Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=4&sort=-funder_divisions
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-funder_divisions", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1397&sort=-funder_divisions", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=5&sort=-funder_divisions", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=3&sort=-funder_divisions" }, "data": [ { "type": "Grant", "id": "10442", "attributes": { "award_id": "2221469", "title": "Engineering Academic Pathways", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "S-STEM-Schlr Sci Tech Eng&Math" ], "program_reference_codes": [], "program_officials": [ { "id": 1223, "first_name": "Alexandra", "last_name": "Medina-Borja", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-10-01", "end_date": "2028-09-30", "award_amount": 1499608, "principal_investigator": { "id": 26388, "first_name": "Brett", "last_name": "Tempest", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26386, "first_name": "Stephanie N", "last_name": "Galloway", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26437, "first_name": "Catherine", "last_name": "Blat", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26438, "first_name": "Sejal", "last_name": "Foxx", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 248, "ror": "https://ror.org/04dawnj30", "name": "University of North Carolina at Charlotte", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at the University of North Carolina at Charlotte, an urban, access-oriented institution. Over its 6-year duration, this project will fund scholarships to 25 unique full time students who are pursuing bachelor’s degrees in Civil Engineering, Mechanical Engineering, Systems Engineering, and Engineering Technology. Eligible scholars will be able to receive up to four years of support while they complete their undergraduate degree. A suite of evidence-based programming will be deployed to enhance opportunities for social-emotional learning, academic skills development, and social and navigational capital building that were missed due to the pandemic. The project makes an urgent, evidence-based response to pandemic impacts on low-income students’ preparation for and enrollment of engineering majors, as well as their missed opportunities for social and emotional learning. Key components of programing include a summer bridge program, high engagement mentoring, a college skills and professional development seminar, and dedicated advising. The programing will improve employment prospects by developing social and cultural capital in students. Through outreach, the program will also help large numbers of high school students learn about engineering majors and prepare them for the college application process and will train high school counselors about engineering opportunities for low income students.\n\nThe Engineering Academic Pathways program is specifically designed to enhance the prospects of economic mobility by responding to the unique needs of low-income students that the pandemic has substantially exacerbated. Recent data indicate the pandemic has disproportionately harmed people in low-income households relative to employment, health, and well-being. Prior to the setbacks of the COVID 19 pandemic, Charlotteans were responding to substantial disparities in opportunity after the city was ranked 50th out of the 50 largest US cities for economic mobility in 2015. The program will implement four of the strategies for improving economic mobility that were recommended by the Charlotte-Mecklenburg Opportunity Task Force in 2017. First is to broaden the range of and access to high quality college and career pathways offered by K-12 and postsecondary institutions. Second is to equip all students and their parents with the information and guidance they need to understand and navigate multiple college and career pathways, preparation, and processes. Third is to expand and strengthen support for First Generation and other low-socioeconomic students who need help transitioning to and completing secondary education. Fourth, and finally, is to elevate and actively promote the critical importance of acquiring a post-secondary degree. The success of individual elements of the program will be rigorously evaluated and adapted for the greatest effectiveness. This will advance understanding of the unique needs of low income students in a post-pandemic world and enable the dissemination of best practices through professional development seminars and scholarly publications to other institutions that are reacting to similar conditions. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students.\n\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": "4586", "attributes": { "award_id": "1356496", "title": "Rewarding Achievement in Mathematics and Science (RAMS) Scholarships", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "S-STEM-Schlr Sci Tech Eng&Math" ], "program_reference_codes": [], "program_officials": [], "start_date": "2014-03-15", "end_date": "2021-02-28", "award_amount": 607830, "principal_investigator": { "id": 15835, "first_name": "Richard", "last_name": "Kopec", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1311, "ror": "", "name": "Saint Edward's University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 15832, "first_name": "Charles R", "last_name": "Hauser", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 15833, "first_name": "Carol", "last_name": "Gee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 15834, "first_name": "Michael U", "last_name": "Kart", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1311, "ror": "", "name": "Saint Edward's University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "A team of faculty and administrators at St. Edwards University is increasing the number of academically talented students with demonstrated financial need who earn baccalaureate degrees in the bioinformatics, environmental and forensic chemistry, computer science, and mathematics. The Rewarding Achievement in Mathematics and Science (RAMS) project is providing 14 scholarships to students who are being retained as STEM majors through participating in a close-knit community of learners. This student cohort is developing academically and socially through an array of support mechanisms, including: (a) a first year seminar, (b) supplemental instruction and peer-led team learning, (c) a science living and learning community, (d) the Science Speakers Series, (e) summer research, and (f) internships with local industrial partners. The PI team is recruiting from a diverse pool of students in which 35% of their STEM majors are Hispanic.