundational work of Quillen, algebraic and algebraic geometry data like formal groups gives rise to new invariants for spaces with striking properties. This project combines this classical thread with much more recent developments coming from equivariant algebraic topology. "Equivariant algebraic topology" remembers a collection of symmetries inherent in a space as part of the data, systematically grouping spaces with the same symmetries, and the numbers and invariants produced must reflect this. This extra structure provides more nuanced computations, giving more information about how the classically described invariants change under symmetries. Equivariant algebraic topology has experienced a renaissance recently due to the solution by the PI, Hopkins, and Ravenel to the Kervaire Invariant One problem, one of the oldest outstanding problems in algebraic topology. The solution introduced a host of new constructions and techniques that have striking ramifications in classical and equivariant algebraic topology, and this project focuses on unpacking some of these new constructions, exploring their ramifications in classically studied computations, and describing what they mean for algebraic topology in general. Many of the projects focus on diversity in STEM. Building on the PI's prior First Year seminar on Women in Math, the PI will create a diversity-driven class, combining mathematical content and pedagogy with discussions of representation and inclusion in mathematics. At the same time, the PI intends to create more opportunities for students who do not see themselves as "math people" to connect with algebra and geometry concepts using UCLA's "Maker Spaces" to have students design and build concrete models. The PI will continue conference organizing, especially conferences focusing on making space for early career mathematicians and for advanced undergraduates, using these as a way to connect students with the ideas and researchers in stable homotopy. Using newly developed tools in equivariant stable homotopy, the PI will study the slice spectral sequences for certain chromatically meaningful quotients of hyperreal spectra. These are closely connected to the classical approaches to studying K(n)-local phenomena using the Hopkins--Miller higher real K-theory spectra, and at the prime 2, computations here subsume all previously known higher real K-theory computations. The project focuses mainly on concrete computations (both of chromatically meaningful quotients of hyperreal bordism and of more traditional objects like the dual Steenrod algebra), while also studying more abstract questions of what kinds of multiplicative structures we can see. Finally, an application of all of this machinery to the classical questions of orientability of vector bundles is explored. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This research project will advance the frontiers of modern statistical theory and methodology in cognitive diagnosis modeling. Cognitive diagnosis models (CDMs) are psychometric tools designed to infer respondents' unobserved psychological attributes from their manifest responses to a set of items in a test or questionnaire. CDMs have been used in educational assessments and successfully applied in psychology and the social sciences. However, existing CDMs have limited utility because they often assume binary attributes. This project will further extend the applicability of CDMs by developing a general family of models that offer a unified framework for CDM analyses, and that also can be used as a basis for the development of new CDMs. The scientific products of this project will be disseminated via workshops, conference presentations, and publications in peer-reviewed journals. The project will develop open-source software to make advanced CDMs accessible to a broader audience. The outcomes of this project will be useful for applied researchers in education, psychology, and the social sciences. Both undergraduate and graduate students will be involved in the conduct of this research, and the investigators will make every effort to include students of underrepresented groups in their research teams. This research project will develop, estimate, and apply a novel family of cognitive diagnosis models (CDMs) to simultaneously accommodate polytomous response data and multi-categorical psychological attributes. In particular, the project will (1) examine the theoretical properties of the proposed models, including model identifiability and model equivalence to ensure the principled use of CDMs in practice, (2) develop computationally efficient parameter estimation methods to make it possible to estimate parameters of CDMs of high dimensions in big data, (3) develop valid statistical inference methods for handling models of high dimensions and data of large sizes, and (4) conduct interdisciplinary collaborations to apply the new methods to representative datasets in various scientific fields to address substantive research questions of interest. To boost the impact of the proposed work, the investigators will create a free software program to make the methodological innovations accessible to applied researchers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The Magnetics Information Consortium (MagIC) provides a web-based research facility that allows researchers and other users free access to archive, search, visualize, manipulate, download, and reuse data that address many grand challenges in the geosciences. Over the past six years, the number of contributors uploading data more than tripled and MagIC