Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1424&sort=-end_date
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-end_date", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=-end_date", "next": null, "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1423&sort=-end_date" }, "data": [ { "type": "Grant", "id": "15735", "attributes": { "award_id": "2433308", "title": "Collaborative Research: III: Small: Towards A Computational Foundation of Teams Network Science", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Info Integration & Informatics" ], "program_reference_codes": [], "program_officials": [ { "id": 27554, "first_name": "Raj", "last_name": "Acharya", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-08-01", "end_date": null, "award_amount": 400000, "principal_investigator": { "id": 28389, "first_name": "Hanghang", "last_name": "Tong", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32792, "first_name": "ChengXiang", "last_name": "Zhai", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 281, "ror": "", "name": "University of Illinois at Urbana-Champaign", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "Teams appear in almost any organization such as universities, corporations, and governments. The importance of teams is even more evident with the work practice has been evolving to a new hybrid mode – a combination of work in office and from home which inevitably changes how people collaborate as a team. Consequently, it presents new challenges to team collaborations, in that it increases difficulty of communications, stifles innovation, and affects collaboration. Despite an organization as well as an individual’s profound dependency on teams and the rapid changing landscape of team-enabled operations, computational models, algorithms and tools to optimize the team collaboration are lacking and lagging. To name a few, how to model the multi-channel, multi-platform team collaboration data? How to foresee the rising or the falling of a team at an early stage? How to form a high-performing team as well as to enhance the performance of an existing team? This project develops data mining models, algorithms and tools to optimize team collaboration facing novel challenges in a new hybrid working environment. It consists of three mutually complementary and synergistic research tasks. The first task models the raw team collaboration data to provide a worldview representation of how complex tasks are conducted by teams in multiple channels and platforms. The second task builds multi-task, multi-target predictive models to forecast the performance of a given team. The third task develops algorithms and tools to optimize teams. Specially, it develops data-driven approaches to form and enhance teams. Based on that, it develops reinforcement learning based methods to proactively optimize teams and game-theoretic methods to interactively optimize teams by incorporating user feedback. This project helps improve team efficacy, and optimize human resource allocation, thereby mitigating the challenges that the post-pandemic age has posed to the workforce. The project team actively seeks to engage under-represented students. The research outcome of this project is disseminated through publications, tutorials and open-source software. 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": "15736", "attributes": { "award_id": "2534608", "title": "Investigating the Uptake of Research-Based Instructional Strategies: A Post-COVID Update", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "IUSE" ], "program_reference_codes": [], "program_officials": [ { "id": 1859, "first_name": "Mike", "last_name": "Ferrara", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-06-01", "end_date": null, "award_amount": 113972, "principal_investigator": { "id": 31635, "first_name": "Naneh", "last_name": "Apkarian", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 448, "ror": "", "name": "San Diego State University Foundation", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by mapping national patterns in the use of research-based instructional practices in post-secondary chemistry, mathematics, and physics courses five years after the disruptions due to the COVID-19 pandemic. In the Spring of 2019, a survey was sent to roughly 18,000 instructors of first-year mathematics, chemistry, and physics courses at nearly 1000 post-secondary institutions. That survey provided a comprehensive view of introductory science courses and instructors across the United States, with responses from nearly 4000 faculty from 660 U.S. colleges and universities. However, just one year later colleges and universities across the nation quickly shifted to online, emergency remote teaching in response to the COVID pandemic. The scale of instructional change during this time was both unprecedented and ubiquitous, with nearly every instructor teaching in the spring of 2020 required to try something new, and many needing to continue experimenting and revising their courses for the following semesters. This project will repeat the 2019 survey in order to characterize any lasting impact of the COVID pandemic on undergraduate science education and understand what this new instructional landscape may mean for change agents working to improve undergraduate science education through the uptake of research-based instructional practices. The goals of this project are to 1) understand the impact of the COVID pandemic on undergraduate science education as well as provide a current description of undergraduate science