Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1383&sort=-funder
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-funder", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1397&sort=-funder", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=-funder", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1382&sort=-funder" }, "data": [ { "type": "Grant", "id": "14839", "attributes": { "award_id": "2409868", "title": "On Iteratively Regularized Alternating Minimization under Nonlinear Dynamics Constraints with Applications to Epidemiology", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "COMPUTATIONAL MATHEMATICS" ], "program_reference_codes": [], "program_officials": [ { "id": 31483, "first_name": "Troy D.", "last_name": "Butler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 200000, "principal_investigator": { "id": 7209, "first_name": "Alexandra", "last_name": "Smirnova", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 300, "ror": "", "name": "Georgia State University Research Foundation, Inc.", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 10457, "first_name": "Xiaojing", "last_name": "Ye", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 300, "ror": "", "name": "Georgia State University Research Foundation, Inc.", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "How widely has the virus spread? This important and often overlooked question was brought to light by the recent COVID-19 outbreak. Several techniques have been used to account for silent spreaders along with varying testing and healthcare seeking habits as the main reasons for under-reporting of incidence cases. It has been observed that silent spreaders play a more significant role in disease progression than previously understood, highlighting the need for policymakers to incorporate these hidden figures into their strategic responses. Unlike other disease parameters, i.e., incubation and recovery rates, the case reporting rate and the time-dependent effective reproduction number are directly influenced by a large number of factors making it impossible to directly quantify these parameters in any meaningful way. This project will advance iteratively regularized numerical algorithms, which have emerged as a powerful tool for reliable estimation (from noise-contaminated data) of infectious disease parameters that are crucial for future projections, prevention, and control. Apart from epidemiology, the project will benefit all real-world applications involving massive amounts of observation data for multiple stages of the inversion process with a shared model parameter. In the course of their theoretical and numerical studies, the PIs will continue to create research opportunities for undergraduate and graduate students, including women and students from groups traditionally underrepresented in STEM disciplines. A number of project topics are particularly suitable for student research and will be used to train some of the next generation of computational mathematicians.<br/><br/>In the framework of this project, the PIs will develop new regularized alternating minimization algorithms for solving ill-posed parameter-estimation problems constrained by nonlinear dynamics. While significant computational challenges are shared by both deterministic trust-region and Bayesian methods (such as numerical solutions requiring solutions to possibly complex ODE or PDE systems at every step of the iterative process), the team will address these challenges by constructing a family of fast and stable iteratively regularized optimization algorithms, which carefully alternate between updating model parameters and state variables.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14840", "attributes": { "award_id": "2435622", "title": "Collaborative Research: Frameworks: Interoperable High-Performance Classical, Machine Learning and Quantum Free Energy Methods in AMBER", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Software Institutes" ], "program_reference_codes": [], "program_officials": [ { "id": 6428, "first_name": "Varun", "last_name": "Chandola", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-01", "end_date": null, "award_amount": 2264054, "principal_investigator": { "id": 25360, "first_name": "Kenneth", "last_name": "Merz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 737, "ror": "", "name": "Cleveland Clinic Foundation", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "With support from the Office of Advanced Infrastructure and the Division of Chemistry at NSF, Professor Merz and his group will work on molecular simulation cyberinfrastructure. Molecular simulations have become an invaluable tool for research and technology development in chemical, pharmaceutical, and materials sciences. With the availability of specialized hardware such as graphics processing units (GPUs), molecular dynamics simulations using classical or molecular mechanical force fields have reached the spatial and temporal scales needed to address important real-world problems in the chemical and biological sciences. Free energy simulations are a particularly important and challenging class of molecular simulations that are critical to gain a predictive understanding of chemical processes. For example, free energy methods can predict the barrier height and rates for chemical reactions, whether a reaction will occur, or how tightly a drug binds to a target. These predictions are extremely valuable for the design of new catalytic agents or drugs. However, the predictive capability of free energy simulations is sensitive to the underlying model that describes the inter-atomic potential energy and forces. Accurate free energy simulations of chemical processes require potential energy models that capture the essential physics and can respond to changes in the chemical environment, but conventional force field models are unsuitable for many processes involving bond breaking and formation as seen, for example, in catalyst design. Consequently, there