usage and dataset downloads increased more than sixfold. These large increases in contributors, contributions, and data usage are possible because of a community-driven unified MagIC data model and because the MagIC Facility deploys highly scalable databases and a highly adaptable web interface. Although there is still a noticeable sparsity of data on the internet that can be characterized as being easily findable, accessible, interoperable, and reusable—these FAIR data principles lie at the heart of MagIC. In addition, training of the next generation of researchers and members of underrepresented groups in the development and use of modern data analysis methods form another central component in the MagIC Facility. In the next three years, the MagIC Facility will focus its efforts on making enhancements to ensure efficient access to data in a wide range of subdomain science disciplines, transparency in data processing, and reliable documentation—altogether to allow questions of reproducibility and standardization to be readily addressed by researcher and student users. The MagIC Facility will develop several novel geoinformatics products including an Integrated Contribution Development Environment using template notebooks on https://jupyterhub.earthref.org with guided steps for programmatically editing, visualizing, reproducing data analysis results, and updating user contributions without leaving the browser; two specialized Subdomain Views with featured filters, relevant data summaries, and subdomain-specific calculations and visualizations; a Dataset Enrichment tool to further reduce efforts to prepare an online data contribution for publication; and Superuser Private Workspaces to support large facility and laboratory data management needs within MagIC. In particular, the new team from Oregon State University, the Institute for Rock Magnetism (IRM) at the University of Minnesota, University of California, Berkeley, and the Scripps Institution of Oceanography at University of California, San Diego, will work collaboratively to integrate PmagPy (an open-source and cross-platform software package for curating and analyzing paleomagnetic data) and preexisting IRM rock magnetic computational tools into the MagIC back-end server to allow for advanced online calculations, interactive visualizations, and integration on JupyterHub and EarthCube’s Pangeo; to make MagIC interoperable with other online data repositories that also follow FAIR principles; and to keep strengthening MagIC as a community facility by engaging researchers from historically underrepresented groups and varied institution types. Over the next three years, the MagIC Facility will be transferred to FIESTA (Framework for Integrated Earth Science and Technology Applications) technology that is being finalized under funding from EarthCube. 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 information explosion in many areas of society, from medicine to economics and business to social media, has resulted in pressing questions for modern data science regarding subject-level knowledge, such as in precision medicine, focused marketing, family economics, and many other areas. These include effective methods for data analysis and prediction in important areas of application ranging from privacy protection via differential privacy (DP) to precision medicine and public health disparities focusing on the prediction of epigenetic markers, and to predictions with employment data from the U.S. Bureau of Labor Statistics (BLS). This project aims to develop and employ new methods known as mixed model prediction. Particularly, for the DP application, the investigators will apply the methods to the publicly released 2020 U.S. decennial census; for the BLS application the investigators will target questions regarding volatility during the ongoing COVID-19 pandemic that thus require robust modifications from traditional approaches. The research will be carried out in conjunction with collaborators who are immersed in a particular application area. In this project, the investigators will focus on three major aims: 1) multivariate mixed model prediction (MMP) in genomic prediction problems where correlated DNA methylation markers reflect underlying disease biology and improved prediction accuracy is possible by borrowing strength across this multivariate structure; 2) MMP for differentially private (DP) data in which cluster or grouping identities are contaminated by design and not released to protect privacy; and 3) MMP with non-Gaussian random effects and errors, which greatly can expand the range of circumstances in which MMP can be applied beyond the classical normality assumptions that do not fit many modern datasets. The investigators will develop the required methodology for each aim, study the procedures theoretically, and carry out extensive empirical simulation studies to compare the new methods with other methods. Furthermore, the investigators will work closely with their collaborators in the subject fields on implementing the methods developed in this project to answering practical questions. 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