\n\nThis project is generating a broad impact in two ways. First, data generated through assessment and evaluation is supporting the rationale that high retention of students in STEM fields can be achieved through a comprehensive program of student development that emphasizes community-building. Formative and summative evaluation is focusing on assessment of the project's ability to recruit, retain, and graduate students. Secondly, dissemination of the project results is providing a model for retaining students in the STEM fields by integrating them into a close-knit community of learners. The project team is presenting their work throughout their many regional and national consortia including the New American Colleges and Universities, the Texas Hispanic-Serving Institutions, and the Catholic Colleges and Universities of Texas.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "4762", "attributes": { "award_id": "1259496", "title": "High-Achievers Scholarship Program in Computer Science and Mathematics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "S-STEM-Schlr Sci Tech Eng&Math" ], "program_reference_codes": [], "program_officials": [], "start_date": "2013-09-01", "end_date": "2018-01-31", "award_amount": 620750, "principal_investigator": { "id": 16512, "first_name": "Rahman", "last_name": "Tashakkori", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 426, "ror": "https://ror.org/051m4vc48", "name": "Appalachian State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 16508, "first_name": "James T", "last_name": "Wilkes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 16509, "first_name": "Cindy", "last_name": "Norris", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 16510, "first_name": "Mark C", "last_name": "Ginn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 16511, "first_name": "Vicky W", "last_name": "Klima", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 426, "ror": "https://ror.org/051m4vc48", "name": "Appalachian State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The S-STEM program, The High-Achievers Scholarship Program in Computer Science and Mathematics, at Appalachian State University (ASU) increases the high technology workforce and the number of Computer Science and Mathematic's students pursuing graduate degrees by increasing educational opportunities for economically disadvantaged students with the potential to succeed. The program increases academic opportunities for students from the Appalachia region in multiple ways, by improving the support infrastructure for all Computer Science and Mathematics students, establishing connections with regional high technology industry, providing leadership training opportunities, and engaging students in research.\n\nThe intellectual merits of this program include: 1) It enables on average 21 academically talented, financially disadvantaged scholars per year to make progress toward gaining undergraduate or graduate degrees in two STEM fields; 2) It includes a STEM seminar that initiates community building, mentoring, and research activities, supplemented with value-added components such as leadership workshops and mentoring relationships with previous ASU S-STEM scholars who have graduated; 3) It supports scholar participation in faculty mentored research projects and the dissemination of results, including conference publications and presentations.\n\nThe broader impacts of this program include: 1) Enhancing educational opportunities for disadvantaged students from the Appalachian region; 2) Increasing student support services for all STEM students at ASU; 3) Contributing to the economic development of the Appalachian region and North Carolina through increasing STEM workforce capacity.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "13190", "attributes": { "award_id": "2153184", "title": "CRII: RI: Analysis and Applications of Multi-Level Games", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Robust Intelligence" ], "program_reference_codes": [], "program_officials": [ { "id": 2534, "first_name": "Roger", "last_name": "Mailler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-05-01", "end_date": null, "award_amount": 175000, "principal_investigator": { "id": 29236, "first_name": "Mithun", "last_name": "Chakraborty", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>With artificial intelligence (AI) becoming progressively more competent and ubiquitous, an AI entity is more likely to find itself operating in a complex, dynamic environment along with other intelligent entities all with different goals and disparate information or beliefs about their environment and each other. Examples include bidders in an online ad auction, algorithmic trading agents in a financial market, (cyber)attackers and defenders in a network, a hierarchy of policymakers formulating a decentralized epidemic response plan in a decentralized manner, and so on. As such, game theory, the systematic study of such multi-agent strategic interactions (or, games as they are called), is becoming increasingly fundamental to AI research and practice. Empirical game-theoretic analysis (EGTA) is a principled framework for (approximately) reasoning about games that are beyond the scope of traditional, analytical game-theoretic methods either because the games are too complex or because obtaining information about possible plays of the game is too expensive. This project seeks to make fundamental methodological improvements to the EGTA framework by constructing finer- grained models (i.e., more accurate approximations) of the game under consideration than those prevalent in the state of the art.