is great need to extend the scope of free energy methods by enabling the use of a broader range of potential energy models that are more accurate as well as reactive and/or capable of quantum mechanical many-body polarization and charge transfer. The cyberinfrastructure created by this project allows for the routine application of free energy methods, using quantum mechanics, machine learning, reactive and classical potentials to a myriad of important problems that advance the state-of-the art in the biological and chemical sciences. The tools can be applied by a range of scientists to address fundamental problems of national interest, for example, in the design of drugs against zoonotic diseases (e.g., COVID-19), the design of materials with novel functions and in the design of improved batteries. Given the sophistication of the methods employed, education of a diverse pool of chemical, biological and computer scientists to advance this field is essential and is addressed in this project, thereby training the next generation of computational scientists that will form the backbone of the work force of the future.<br/> <br/>The project develops accurate and efficient free energy software within a powerful new multiscale modeling framework in the AMBER suite of programs for applications in chemistry, biology, and materials science. The multiscale framework enables the design and use of new classes of mixed-method force fields that involve interoperability between several existing and emerging reactive, machine learning and quantum many-body potentials. These potentials have enhanced accuracy, robustness, and predictive capability compared to classical molecular mechanical force fields and enable the study of chemical reactions and catalysis. The cyberinfrastructure supports innovative multi-layered hybrid potentials that can be customized to meet the needs of complex applications in biotechnology development, enzyme design and drug discovery. A robust endpoint \"book-ending\" approach that leverages the GPU-accelerated capability of the AMBER molecular dynamics engine is used to reach these goals. Specifically, the open-source high-performance software for free energy simulations is designed for multi-layered hybrid potentials using combinations of linear-scaling many-body quantum mechanical methods via the GPU-accelerated QUICK package, scalable reactive ReaxFF force fields via the PuReMD package, as well as the recently developed DeepMD-SE, ANAKIN-ME (ANI) and AP-Net families of machine learning potentials. The cyberinfrastructure is built upon the existing high-performance CUDA MD engine in AMBER and extends it to a broad range of GPU-accelerated architectures using industry-standard programming models. Scalability is ensured using innovative parallel algorithms. High impact is achieved by leveraging AMBER's broad user base to expand the scope and success of FE applications. In this way, the project leverages existing recognized capabilities and actively engages a diverse team of collaborators and the broader molecular simulations community. The cyberinfrastructure delivered by the project enables a wide range of new and enhanced applications for a broad community of users in academia, industry, and national laboratories. These applications include drug discovery, enzyme catalysis, and biomaterials design. The AMBER suite of programs has a long-standing extensive worldwide userbase, and is widely used on national production cyberinfrastructure. The enhancement of AMBER as an established, proven sustainable, and widely used package will ensure that the software has a broad impact well beyond the end of the project. The project will also train a diverse population of students and researchers in theory, programming, computational chemistry/biology, computer science, scientific writing, and communication.<br/><br/>This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemistry within the NSF Directorate for Mathematical and Physical Sciences.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14841", "attributes": { "award_id": "2409138", "title": "SCC-IRG Track 2: NeighborDrive: Community-driven Neighborhood Wellbeing Improvement through Active Vehicular Crowdsensing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "S&CC: Smart & Connected Commun" ], "program_reference_codes": [], "program_officials": [ { "id": 27022, "first_name": "Vishal", "last_name": "Sharma", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-01", "end_date": null, "award_amount": 1461134, "principal_investigator": { "id": 7189, "first_name": "Hae Young", "last_name": "Noh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 31516, "first_name": "Sarah L", "last_name": "Billington", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31517, "first_name": "Pei", "last_name": "Zhang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31518, "first_name": "Carlee", "last_name": "Joe-Wong", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31519, "first_name": "Jackelyn", "last_name": "Hwang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Cities across the US have experienced a significant increase in people experiencing homelessness, especially since the beginning of the COVID pandemic. Timely and early intervention that improves the well-being of those who are experiencing homelessness significantly improves their outcomes, reduces time spent in homelessness, and prevents persistent homelessness. However, because of the dynamic movements of unhoused persons (due to clearing of encampments, weather, safety, etc.) coupled with a reluctance to provide information to the authorities, it is difficult for existing programs to determine the magnitude and location of service needs and to ensure that well-intentioned programs do not inadvertently reduce overall wellbeing. The project will support research that will measure neighborhood conditions and factors that impact the wellbeing of homeless populations through cameras, noise, and environmental sensors mounted on cars driving throughout the city of San Jose. This data will help determine neighborhood conditions at a granular level and the localized need of the homeless population and to optimize the services they receive (e.g., meal delivery, trash and waste removal, and toilets) through our partners including the City of San Jose, Loaves & Fishes, and Feed My Lamb.