Massive stars die spectacularly as Supernovae (SN). The investigators seek to understand the processes in massive stars during their final years before exploding. The investigators will use two sets of optical telescopes in New Mexico and Crete, Greece known as the Total-Coverage Ultra-Fast Response to Binary-Mergers Observatory (TURBO) to image a small set of nearby galaxies. By taking images every several minutes, they will be able to detect the first brief pulse of light emerging from supernovae in those galaxies. These observations will probe the exploding stars’ internal structure and recent mass loss. A second objective of the program is to study the mergers of pairs of neutron stars. Such mergers emit ripples in spacetime known as gravitational waves. Following an alert from a gravitational-wave observatory, TURBO will begin searching for light emitted by the merger. TURBO’s uniquely fast response of two seconds will allow it to find rapidly fading sources. Successful detections will provide insight into whether such mergers are primarily responsible for Earth’s heavy elements. The program will involve students from UMN-Morris, which serves a significant Native American student body, as well as New Mexico Tech, an Hispanic-serving institution. The project will develop the ability to stream TURBO imaging to classrooms and will use Zooniverse to involve the public. Observations of the initial pulse, or breakout of the internal shock at the surface of the SN progenitor, (4–6 each year) can be expected to connect the properties of progenitors to those of the SNe, as well as provide new insight into the structure of massive stars just before the explosion. Furthermore, the investigators expect to identify the shock breakout of the massive progenitor of a “failed SN” in a nearby galaxy. A global and growing network of gravitational-wave detectors has now observed a total of more than one-hundred mergers of compact objects. TURBO will be used to rapidly image hundreds of square degrees for counter parts of gravitational-wave events. TURBO has a unique ability to leverage LIGO’s new early warning alerts and observe mergers as they happen across large areas on the sky. It will provide targets to global follow-up facilities. With an optical counterpart, mergers will enable a measurement of the Hubble constant, addressing the current tension, in addition to the r-process yields of kilonovae. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

NONTECHNICAL SUMMARY This award supports theoretical research and education to examine superconductivity in the strong coupling regime. Superconductivity, the ability of electrons to conduct electric current without dissipation below a certain temperature called Tc, is not only one of the most remarkable phenomena emerging from the quantum physics of many interacting electrons but is also of great technological importance. Metrology, lossless energy transmission, and quantum computation are important examples. Earlier theoretical studies of superconductivity were based on the Bardeen-Cooper-Schrieffer (BCS) model, which assumed that electrons are weakly interacting. This approach was questioned after the discovery of high-temperature superconductivity first in cuprate oxides and then in other series of compounds, including, most recently, graphene-based systems. Experiments revealed that these materials display behavior, which differs fundamentally from that expected for ordinary superconductors both above Tc and below Tc. These discoveries called for a qualitatively new theory of superconductivity in the regime of strong interactions between electrons. In this project, the PI and his students will work to understand the mechanism of superconductivity in these unusual materials and the competition between superconductivity and other ordered states of interacting electrons, such as magnetism. This is a problem with no easy answer because strong interaction makes it more difficult to establish a dissipation-less current flow, required for superconductivity. At the same time, the stronger the interaction the higher Tc is expected. Understanding the dual role of strong interactions also the interplay between superconductivity and other possible ordered states is the main goal of the proposed work. Theoretical advances resulting from this project may guide the identification of materials with higher superconducting transition temperatures and desirable properties, which should have a revolutionary impact on society. This project will contribute to the development of the scientific workforce by training two graduate students. The PI will continue running conferences and lecturing at schools for graduate students and junior faculty. TECHNICAL SUMMARY This award supports theoretical research and education which will examine superconductivity in the strong coupling regime. This is a central problem in experimental and theoretical condensed-matter physics. In addition to incipient practical applications, the interest in this field is driven by the fascinating variety of observed effects and universality of underlying theoretical ideas. The PI will focus on several fundamental issues related to a system behavior near a quantum-critical point (QCP), where interaction, mediated by a soft critical boson, provides a mechanism for pairing and at the same time gives rise to non-Fermi liquid normal state behavior. The issues the PI will address include understanding: the competition between fermionic incoherence and Cooper pairing, a fundamentally non-BCS pairing mechanism, how dynamical vortices emerge, a topological transition that occurs when the number of vortices becomes infinite, the bound pair state without phase coherence, and the collective modes of a quantum-critical superconductor. The PI will also analyze theoretically superconductivity emerging from a pseudogap state, with special emphasis on the behavior of superfluid stiffness and on the range where bound pairs of fermions form, but remain incoherent and do not give rise to supercurrent. In related studies, the PI will analyze superconductivity in a two-dimensional metal near a van Hove singularity and near the end point of s-wave superconductivity in systems with electron-phonon attraction and Hubbard repulsion. The PI will contribute to the development of the scientific workforce by training two graduate students in modern theoretical condensed matter physics and will continue running conferences and workshops and lecturing at schools for graduate students and junior faculty. 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