instruction, and 2) in consideration of any shifts following the COVID disruption to higher education, revise and update the research-based insights and recommendations for supporting and achieving instructional change in undergraduate STEM. To do so, the roughly 18,000 instructors will be re-surveyed. Some of the survey analyses will be conducted on the new responses alone, including multilevel modeling of the impact of malleable factors on instructors’ adoption of research-based instructional practices. Other analyses will incorporate the prior results for pre-post analysis to capture changes in the practices of both individuals and the disciplines in the aggregate. Where changes are observed, additional statistical tests and modeling will be used to identify the impact of emergency response teaching strategies on those shifts. These findings will be used by change agents (e.g., professional development organizations, instructional coaches) to better support undergraduate instructors in implementing research-based instructional strategies and by administrators (e.g., department chairs, course coordinators) in making resource allocations and policy decisions. These results will update the foundational knowledge base needed to support widespread pedagogical shifts toward the use of research-based instructional practices in post-secondary STEM education, impacting undergraduate students across the country. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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": "15737", "attributes": { "award_id": "2534132", "title": "HBCU-UP RAPID: HBCU Leadership Crisis on STEM Broadening Participation and Research Capacity Building - Impact and Implications", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Hist Black Colleges and Univ" ], "program_reference_codes": [], "program_officials": [ { "id": 1154, "first_name": "Carleitta", "last_name": "Paige-Anderson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-02-15", "end_date": null, "award_amount": 199999, "principal_investigator": { "id": 4514, "first_name": "Trina", "last_name": "Fletcher", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 207, "ror": "https://ror.org/02gz6gg07", "name": "Florida International University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 2608, "ror": "", "name": "University of Arkansas at Pine Bluff", "address": "", "city": "", "state": "AR", "zip": "", "country": "United States", "approved": true }, "abstract": "The Historically Black Colleges and Universities - Undergraduate Program (HBCU-UP) supports RAPID projects when there is an urgency concerning the availability of, or access to, data, facilities, or specialized equipment, including quick-response research on natural or anthropogenic disasters and similar unanticipated events, such as the COVID-19 pandemic. During and after the COVID-19 pandemic, several higher education institutions experienced changes in the president and chancellor positions. For Historically Black Colleges and Universities (HBCUs), in 2022 alone, there were 23 leadership changes announced, and in 2023, 41 changes were announced, almost double within one year. Essentially, one in four HBCUs experienced a resignation or termination at the highest administrative level. These leadership changes have been an added challenge to the ongoing recovery efforts of many HBCUs that were also disproportionately impacted by the global pandemic. HBCUs are critical for science, technology, engineering, and mathematics (STEM) education and workforce development and for their contributions to STEM research. HBCUs are critical players in helping the nation stay competitive globally and are a national asset, considering the large numbers of diverse students earning degrees in STEM from HBCUs. Unfortunately, excessive executive leadership turnover could negatively impact those efforts. This research study will explore the institutional impact of turnover at the President/Chancellor and executive cabinet levels at HBCUs. By using pilot data collected at one of the largest annual convenings of HBCU executives related to the impact and implications of HBCU leadership turnover, our proposed convening to collect rich qualitative data, and their feedback on the pilot survey results, will dynamically and strategically gain insight on this unprecedented challenge. This project will contribute to better understanding the impacts of leadership turnover and create recommendations for best practices. Ultimately, the results from this study are intended to increase stability at HBCUs experiencing leadership transitions so HBCUs can continue to play their important role in broadening participation in STEM, undertaking important STEM research, and providing excellent STEM educational programs. 