<br/><br/>The project has four main technical research steps to achieve the goal of understanding neighborhood wellbeing and the local needs of the homeless population: (1) developing a community-driven vehicular and mobile crowdsensing system to measure neighborhood conditions, (2) designing clustered federated learning algorithms to reconstruct city-wide maps of neighborhood environments and service needs, (3) modeling the causal relationships between neighborhood environments and wellbeing across different communities, and (4) developing methods to optimize services to improve and reduce inequality in wellbeing. The research project involves three types of community partners: local food pantries, local residents, and the city government of San Jose. Through collaboration with these partners, the project will have immediate impact to provide localized actionable needs relating to food, trash, and toilets, and to improve the wellbeing of vulnerable populations in San Jose, CA. The methods and models developed in the project will be generally applicable to other cities and areas with diverse neighborhoods.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14842", "attributes": { "award_id": "2343881", "title": "The Aggregate Impact of Working from Home", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Economics" ], "program_reference_codes": [], "program_officials": [ { "id": 1865, "first_name": "Nancy", "last_name": "Lutz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-01", "end_date": null, "award_amount": 493362, "principal_investigator": { "id": 7351, "first_name": "Nicholas", "last_name": "Bloom", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2500, "ror": "", "name": "National Bureau of Economic Research Inc", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Work from home (WFH) has surged in America, rising from 5% of workdays in 2019, peaking at about 60% in May 2020 during the lockdown, to stabilize at about 27% by May 2023. This five-fold increase in working from home, including both full time remote and part time telework, has been possibly the largest change to US labor markets since World War II. This WFH surge has generated major economic and policy questions over the impact of this on many areas of the US economy. This project will investigate the impact of this WFH surge on the aggregate US economy and labor market, arising from the impacts on productivity (which could be positive or negative), and on labor force participation. These questions are important academically, for monetary and fiscal policymakers, for businesses and managers, and for investors planning for the impact of WFH on goods and labor markets. <br/><br/>This project has three major strands to advance research on this topic. First is the Survey of Working Arrangements and Attitudes (SWAA) which will collect detailed WFH information for around 8,000 working Americans a month aged 20 to 64 on current practices, intentions and impacts on lifestyle, productivity and living arrangements. This provides detailed monthly data on exactly the working patterns across regions, industries and occupations across the US. Second, the project will also develop an employee-employer dataset from a leading US payroll processing firm to examine where people live and work pre and post pandemic. Payroll data usually has accurate home and work location data, and by examining a panel of employees and individuals it is possible to examine impacts of WFH on locational choice and infer WFH patterns. Third, the team will examine the impact of working from home on aggregate US productivity and worker welfare using a general equilibrium model. This aim will provide results on individual workers’ relative productivity while working from home and then enable counterfactual exercises to see how economy-wide welfare and productivity would differ if, for example, we forced working from home back to the low levels from before the pandemic. This will be invaluable for considering some of the larger, long-run aggregate impacts of the roughly 5-fold increase in rates of working from home experienced post-pandemic.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14843", "attributes": { "award_id": "2334581", "title": "BRC-BIO: Development of two biochemical tools to study Potato virus Y infection in plants", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "NFE-New Faculty Enhancement" ], "program_reference_codes": [], "program_officials": [ { "id": 31520, "first_name": "David J.", "last_name": "Klinke", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-01", "end_date": null, "award_amount": 498983, "principal_investigator": { "id": 31521, "first_name": "Erin", "last_name": "Weber", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2501, "ror": "https://ror.org/00wkay776", "name": "Carthage College", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true }, "abstract": "The impact of pathogens, specifically viruses, on living systems, has never been more greatly appreciated than during the age of COVID-19. There is great public awareness of the importance of understanding how viruses infect animal hosts and spread throughout a community. However, host-pathogen interactions in plant systems are less well understood, despite the considerable impact of plants on our lives. This project focuses on Potato virus Y (PVY) that can cause an 80% loss in the yield of potatoes (Solanum tuberosum), the fourth-most important crop worldwide. To improve understanding of how this virus circumvents host immunity and spreads through the host, the PI proposes to develop easy-to-use biochemical