Scientists and engineers play an important role in addressing critical challenges faced by society in energy security, environmental sustainability, and human health. This National Science Foundation Research Traineeship (NRT) award to the University of Minnesota will address these grand challenges by training graduate students to work across common disciplinary boundaries through the integration of traditional domain science with data-driven methods. The project anticipates training 150 M.S. and Ph.D. students including 25 funded trainees across disciplines including chemical engineering, biological engineering, chemistry, and materials science. Trainees will leverage their knowledge in these core science and engineering disciplines and build upon this foundation with tailored education in scientific computing, machine learning, systems-level analysis, and personal and professional development. Upon completion of this program, students will be uniquely prepared to solve complex, interdisciplinary problems that leverage their expertise in traditional and data-driven science and engineering. Data science is beginning to shape the design of materials, chemicals, and pharmaceuticals, but the heterogeneous nature and scarcity of data relevant to these disciplines presents a major challenge. The cost of data acquisition necessitates the integration of computational research to predict outcomes and inform experimental design. Research in this NRT will combine atomistic simulations, machine learning, and experimental methods to build models that integrate multiple data sources and scales. A defining feature of the proposed research will be the incorporation of systems engineering across these modalities to address process-level considerations related to the design of emerging chemical, material, and biological platforms. Research will address three core themes including the development of foundational tools for multiscale modeling and integrated materials and process engineering, the discovery and design of materials and processes for sustainable energy conversion and storage, and the development and optimization of new vehicles for drug delivery. Beyond research, this NRT will enhance educational infrastructure through a convergent graduate curriculum that provides students from diverse backgrounds with core skills in scientific computing and an integrated education on the fundamentals of data science and its application to problems in science and engineering. Close collaboration with industrial partners will provide trainees with unique professional development opportunities and inform the research questions and educational content addressed in this program. The overarching goal of this project is to establish a foundation for training scientists and engineers as disciplinary experts who can work seamlessly with digital technology to address grand societal challenges. 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

The global environment is changing in many ways, and organisms often face multiple stresses at the same time. It is hard to predict how these combined stresses will affect a population, because the impact of one stress can change when another is also present. For example, high heat can be more harmful when organisms also lack water. The problem of predicting the impact of combined stresses is even more complicated in mutualistic relationships, where species depend on each other, and stress on one species can indirectly affect the other. However, it is crucial to understand how combined stresses impact mutualisms, because mutualisms are common and essential for everything from crop production to global nutrient cycling. The project will specifically test how mutualistic interactions between bacteria alter their ability to survive and evolve resistance to antibiotic combinations. The project will improve the ability to predict and manage the effects of multiple stresses in microbial systems and mutualisms more broadly. The central hypothesis of the project is that mutualism will change the ecological and evolutionary impact of stress combinations, but that these impacts can be predicted. The researchers will test this hypothesis through integrating computational and experimental approaches based on a model obligate mutualism between strains of Escherichia coli and Salmonella enterica that rely on each other for essential metabolites. The researchers will carry out over 3,900 growth experiments to test the impact of drug combinations on the growth of bacteria in monoculture and mutualistic co-culture. The researchers will also do over 150 evolution experiments to test how species interactions alter the rate and mechanisms by which resistance to multiple stresses evolves in monoculture and mutualism. These data will be integrated with genome-scale metabolic models, general steppingstone models, and clustering algorithms to develop tools to quantitatively predict the ecological and evolutionary impact of combined stresses in mutualisms. 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