<br/><br/>Specifically, the aim of the project is to adapt EGTA to multi-level game model forms: strategic interactions that can be represented in the form of a directed, rooted tree. Extensive-form games (EFGs) are a classic example of this class of games that capture temporal patterns in agent activity, information revelation, and possible stochastic events (acts of Nature). Current EGTA practice abstracts all such temporal patterns away in a simulator (e.g., an agent-based model) that is queried to obtain payoff data for strategy combinations over players but induces a coarser game model that is essentially normal-form and does not reflect such patterns. This project will take the next step in EGTA design by explicitly incorporating features of the underlying game tree into the empirical game model itself and tackling the resulting conceptual and computational design challenges such as striking a balance between model granularity (which leads to better approximation) and per-iteration computational burden. The second phase of the project will seek to transfer knowledge gained from development of EGTA for EFGs to similar treatments of other tree-based game model forms. Advances in this project can significantly improve our understanding of systems that comprise interacting AI agents employing highly sophisticated strategies (such as deep reinforcement learning algorithms), and in turn inform robust design of such agents.<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": "15307", "attributes": { "award_id": "2450124", "title": "Collaborative Research: NSF-CSIRO: Fair Sequential Collective Decision-Making", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Robust Intelligence" ], "program_reference_codes": [], "program_officials": [ { "id": 1863, "first_name": "Erion", "last_name": "Plaku", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-08-15", "end_date": null, "award_amount": 299977, "principal_investigator": { "id": 2616, "first_name": "Lirong", "last_name": "Xia", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 148, "ror": "https://ror.org/01rtyzb94", "name": "Rensselaer Polytechnic Institute", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 218, "ror": "", "name": "Rutgers University New Brunswick", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to develop (artificial intelligence) AI-powered approaches to address challenging societal problems, such as dealing with droughts, infectious diseases, and environmentally harmful emissions. This project engages specific questions in these areas, such as: How can one effectively allocate water resources to increase agricultural drought resilience during drought seasons? How can one effectively determine when and where to construct hydrogen or electric vehicle refueling stations to encourage citizens to adopt these technologies to lower emissions? How can one effectively distribute vaccines and other medical supplies daily to enhance response to infectious diseases during pandemics? These problems belong to a class of classical and important problems in sequential collective decision-making. While sequential decision-making and collective decision-making have been studied previously, decision-making problems that are simultaneously sequential and collective are poorly understood, especially for specific domains such as resource allocation and when combined with goals such as responsibility and equitability. The overarching project goal is to establish theoretical and algorithmic foundations for responsible and equitable AI-powered sequential, collective decision-making. It also seeks to ensure that sequences of decisions satisfy multiple objectives and make appropriate trade-offs between short and long-term rewards subject to fairness criteria. The proposed research will lead to efficient and fair solutions to our social good and use-inspired applications in drought resilience, towards net zero, and infectious disease resilience. <br/><br/>The project will achieve its goals by focusing on three interconnected challenges, leveraging a wide range of techniques from AI, economics, and operation research. First, the challenge of fair multi-objective collective decision-making for a single time period subject to multiple objectives and fairness criteria. Second, explore fair sequential multi-objective collective decision-making that addresses trade-offs between immediate and long-term efficiency and fairness. Finally, understand the strategic aspects of fair multi-objective collective decision-making in collaboration with stakeholders, who provide information that facilitates the process.<br/><br/>This is a joint project between United States and Australian researchers funded by the Collaboration Opportunities in Responsible and Equitable AI under the U.S. NSF and the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO).<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": "13020", "attributes": { "award_id": "2153380", "title": "CRII: RI: A Deep Gameplay Framework for Strong Story Experience Management", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Robust Intelligence" ], "program_reference_codes": [], "program_officials": [ { "id": 2534, "first_name": "Roger", "last_name": "Mailler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-01", "end_date": null, "award_amount": 174894, "principal_investigator": { "id": 28555, "first_name": "Joseph", "last_name": "Robertson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 203, "ror": "", "name": "Kennesaw State University Research and Service Foundation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>Experience managers are intelligent agents that produce personalized stories that change based on decisions a player makes in a digital game. These agents create new and powerful types of interactive stories for art and entertainment, training applications, and personalized education. A central problem for experience managers is avoiding dead ends, which are situations where story structure is broken due to a choice by the player. This problem results in experiences with un-interesting stories, missed training sequences, and long spells without appropriate targeted educational content. This project will develop a novel experience management architecture to quickly navigate around dead end situations during real-time interaction with a human participant. The architecture is based on recent advances in deep reinforcement learning for general game playing. This experience management platform will enable new forms of real-time training and education applications.