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": "15806", "attributes": { "award_id": "2603320", "title": "Rational Design and Fundamental Understanding of Multimodal Amyloid Probes", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "BIOSENS-Biosensing" ], "program_reference_codes": [], "program_officials": [ { "id": 961, "first_name": "Aleksandr", "last_name": "Simonian", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-12-01", "end_date": null, "award_amount": 361996, "principal_investigator": { "id": 1842, "first_name": "Jie", "last_name": "Zheng", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 405, "ror": "https://ror.org/02kyckx55", "name": "University of Akron", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 240, "ror": "", "name": "University of Texas at San Antonio", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The hallmark of many debilitating diseases, such as Alzeimer’s disease (AD) and type II diabetes (T2D), is the presence of abnormal masses/aggregates of proteins termed “amyloids”. These amyloids, in which composition is disease dependent, are generally considered to be ideal markers for disease diagnosis and therapeutic intervention. Unfortunately, existing probes are limited in that they are only able to detect the presence of a single targeted amyloid protein. This project will develop a new class of generic, multiple-mode, multi-target amyloid probes that will detect a wide variety of proteins associated with different amyloid diseases. Design principles for the multimodal probes can be transformed to numerous molecular-recognition applications for targeted drug therapy, biomarker detection, and disease diagnostics (e.g., cancers and COVID-19). The proposed multi-disciplinary research activities will provide diverse training for students at all levels, especially from underrepresented and low-income families. The students will develop knowledge and skills in data mining, molecular simulations, neuroscience, and lab-on-chip techniques in close relation to public health problems. Finally, the integrated educational and research activities will enrich the curriculum of the Corrosion Engineering program at the University of Akron.\r\n\r\nThe overall objectives of this project are to (1) fully explore, identify, and engineer – with both data-driven simulations and experiments – a new family of AIE@βPs (an aggregation-induced emission (AIE) molecule conjugated with small β-sheet-forming peptides (βPs)) probes capable of early and enhanced detection of multiple pathological aggregates and co-aggregates formed by the same and different amyloid proteins, which co-exist in human body fluids across different amyloid diseases and (2) conduct fundamental sequence-structure-recognition studies on these multi-mode, multiple-target AIE@βPs probes. The AIE molecule targets the aggregated amyloids and avoids the aggregation-induced quenching, while βPs target the β-structures of amyloid aggregates via specific β-sheet interactions. The project’s objectives will be achieved via three tasks: (1) develop a machine-learning model, combined with molecular simulations and biophysical experiments, to screen, identify, and validate a library of βPs capable of self-assembling into β-sheet structures and cross-interacting with both Aβ (associated with AD) and hIAPP (associated with T2D); (2) design and synthesize a series of AIE@βPs probes to detect Aβ, hIAPP, and hybrid Aβ-hIAPP species at different aggregation states for demonstrating “conformational-specific, sequence-independent” mechanisms via synergetic AIE- and βPs-induced binding modes; and (3) transform AIE@βPs probes into different amyloid sensors via surface immobilization by controlling their packing structures, densities, and patterns of AIE@βPs. In parallel, multiscale molecular simulations will be conducted to study the structures, dynamics, and interactions of βPs and AIE@βPs with amyloid aggregates in solution and on surfaces, which will be correlated with amyloid recognition mechanisms of AIE@βPs by experiments. If successful, this work will provide new design principles and sensor systems for early amyloid detection beyond few available today.\r\n\r\nThis 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": "15807", "attributes": { "award_id": "2536516", "title": "Enhancing the Reach and Contributions of Informal STEM Learning: A Consensus Study", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "AISL" ], "program_reference_codes": [], "program_officials": [ { "id": 4115, "first_name": "Ellen", "last_name": "McCallie", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-09-15", "end_date": null, "award_amount": 1100000, "principal_investigator": { "id": 3230, "first_name": "Heidi", "last_name": "Schweingruber", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 339, "ror": "https://ror.org/038mfx688", "name": "National Academy of Sciences", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 339, "ror": "https://ror.org/038mfx688", "name": "National Academy of Sciences", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "Opportunities to learn about science, technology, engineering, and mathematics (STEM) are all around. Yet, people often assume that formal school settings are the only places to learn about the STEM disciplines. A consensus report from The National Academies, Learning Science in Informal Environments: People, Places and Pursuits (NRC, 2009), helped to upend this assumption and galvanized over a decade of expansion in programming and research focused on informal STEM learning. In the years since publication of the 2009 report, opportunities to learn science and STEM more broadly in informal environments have greatly expanded and they now serve as an essential component of STEM education and engagement across the country. In parallel with the expansion of programs, research on all aspects of STEM learning has continued to progress, offering new insights into how to improve people's STEM learning in all settings. Given this tremendous growth, the new insights generated by advances in research, and the considerable changes wrought by the COVID-19 pandemic the time is ripe for taking stock of the past 15 years of work. This new consensus study will update the 2009 report to codify what is currently known about how to best support learning across informal STEM environments, thus laying the groundwork for effective decision-making in practice, policy, and research in the field. The report will help to identify gaps for where additional programming in informal STEM education would be valuable and help decisionmakers to better understand the landscape in order to advocate for high-quality STEM learning opportunities.