Life as we know it relies on a common set of foundational elements, including four standard bases in the genetic alphabet of DNA (G, A, T, and C), and a highly conserved genetic code for the translation of proteins. Synthetic biology seeks to determine the uniqueness and fungibility of these constraints, and one promise of synthetic cell engineering is to transcend the evolutionary constraints that have been handed down to us and instead create life-like systems with expanded chemistries. The Ellington and Adamala labs seek to leverage the flexibility, evolvability, adaptability, and safety of purely in vitro (test tube-based) systems, to engineer synthetic cells with expanded genetic alphabets and genetic codes. In particular, they aim to employ non-canonical nucleotides to broaden the scope of codons (triplet and ultimately new quadruplet codons) in the genetic code, ultimately leading to the incorporation of over 24 amino acids (that have distinctive and biotechnologically useful chemistries) into proteins. Additionally, the project will focus on the biosafety and biosecurity impacts of expanded genetic alphabets. The development of non-canonical genetic alphabets and codes is rapidly advancing, with various groups exploring novel genetic alphabets that are becoming more accessible both in vitro and in vivo. Successful generation of an 8-letter code, and enzymatic incorporation in vitro, demonstrated utility of this technology for engineering novel genetic systems. However, adapting non-canonical genetic alphabets to non-canonical genetic codes presents challenges, mainly due to interdependencies within biological systems. Attempts to modify genetic alphabets and codes have faced systemic disruptions and fitness impacts in natural cells. In response, the focus is shifting towards synthetic cells, which offer greater control over systems biology, free from the evolutionary constraints of natural cells. This proposal aims to utilize synthetic cells for engineering efforts, particularly exploring the implementation of quadruplet codons for genetic code expansion, a task challenging to address in living cells. The synthetic cell approach allows for rationally designed, bottom-up experimentation and the concomitant resolution of complexities related to codon instantiation, contributing insights to both living and synthetic systems. In this work, the researchers will investigate the incorporation of non-canonical nucleotides into translation (Aim 1), followed by the incorporation of non-canonical amino acids via non-canonical genetic alphabets (Aim 2). Finally, they will use artificial evolution to optimize translation systems with non-canonical nucleotides and amino acids (Aim 3). Collectively, this project will explore biological diversity beyond that which currently exists in nature and is supported by the Systems and Synthetic Biology Cluster of the Division of Molecular and Cellular Biosciences. 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 award will contribute to the advancement of national health and welfare by developing a comprehensive modeling and solution framework for generating annual rotation schedules for family medicine residents, in alignment with the objectives of the Clinic First model. The Clinic First model has emerged in academic family medicine as an innovative educational approach that prioritizes ambulatory (out-of-hospital) training and continuity of care while incorporating physicians' specialized interests. The United States faces an acute shortage of primary care physicians (PCPs), a trend projected to worsen over the next decade, potentially jeopardizing the effectiveness of the healthcare system. The shortage is exacerbated by many family medicine residents opting for sub-specialization or hospitalist roles instead of becoming family medicine physicians. A major contributing factor to this trend is the prevailing emphasis within family medicine residency training programs on hospital-based experiences rather than outpatient primary care. This project is focused on creating specialized methods for designing annual rotation schedules that support the Clinic First model, with the goal of better preparing residents and improving retention rates in family medicine. These schedules are designed to enhance residents' clinic experiences, motivating them to pursue PCP careers and mitigating the PCP shortage nationwide. This research aims to develop innovative analytical methods to enhance residents' clinic experiences through a new decision-support framework. At the core of this framework is a novel Dantzig-Wolfe formulation designed to create annual rotation schedules that optimize two objectives consistent with the Clinic First model. Methodologically, the project seeks to advance the solution of large-scale bi-objective integer programs using column generation-based decomposition algorithms by introducing new branching and cutting plane strategies to improve computational efficiency. Additionally, new methods based on Branch-and-Price will be developed to efficiently identify a diverse set of such solutions for large-scale bi-objective integer programs. The research will also contribute new solution algorithms and insights into Parametric Integer Linear Programs. The benefits of the new schedules will be analyzed and compared with those created manually at a partnering family medicine residency program. 