<br/><br/>This work addresses a fundamental gap in existing experience managers that do not adversarially plan against sequences of player actions that lead to dead end situations. The specific research objectives are to create a deep reinforcement learning-based gameplay agent platform that (1) builds state spaces described by an action language, (2) identifies dead end states without performing exhaustive search, (3) relaxes assumptions of zero-sum and symmetric gameplay, and (4) solves narrative planning problems by compiling specialized narrative reasoning into a standard action language domain description. If successful, this research will significantly improve the speed and control of experience management agents and provide a pipeline to controlling existing and future specialized interactive narrative formalisms. These improvements will allow control of larger and more immersive narrative, training, and pedagogical environments compared to current systems.<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": "15671", "attributes": { "award_id": "2517733", "title": "Collaborative Research: RI: Medium: Transparent Fair Division of Indivisible Items", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Robust Intelligence" ], "program_reference_codes": [], "program_officials": [ { "id": 32177, "first_name": "Andy", "last_name": "Duan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 621874, "principal_investigator": { "id": 2616, "first_name": "Lirong", "last_name": "Xia", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 148, "ror": "https://ror.org/01rtyzb94", "name": "Rensselaer Polytechnic Institute", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 218, "ror": "", "name": "Rutgers University New Brunswick", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "Fair division deals with the distribution of resources and tasks among different parties, e.g., individuals, firms, nations, or autonomous agents, with the goal of achieving fairness and economic efficiency. Fairness has increasingly become crucial in distributing precious and scarce medical equipment, and its absence has exacerbated healthcare issues during the COVID-19 global pandemic. A wide variety of real-world applications such as scheduling, dispute resolution, healthcare management, and refugee settlement assume complete knowledge about allocation decisions, which gives rise to negative computational and impossibility results. The existing approaches to mitigate these challenges, in turn, impose a high cost on transparency. The broad goal of this project is to provide theoretical and algorithmic solutions for fair allocation of indivisible items in practical, large-scale settings, as a broad contribution to the grand scheme of artificial intelligence (AI) and economics for social good. This research will offer a novel and promising perspective for developing practical and transparent fair solutions while providing a systematic investigation on the perceived fairness of allocation mechanisms that are applicable to societies at large. This project will integrate and develop algorithmic solutions for transparent fair division in a publicly available software system with the goal of extending its reach--and in general promoting fairness and transparency--to a broad national and international audience. This project will develop a new framework for achieving fairness and efficiency in the allocation of indivisible resources with minimum cost on transparency. Specifically, it will make progress in four interconnected dimensions: 1) Tradeoffs between transparency, fairness, and efficiency, that aim at analyzing the compatibility of the properties and devising algorithmic solutions when allocating indivisible items, 2) Strategic aspects of fair division, that investigates agents' behavior and strategies under transparency requirements, 3) Domain restriction, that focuses on developing tractable solutions by circumventing the impossibility results in achieving compatible solutions, and 4) Bads and mixtures, that extend the transparency and fairness framework to include desirable (goods) and undesirable items (bads). Furthermore, this research plans to close the current gap between theoretical foundations of fairness and the perception of fairness through a series of comprehensive empirical evaluations. 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": "13328", "attributes": { "award_id": "2143706", "title": "CAREER: End-to-end Constrained Optimization Learning", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Robust Intelligence" ], "program_reference_codes": [], "program_officials": [ { "id": 2534, "first_name": "Roger", "last_name": "Mailler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-15", "end_date": null, "award_amount": 515403, "principal_investigator": { "id": 29411, "first_name": "Ferdinando", "last_name": "Fioretto", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 579, "ror": "https://ror.org/025r5qe02", "name": "Syracuse University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>Constrained optimization is used daily in our society with applications ranging from supply chains and logistics to electricity grids, organ exchanges, marketing campaigns, and manufacturing. Although these problems are often computationally challenging even for medium-sized instances, they constitute fundamental building blocks for the optimization of many industrial processes with profound effects on our society and economy. Yet the complexity of many constrained optimization problems often prevents them from being effectively adopted in contexts where many instances must be solved over a long-term horizon or when solutions must be produced under stringent time constraints. This project proposes a new paradigm that tightly integrates fundamental optimization techniques with machine learning algorithms to solve constraint optimization problems in real-time. This research holds the promise to create a new and transformative generation of optimization tools that solve hard constraint optimization problems under stringent time constraints leading to significant economic and societal benefits. <br/><br/>From a scientific standpoint, this project will develop a new integration of optimization and machine learning tools that deliver high-quality solutions to large-scale hard constraint optimization problems at unprecedented computational speeds. The proposed end-to-end Constraint Optimization Learning (e2e-COL) contributes to new scientific knowledge along three main directions: (1) It accommodates the presence of domain knowledge or complex problem constraints by combining fundamental methodologies from optimization into the training cycle of deep neural networks. (2) It addresses the need of generating large datasets to train high-quality models by devising efficient data generation procedures, linking methodologies from optimization with the model learning ability, and developing semi-supervised models requiring small amounts of labeled data. (3) Finally, to scale to large problem instances, this proposal enables e2e-COL to learn decompositions and approximations of the problem structure.