tools to detect and modify this plant pathogen and to better understand the molecular drivers of viral infectivity. Detecting viral spread will enable identifying early outbreaks of this plant disease to mitigate the impact on the associated agricultural industry. Understanding the molecular drivers of viral infectivity will aid in developing resistant commercial cultivars. The project also provides a framework for introducing undergraduates to viral biology using model organisms and to classic biochemistry techniques in the context of experiential learning. <br/><br/>Building upon the PI’s prior work with plant models and investigating host-viral interaction, this project aims to develop and validate two biochemical tools to address two major hurdles to studying how PVY establishes infection in plant hosts: the inability to track the virus as it spreads through the host and the ability to identify the contributions of specific viral sequences to circumventing host immunity. The first tool, a synthetic PVY viral clone, will facilitate the targeted design of chimeric viruses, enabling the identification of the genetic determinants of viral infection. The second tool, an enzymatic reporter probe, will enable viral detection earlier in infection. Together, these tools will enable investigating the virus-host interactions PVY uses to hijack the host and spread throughout the plant. Understanding the protein- protein interactions that enable PVY to establish infection is essential to developing resistant cultivars. To build research capacity, the project will engage students in cross-disciplinary research as part of laboratory-based and course-based activities.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14844", "attributes": { "award_id": "2343976", "title": "How Do Individuals Form Beliefs About and Evaluate Multi-Layer Prospects under Risk and Ambiguity", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Economics" ], "program_reference_codes": [], "program_officials": [ { "id": 27684, "first_name": "Eric", "last_name": "Bahel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-01", "end_date": null, "award_amount": 384714, "principal_investigator": { "id": 5995, "first_name": "Michael", "last_name": "Price", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 5996, "first_name": "Laura", "last_name": "Razzolini", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 229, "ror": "", "name": "University of Alabama Tuscaloosa", "address": "", "city": "", "state": "AL", "zip": "", "country": "United States", "approved": true } ] }, { "id": 31522, "first_name": "Tigran", "last_name": "Melkonyan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 229, "ror": "", "name": "University of Alabama Tuscaloosa", "address": "", "city": "", "state": "AL", "zip": "", "country": "United States", "approved": true }, "abstract": "This project is jointly funded by the Economics program and the Established Program to Stimulate Competitive Research (EPSCoR). The research team investigates how individuals (i) process information when they are confronted with prospects characterized by multiple layers of risk and/or ambiguity and (ii) evaluate and make choices over such prospects. As an example of a multi-layered uncertain prospect, consider change where the translation of greenhouse gas emissions into regional or global climatic effects and the subsequent adverse climate effects are uncertain; or a public health emergency, where the likelihood of a future pandemic and its subsequent adverse effects on health outcomes are uncertain. This research project will outline a series of laboratory and survey experiments designed to elicit key behavioral parameters to inform development of theories of choice under multi-layer uncertainty.<br/><br/>This project will develop novel methods for eliciting preferences and beliefs in situations characterized by multi-layer uncertainty. This will advance methodologies to elicit preferences over policy alternatives to address climate hazards or prevent the next pandemic and uncover factors that shape our perceptions of and attitudes towards compound and reduced form representations of multi-layer ambiguous prospects. Our experiments are designed to (i) elicit behavioral parameters to help develop models of decision-making under multi-layer uncertainty; and (ii) test existing models of choice under multi-layer risk and the formation of beliefs when facing multi-layer uncertainty, across gain and losses domains. The results will inform policy makers and local community leaders and enhance resilience towards uncertain events such as environmental change or future pandemics.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14845", "attributes": { "award_id": "2436941", "title": "Conference: Cnidofest: A workshop on cnidarian model organism biology, August 14th-17th, Bethlehem, PA", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Animal Developmental Mechanism" ], "program_reference_codes": [], "program_officials": [ { "id": 7428, "first_name": "Anna", "last_name": "Allen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-01", "end_date": null, "award_amount": 20000, "principal_investigator": { "id": 31524, "first_name": "Michael", "last_name": "Layden", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31523, "first_name": "Celina", "last_name": "Juliano", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 341, "ror": "https://ror.org/012afjb06", "name": "Lehigh University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Cnidarian Model Systems Meeting, or \"Cnidofest,\" is a biennial conference that brings together researchers studying cnidarians, a group of animals that includes jellyfish, corals, and sea anemones. These animals have several features of scientific interest, including their importance to ocean health, for example coral reef habitats, and their unique biology, for example their extreme regenerative abilities and diverse forms. Cnidofest 2024 is the third iteration of this