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 joint proposal between the University of Minnesota, USA, Indian Institute of Technology at Ropar, and Indian Institute of Technology at Delhi, India under US- Meity program involves an international multidisciplinary team that proposes to develop semimetals quantum materials-based magnetic tunnel junctions (MTJs) for energy-efficient Stochastic Computing Computational Random-Access Memories (SC-CRAM) for applications in stochastic and neuromorphic computing. Quantum materials with unconventional properties have recently captured the attention of the scientific community. These materials play a crucial role in the development of magnetic tunnel junctions (MTJs), specialized devices that exploit their unique properties. MTJs are building blocks for memory and computing on a nanometer scale that can switch between different magnetic states. One of the exciting aspects of this project is the development of a new kind of computing technology, known as Stochastic Computing Computational Random-Access Memory (SC-CRAM) using these MTJs. This new computing technology promises to be much more efficient than what is currently available. The team will combine theoretical research, material synthesis (making the materials), and advanced manufacturing techniques to bring this vision to life. Overall, the project aims to push the boundaries of current computing technology, making future computing faster, more efficient, and capable of handling more complex tasks with less energy consumption. The outcome of our proposal will directly impact industries engaged in the development of technology for unconventional computing. It will also benefit society by expanding present computation beyond the limits of classical computers. The joint team will reach out to a broad range of groups and cultivate a welcoming and vibrant work environment that promotes mutual respect, effectiveness, and professionalism. The joint team will continue mentoring high school and undergraduate students both in India and the US by offering summer projects and interactive public lectures. Online databases and short courses on quantum materials and unconventional computing will be built and offered. The data handling and energy consumption of computing systems are growing exponentially at unsustainable levels. To address these challenges, new concepts and technologies need to be developed urgently. Unconventional computing is a broad and interdisciplinary field, encompassing various approaches with the common goal of expanding computation beyond the limits of classical computing paradigms. This proposal aims to address this urgent societal need by using the unique properties of quantum materials. The primary objective is to develop quantum materials-based magnetic tunnel junctions (MTJs) to implement energy-efficient computing paradigms. The goal is to surpass the constraints of traditional computing systems by introducing Stochastic Computing Computational Random-Access Memory (SC-CRAM), which will be built upon the foundation of quantum materials-based MTJs. A systematic and interdisciplinary approach will be employed, combining theoretical calculations, material synthesis techniques, and advanced fabrication methods. The aim is to understand fundamental aspects of quantum materials and spintronics and translate this knowledge into technological 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 · 2024-09

Urban trees and metropolitan forests are assumed to be a valuable tool to mitigate the increasing frequency and intensity of heat waves and flood events, worsening urban water and air quality, and concerns for the effects of greenhouse gas emissions. Despite these benefits, urban trees also produce hazardous environmental disservices, such as adverse nutrient export to urban stormwater, which are often distributed unevenly and inequitably across cities and metropolitan areas. Regional regulatory agencies, community-based organizations, and allied communities of practice have a powerful influence over planning and management decisions that affect the structure, function, composition, and resilience of urban forest systems - but the earth system science around urban trees and urban forests have not traditionally incorporated community partners, especially in the evaluation of complex synergies and tradeoffs across various scenarios. This project assesses (i) the convergent conditions of synergies and tradeoffs between tree benefits and burdens in metropolitan regions, (ii) tree species-level vulnerability to climate and urban stressors, (iii) the spatial scope of regional urban forests and relevant spheres of influence; (iv) the extent to which related research is generalizable and which is place-based. This planning proposal connects disparate areas of academic scholarship and communities of practice through a three-part approach intended to: synthesize convergent earth system sciences; reinforce existing networks of community partnership; and build new networks of community partnership and collaboration. These activities lay the groundwork for identifying areas of mutual interest and capacity for researchers and practitioners. Traditional and non-traditional partners are engaged in the overall research plan, including academic researchers across disciplines, public and private practitioners from urban forestry, regional planning, and stormwater management. The project includes plans to foster communities of practice that integral to addressing compounded hazards in cities, including technology and innovation hubs and workforce development organizations. A series of facilitated workshops, meetings, and proposal-drafting sessions, are convened in order for the project team to co-develop and identify the questions, materials and resources needed to develop locally scoped and community partner-centered solutions alongside locally-relevant earth system 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