<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": "12635", "attributes": { "award_id": "2303650", "title": "CRII: RI: A Deep Gameplay Framework for Strong Story Experience Management", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Robust Intelligence" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28555, "first_name": "Joseph", "last_name": "Robertson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 510, "ror": "", "name": "Rochester Institute of Tech", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Experience managers are intelligent agents that produce personalized stories that change based on decisions a player makes in a digital game. These agents create new and powerful types of interactive stories for art and entertainment, training applications, and personalized education. A central problem for experience managers is avoiding dead ends, which are situations where story structure is broken due to a choice by the player. This problem results in experiences with un-interesting stories, missed training sequences, and long spells without appropriate targeted educational content. This project will develop a novel experience management architecture to quickly navigate around dead end situations during real-time interaction with a human participant. The architecture is based on recent advances in deep reinforcement learning for general game playing. This experience management platform will enable new forms of real-time training and education applications.This work addresses a fundamental gap in existing experience managers that do not adversarially plan against sequences of player actions that lead to dead end situations. The specific research objectives are to create a deep reinforcement learning-based gameplay agent platform that (1) builds state spaces described by an action language, (2) identifies dead end states without performing exhaustive search, (3) relaxes assumptions of zero-sum and symmetric gameplay, and (4) solves narrative planning problems by compiling specialized narrative reasoning into a standard action language domain description. If successful, this research will significantly improve the speed and control of experience management agents and provide a pipeline to controlling existing and future specialized interactive narrative formalisms. These improvements will allow control of larger and more immersive narrative, training, and pedagogical environments compared to current systems.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": "3657", "attributes": { "award_id": "1731202", "title": "The 9th USA-China Chemical Engineering Conference, Beijing, China, October 15-19, 2017", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Proc Sys, Reac Eng & Mol Therm" ], "program_reference_codes": [], "program_officials": [], "start_date": "2017-04-15", "end_date": "2018-03-31", "award_amount": 35000, "principal_investigator": { "id": 11914, "first_name": "Ralph", "last_name": "Yang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 11913, "first_name": "Norman N", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "This grant is to provide travel support for US-based researchers to attend The 9th Sino-USA Joint Conference of Chemical Engineering (SUCE 2017), to be held in Beijing, China, on October 15 - 19, 2017. The theme of the conference is Green Chemical Engineering and a special emphasis will be placed on Chemical Engineering yielding environmental benefits. The organizing committee, which consists of distinguished researchers with a broad range of skills in chemical engineering, will provide for a vibrant and diverse atmosphere that will foster exchange of ideas on research and education. It is anticipated that approximately 35 US-based researchers will receive partial travel support to attend this conference.\n\nGlobal challenges related to depletion of nonrenewable energy resources, climate change, environmental pollution, and population growth are creating a need for the development of green and sustainable manufacturing technologies. The three-and-half-day technical conference will feature invited plenary and keynote lectures, oral presentations, two plenary discussion panels, one forum and a poster session. The conference will emphasize not only the role of chemical engineers in the development of sustainable chemical and energy processes, but will also explore the frontiers of Chemical Engineering research and the fundamental principles that are needed to tackle these challenges. The US-based conference participants supported by this grant will have the opportunity to network with leading researchers from China and exchange ideas about the current status and future directions in research and education. They will benefit from state-of-the-art reviews and presentations by leading experts and by debating controversial points with their peers. The meeting will also provide an opportunity for interaction and cooperation among industrial and academic researchers. The organizers plan to give special consideration to young or junior faculty members from U.S. Universities. Female and under-represented minorities will also receive special consideration. The conference will address global challenges with potentially significant benefits to society.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 4, "pages": 1397, "count": 13961 } } }