meeting and is taking place at Lehigh University. The first Cnidofest was held in 2018 at the University of Florida and the second in 2022 at the University of California, Davis after taking 2020 off due to the pandemic. This growing conference emphasizes the importance of bringing together scientists of all career stages. The conference prioritizes trainee involvement and networking, with trainees giving 75% of the oral presentations. Through the support of trainees, Cnidofest is committed to expanding the community, both in total numbers and in diversity. In particular, expanding opportunities for researchers from diverse backgrounds as well as researchers using diverse model systems is a high priority. Therefore, efforts are made to keep costs low and provide financial support for trainees, ensuring broad participation. Cnidofest also showcases cutting-edge technologies, helping researchers integrate new tools into their research. These talks are given by researchers outside of the community to enable new perspectives. Overall, Cnidofest supports the growth and advancement of the cnidarian research community and is vital component of their success.<br/><br/>Cnidarian laboratory models have been used to make fundamental discoveries, including in neurobiology, developmental biology, ecology, evolution, and symbiosis. However, this group of organisms have been historically understudied due to technology limitations. In the past several years, this has changed due to advances in genomics and gene manipulation technologies that can now be easily applied to diverse animals. The advantages of cnidarians for laboratory research include: 1) Simple, well-understood body plans with highly complex and variable life cycles. 2) An informative phylogenetic position as the clade that is sister to bilaterians; discoveries made in cnidarians often uncover deeply conserved processes. 3) Transparency makes them amenable to live imaging. 4) Interesting biology such as self/non-self recognition, the study of algal symbioses, such as found in reef-building corals, and extreme regenerative abilities. The Cnidarian Model Systems Meeting, or \"Cnidofest 2024,\" will bring together researchers studying diverse cnidarians and will emphasize new technological approaches to enhance cnidarian research. Three technology speakers will present seminars on emerging technologies 1) Spatial Transcriptomics, 2) Expansion Microscopy, and 3) Optogenetics. Interactions among participants will be encouraged through a schedule that includes oral presentations, lightning talks, and poster presentations. In addition, time is built into the schedule for informal discussions over breaks and meals, which are all done as one group. The goal is to exchange ideas for advancing research in cnidarians, as well as foster growth in the community by supporting trainee meeting costs.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14846", "attributes": { "award_id": "2409903", "title": "Development of novel numerical methods for forward and inverse problems in mean field games", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "COMPUTATIONAL MATHEMATICS" ], "program_reference_codes": [], "program_officials": [ { "id": 31483, "first_name": "Troy D.", "last_name": "Butler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-01", "end_date": null, "award_amount": 298862, "principal_investigator": { "id": 31525, "first_name": "Yat Tin", "last_name": "Chow", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Mean field games is the study of strategic decision making in large populations where individual players interact through a certain quantity in the mean field. Mean field games have strong descriptive power in socioeconomics and biology, e.g. in the understanding of social cooperation, stock markets, trading and economics, biological systems, election dynamics, population games, robotic control, machine learning, dynamics of multiple populations, pandemic modeling and control as well as vaccination distribution. It is therefore essential to develop accurate numerical methods for large-scale mean field games and their model recovery. However, current computational approaches for the recovery problem are impractical in high dimensions. This project will comprehensively study new computational methods for both large-scale mean field games and their model recovery. The comprehensive plans will cover algorithmic development, theoretical analysis, numerical implementation and practical applications. The project will also involve research on speeding up the forward and inverse problem computations to speed up the computation for mean field game modeling and turn real life mean field game model recovery problems from computationally unaffordable to affordable. The research team will disseminate results through publications, professional presentations, the training of graduate students at the University of California, Riverside as well as through public outreach events that involve public talks and engagement with high school math fairs. The goals of these outreach events are to increase public literacy and public engagement in mathematics, improve STEM education and educator development, and broaden participation of women and underrepresented minorities.<br/><br/>The project will provide novel computational methods for both forward and inverse problems of mean field games. The team will (1) develop two new numerical methods for forward problems in mean field games, namely monotone inclusion with Benamou-Brenier's formulation and extragradient algorithm with moving anchoring; (2) develop three new numerical methods for inverse problems in mean field games with only boundary measurements, namely a three-operator splitting scheme, a semi-smooth Newton acceleration method, and a direct sampling method. Both theoretical analysis and practical implementations will be emphasized. In particular, numerical methods for inverse problems for mean field games, which is a main target of the project, will be designed to work with only boundary measurements. This represents a brand new field in inverse problems and optimization. The project will also seek the simultaneous reconstruction of coefficients in the severely ill-posed case when only noisy boundary measurements from one or two measurement events are available.