The project will provide state-of-the-art computational tools for the development of novel 2D materials and their potential application to ultra-fast electronic, opto-electronic, and magnetic devices; unconventional optical and photonic devices; communication devices; and quantum computing applications. The project will address interconnected challenges in emerging areas of quantum science, computational mathematics and computer science by effectively merging highly domain-specific techniques with general machine learning techniques, thus informing and motivating analogous research on model order reduction across the sciences and engineering. 2D materials research is an ideal platform to motivate new mathematics training and curricula in the analysis, modeling, and computation of electronic structure, mechanical and topological properties of materials, and analysis of experimental data. The project’s outreach to female and underrepresented student populations will broaden the diversity of the mathematical research community, and the project provides research training opportunities for graduate students. Many quantum phenomena of scientific and technological interest emerge naturally at the moiré length scales of layered 2D materials which makes those materials an exciting platform to explore quantum materials properties and to prototype quantum devices. For example, correlated electronic phases such as superconductivity have been recently observed in twisted bilayer graphene (tBLG). Such pioneering results have opened up a new era in the investigation and exploitation of quantum phenomena. Despite the continuing increase in computational resources, high-fidelity modeling and simulation of many quantum materials systems remains out of reach. The limitation is particularly serious in 2D heterostructures due to the large scales at which the quantum phenomena of interest emerge. The objective of this NSF-NSERC Alliance project is to develop an advanced computational modeling workflow, merging state-of-the-art quantum modeling and machine-learning methods to enable rapid, automated, high-fidelity exploration of mechanical and electronic properties of 2D quantum materials. This award is jointly supported by the Division of Mathematical Sciences, the Division of Materials Research and the Office of Advanced Cyberinfrastructure. 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 supports a five-day international conference at the Cochin University of Science and Technology (CUSAT) in Kochi, India, from December 27 to December 31, 2024. The conference serves as the official annual meeting of the International Indian Statistical Association (IISA). The conference provides its members a unique opportunity to meet and exchange ideas among researchers, and students with a broad focus on theoretical, methodological, and applied research across various scientific domains. Attendees can expect captivating plenary sessions, special invited talks, panel discussions, and a diverse array of invited and contributed sessions. IISA and CUSAT are the primary organizers of the conference. The conference's main objective is to bring together well-established and emerging young researchers from around the world who are actively pursuing theoretical and methodological research in statistics, data science, and their applications in various allied fields. It aims to provide a forum for leading experts and young researchers to discuss recent progress in statistical theory and data science. The conference offers a vibrant agenda, including student paper competitions, insightful presentations, and awards, complemented by enriching workshops and the esteemed Early Career Award in Statistics and Data Science (ECASDS). It strives to maintain a healthy presence of women and minorities in all these categories and of young researchers (within five years of their doctoral degrees) in invited sessions. The meeting is primarily self-funded, with revenue primarily coming from registration. The revenue generated from the registration fee will be mainly used to cover the cost of the conference. The requested budget will cover registration, partial airfare, and lodging for 20 students and 8 junior researchers to support the participation of students studying at US institutions and US-based junior researchers in the 2024 IISA conference. The official website https://www.intindstat.org/conference2024/index provides details on different activities planned during the conference. 