\r\n\r\nThe Board on Science Education at the National Academies of Sciences, Engineering, and Medicine will appoint an expert committee to conduct a consensus study on Enhancing the Reach and Contributions of Informal STEM Learning. The study will take stock of the evidence base on STEM learning in informal environments, and identify trends in research and practice across the range of informal STEM learning experiences and environments that compose the field of lifelong STEM learning. The consensus report will: (1) characterize the state of informal STEM learning by defining who participates and supports learning in informal environments, as well as describing the nature of programming and learning opportunities in the United States; (2) discuss how understandings of learning have evolved over time, describe where the field has seen the most growth over the past decade in research and practice, and identify infrastructures and organizational/institutional practices that have emerged in that time; (3) identify evidence-based strategies that can be used to expand the reach of informal STEM learning, and point to relevant challenges and opportunities; and (4) develop recommendations for policy, practice, and research for enhancing the reach and contributions of informal STEM learning experiences.\r\n\r\nThis consensus study project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing everyone multiple pathways for accessing and engaging in STEM learning experiences.\r\n\r\nThis 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": "15808", "attributes": { "award_id": "2546659", "title": "CAREER: Re-Thinking the Perception-Action Paradigm for Agile Autonomous Robots", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "FRR-Foundationl Rsrch Robotics" ], "program_reference_codes": [], "program_officials": [ { "id": 26885, "first_name": "Eyad", "last_name": "Abed", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-07-01", "end_date": null, "award_amount": 600000, "principal_investigator": { "id": 719, "first_name": "Giuseppe", "last_name": "Loianno", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 167, "ror": "https://ror.org/0190ak572", "name": "New York University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 176, "ror": "", "name": "University of California-Berkeley", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Autonomous robots will become pervasive in our society and will solve complex tasks, actively collaborating with each other and with humans. As the recent COVID-19 outbreak has highlighted, autonomous robots can solve a range of time-sensitive problems including logistics, reconnaissance, and disinfection of critical areas. Beyond pandemic, small-scale robots can help humans in complex or dangerous tasks such as search and rescue, security, and surveillance, and, thanks to their lighter weight, they pose only a modest risk to human safety. These time-sensitive tasks require robots to make fast decisions and agile maneuvers in complex and dynamic environments. State-of-the-art autonomous navigation approaches, while mature, are slow and brittle and prevent robust and resilient agile navigation. This Faculty Early Career Development (CAREER) Program studies the fundamental perception-action problem for agile navigation of autonomous robots in complex environments by planning a novel, low-latency, robust, adaptive, safe, and resilient paradigm. This project aims also to educate students on the technical aspects, societal benefits, and ethical use of autonomous systems by establishing a unique multi-disciplinary, and integrated research and educational platform which includes a core curriculum on robot localization and navigation, and a series of online racing hackathons for a post-pandemic customized and integrated research and educational experience. These will contribute to lowering the barrier to participation in research and education for students.\r\n\r\nThis project will generate a new foundational theory, which includes models and algorithms resulting from a principled combination of perception, learning, and control to holistically design visual perception and action to create small-scale agile autonomous robots. The goal is to capture the strict cross–coupling effects between perception and action to jointly and concurrently resolve the perception-action problem to speed up the robots’ decision making process and increase their agility. The project is organized in three thrusts according to a series of objectives, culminating in innovations in robotics autonomy research and education. A compressed and unified representation of the perception and action spaces guarantees to reduce the robot's inference latency and naturally reveals the cross-coupling effects among them. Next, the robot will exploit using this representation its action-predictive information to enhance its inference capabilities and will employ an optimal control/planning approach to maximize its perception accuracy and quality.\r\n\r\nThis project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).\r\n\r\nThis 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": 1424, "pages": 1424, "count": 14236 } } }