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14847", "attributes": { "award_id": "2431893", "title": "Conference: 2024 & 2025 NSF Louis Stokes Alliances for Minority Participation (LSAMP) Stakeholders Convenings", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Alliances-Minority Participat." ], "program_reference_codes": [], "program_officials": [ { "id": 884, "first_name": "Martha", "last_name": "James", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-15", "end_date": null, "award_amount": 1154768, "principal_investigator": { "id": 31526, "first_name": "Christopher", "last_name": "Botanga", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 238, "ror": "https://ror.org/05ekwbr88", "name": "Chicago State University", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "The Louis Stokes Alliances for Minority Participation (LSAMP) program assists universities and colleges in diversifying the science, technology, engineering, and mathematics (STEM) workforce through their efforts at significantly increasing the numbers of students from historically underrepresented minority populations (Blacks and African Americans, Alaska Natives, American Indians, Hispanic and Latino Americans, Native Hawaiians, and Native Pacific Islanders) to successfully complete high-quality degree programs in STEM.<br/><br/>Chicago State University (CSU) will organize and convene two meetings of the LSAMP grantee community which will occur in-person in Washington, DC over two days each in October 2024 and Winter/Spring 2025. These events will provide a forum for updates on NSF funding opportunities, policies and procedures. Additionally, participants will share successful practices in STEM broadening participation post-pandemic. Emphasis will be placed on the Chips and Science Act of 2020. <br/><br/>All LSAMP program grantees, selected LSAMP student participants, and representatives from federal laboratories and the corporate sector will participate. Proceedings from the meeting will be disseminated broadly via the organizer's websites.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14848", "attributes": { "award_id": "2406488", "title": "Implementation Project: Leveraging Innovation and Discovery for STEM Success (LIDSS)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Hist Black Colleges and Univ" ], "program_reference_codes": [], "program_officials": [ { "id": 27246, "first_name": "Alfred", "last_name": "Hall", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-15", "end_date": null, "award_amount": 3000000, "principal_investigator": { "id": 31528, "first_name": "Connie", "last_name": "Walton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31527, "first_name": "Stacey", "last_name": "Duhon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2502, "ror": "https://ror.org/05mnb6484", "name": "Grambling State University", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Historically Black Colleges and Universities - Undergraduate Program (HBCU-UP) provides support to strengthen STEM undergraduate education and research at HBCUs. This Implementation Project: Leveraging Innovation and Discovery for STEM Success (LIDSS) is a comprehensive effort at Grambling State University to prepare highly competitive STEM graduates to meet the challenges of an ever-changing world. Discovery and Innovation are the core of the design for each strategic activity. This project aligns with the goals of HBCU-UP in its work to foster STEM student success via its support of faculty research experiences, student support programs, and outreach initiatives for K-12 students and teachers.<br/><br/>The overarching goal of this project is to enhance the ability of Grambling State University to train highly prepared STEM majors to meet workforce needs, while reversing the effects that the pandemic has had on education at all levels. The components of this project were identified using a challenge-based learning approach. STEM faculty and students identified the problems and provided possible solutions. The overall premise is STEM education must not remain static but constantly evolve to meet a changing world. The model used to design each component of this project has discovery and innovation as the core for STEM Learning. The LIDSS project aims to improve the recruitment, retention and graduation of STEM students. A priority will be given to the recruitment of veterans as STEM majors. A STEM Entrepreneurship Academy and a Makers Space will support faculty being able to integrate entrepreneurship within curricula to further nurture the creativity of STEM majors. A Student Success Initiative will be established that will create a judgement free zone where students can enhance skills with assistance from faculty/student leader teams. This project aims to establish partnerships with research intensive institutions to expand the research capacity of STEM faculty through collaboration and mentoring opportunities. The results of this project should be of great interest to educators who also face challenges related to recruiting, retaining and graduating STEM students who are prepared to be innovative leaders. A Biennial Symposium that will focus on the use of innovative educational practices to promote STEM learning will be hosted on campus. Data collected in this project, including the symposia, will advance the knowledge of best practices that will lead to improved STEM programs that are nimble and able to utilize innovative strategies to respond to ever changing needs.<br/><br/>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.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1383, "pages": 1397, "count": 13961 } } }