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 is a project to develop connections between number theory and physics. A modern paradigm in number theory uses highly symmetric functions to answer the most fundamental questions about solutions of equations in several variables. Quite surprisingly, these same symmetries arise in physics, particularly statistical mechanics, where one seeks to determine global behavior of molecules based on local interactions between particles. The PI, collaborators, and students, will explain and explore further mathematical consequences of this connection. The project will provide research training opportunities for both undergraduate and graduate students. More precisely, the bridge between number theory and statistical mechanics alluded to above is the theory of quantum groups and most of the specific projects pursued will use the representation theory of quantum group modules. To make connections with special functions in number theory, particularly matrix coefficients of algebraic groups over local fields, one needs new results on quantum group modules. The PI and collaborators will use quantum affine Lie superalgebra modules to produce lattice models with the required symmetry used in the study of matrix coefficients for metaplectic groups. In reverse, by expressing new classes of special functions from representation theory as partition functions of solvable lattice models, one obtains conjectural invariants of multi-parameter quantum groups. The primary scientific goals include deeper insight from quantum groups into various aspects of the Langlands program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Dr. Aleksandr Zhukhovitskiy of the University of North Carolina at Chapel Hill and Dr. Ian Tonks of the University of Minnesota-Twin Cities will develop catalytic methods to edit the molecular architectures of various plastics such as polyesters and polyurethanes. Architecture—e.g., the extent and type of branching—of a polymer underpins its thermomechanical properties and, consequently, applications. For instance, linear architecture of high-density polyethylene (HDPE) leads to stiff materials that could be used as milk jugs; meanwhile, highly branched linear low-density polyethylene (LLDPE) is more flexible and extensible, which supports applications like plastic bags. Accessing a spectrum of architectures for a given polymer remains a challenge. The proposed research will address this challenge by developing catalysts and new mechanisms that can rearrange the bond between atoms in the polymer skeleton, thereby turning branched chains into linear ones, and vice versa. This chemistry will allow scientists and engineers to design new types of plastics with variable and changeable properties, such as force-responsive materials that change properties upon stretching or compressing, or materials with improved degradation/recyclability properties. This project will provide interdisciplinary research training to students and help to prepare a skilled workforce for academia and industry. As a part of this work, polymer-focused educational programs will be developed that integrate concepts of sustainability and circularity. This proposal will develop branched-to-linear transformations of polymer backbones via catalyzed sigmatropic rearrangements. Transition metal- and organo-catalyzed [3,3]-sigmatropic rearrangements will be developed to isomerize a broad range of vinyl sidechain-containing polymer classes between branched and linear architectures. The specific ratio of the branched-to-linear conversion will be dictated by the percent conversion and the thermodynamics of a given system. These rearrangements will result in transformations of the thermal properties of polymers, namely lowering their glass transition temperatures and increasing their crystallinity. The stereospecific nature of concerted [3,3]-rearrangements will be utilized to enable tacticity transfer from starting polymers to rearranged polymers. Additionally, mechanical force will be utilized to alter the thermodynamic landscape of the rearrangement reaction coordinates, creating a thermodynamic bias toward linear isomers. Ultimately, this work will leverage a detailed understanding of catalyzed [3,3]-rearrangements of polymer backbones to enable broad architectural and property editing of soft materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The primary objective of threat detection is to identify incidents that may pose a risk to computer systems, networks, data, social activities, or living communities. Advanced artificial intelligence (AI) algorithms, including machine learning and statistical learning methods, can analyze large amounts of data to identify features indicative of threats. Despite the success and promise of AI algorithms, they present challenges and concerns that warrant careful consideration. Many AI algorithms operate as "black boxes" and lack transparency in their decision-making processes, which can be problematic in critical applications. Additionally, inadequate training data can lead to biases, lack of robustness, and overfitting, resulting in inaccurate predictions, especially with new or unseen data. This project aims to mathematically address some challenges of trustworthy threat detection using AI algorithms, focusing specifically on effective learning from a very small number of samples, known as few-shot learning. The PI will investigate various problems related to few-shot learning. One such problem is few-shot domain adaptation, which uses knowledge from a related domain to build models for a target domain with limited unlabeled data. This is particularly relevant to automated threat detection, where events of interest are rare and have few examples. Effective metrics for learning from minimal target data will be explored. Another focus is few-shot graph generation, which can advance the theoretical understanding of machine learning on graphs with very limited training data. This is crucial for building trustworthy systems, as many relevant threat detection scenarios involve social and transportation networks. Since the most threatening events are rare, a few-shot generation is necessary. A primary emphasis of this proposal is on the mathematical understanding of learning effectively with very limited information. 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.