Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1393&sort=-keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=-keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1394&sort=-keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-keywords" }, "data": [ { "type": "Grant", "id": "9956", "attributes": { "award_id": "2153607", "title": "Collaborative Research: CIF: Small: Interpretable Fair Machine Learning: Frameworks, Robustness, and Scalable Algorithms", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Comm & Information Foundations" ], "program_reference_codes": [], "program_officials": [ { "id": 1867, "first_name": "Phillip", "last_name": "Regalia", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-15", "end_date": "2025-01-31", "award_amount": 249996, "principal_investigator": { "id": 25753, "first_name": "Weijun", "last_name": "Xie", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Machine-learning algorithms are revolutionizing modern decision-making processes, from deciding job offers, evaluating loans, and determining university enrollments to proposing medical interventions. However, despite the recent success of machine-learning algorithms in solving large-scale problems, serious concerns have been raised that they are not entirely objective and can inadvertently amplify human biases. The proposed research project addresses this fundamental shortcoming by developing scalable data-driven methods and algorithms that generate interpretable policies aiming for provable fairness guarantees. The project will inform the policy-makers or decision-makers about possible outcomes and tradeoffs between machine learning outcomes and social equity/fairness. Furthermore, the research results will provide guidelines to support policies as well as regulations to promote diversity and fairness in many relevant domains of application.\n \nThe proposed research leverages recent advances in discrete and robust optimization, aiming for solution methodologies that faithfully address the exact learning models with fairness measures, provide strong out-of-sample fairness guarantees, are robust against bias and noisy outliers in the dataset, and can be solved efficiently for large-scale problem instances. More specifically, the proposed research aims to develop effective new frameworks for fair learning via sub-data selection that can leverage past efforts and enhance the fairness in the learning outcomes. Robust solution schemes will be carefully designed to significantly mitigate the severe overfitting effects of empirical-based methods and improve out-of-sample performance. Efforts will also be devoted to addressing algorithmic fairness in multi-stage decision-making and resource-allocation problems.\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": "9957", "attributes": { "award_id": "2211126", "title": "Conference: Support for Students to Attend 2022 APS-DAMOP Conference", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "AMO Experiment/Atomic, Molecul" ], "program_reference_codes": [], "program_officials": [ { "id": 6612, "first_name": "Kevin", "last_name": "Jones", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-01", "end_date": "2023-02-28", "award_amount": 12000, "principal_investigator": { "id": 25754, "first_name": "Subhadeep", "last_name": "Gupta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award provides support for student participation in the 2022 meeting of the American Physical Society's Division of Atomic, Molecular and Optical Physics, to be held May 30 – June 3 in Orlando FL, pandemic conditions permitting. The award supports travel expenses for approximately 20 graduate students. The conference program includes many of the research topics central to theoretical and experimental Atomic, Molecular, and Optical Physics and Quantum Information Science. The support of students through this award makes a substantial contribution to the education and training of future scientists. Students who graduate with a background in Atomic, Molecular, and Optical physics acquire a broad range of knowledge and skills that enable them to contribute to progress in many areas of science and technology. \n\nThe conference includes a special “Graduate Student Symposium” on the day prior to the main meeting. The symposium provides students with a pedagogical introduction to important themes in current research. The subject for 2022 will be “Textbook AMO platforms for Fundamental Science and Technology.” In addition to the symposium, the conference offers an opportunity for students to present their research results and to interact with more senior scientists from the United States and abroad. Travel support is provided only for students enrolled in US universities.\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": "9958", "attributes": { "award_id": "2131963", "title": "Physics of virus assembly and disassembly: Energetics and dynamics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "CONDENSED MATTER & MAT THEORY" ], "program_reference_codes": [], "program_officials": [ { "id": 1053, "first_name": "Daryl", "last_name": "Hess", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2025-01-31", "award_amount": 364000, "principal_investigator": { "id": 3324, "first_name": "Roya", "last_name": "Zandi", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "NONTECHNICAL SUMMARY\n\nThis award supports theoretical and computational research, and education to advance understanding of the factors contributing to the assembly and disassembly of virus particles. Viruses infect all kinds of hosts causing serious economic and health concerns worldwide. A critical step in the “life” cycle of most viruses, whether infecting bacteria, plants or animals, involves the formation of a protein shell, called the capsid that encloses the genome molecules (RNA or DNA). Viruses have not only optimized the feat of encapsulating their genetic material, but many of them have also evolved to efficiently disassemble and release their genetic materials upon entry into a host cell. \n \nDue to advances in experimental techniques at the nanoscale, the number of experiments investigating the physical basis of self-assembly and disassembly of viral particles is soaring. However, the current theoretical understanding of virus formation is incomplete. Improving this theory may guide the design of novel antiviral drugs based on direct interference of the virus assembly and/or disassembly. \n \nThis award supports research aimed at making progress towards such a theory. In particular, the PI aims to develop theoretical and computational models for several new insightful experiments, which have raised basic questions relating to the formation and disassembly of virus particles. The models will describe how virus coat proteins assemble around their genetic material to form a stable shell and under what conditions a virus falls apart and release its genetic material. The results of the theoretical modeling and computer simulations will be assessed by testing model predictions against data from experiments. In advancing understanding of the formation and disassembly of viruses, this project contributes more generally to understanding the process of self-assembly which shapes much of the biomolecular world as well as biomaterials and polymer-based materials. \n \nThis research on the assembly and disassembly of viruses is at the interface of condensed matter physics and biology and thus can have applications in other fields such as nanotechnology, drug delivery, and gene therapy. It can also play an important role in the development of alternative antiviral strategies based on direct interference of the capsid assembly and/or disassembly, which belong to the important areas for future studies. The research also contributes to the training of students interested in working on a new area of physical virology in a multidisciplinary field. The work of the PI will have an impact on the education of high school, undergraduate and graduate students, and in general to the training of the next generation of biological and soft condensed matter physicists.\n\n\nTECHNICAL SUMMARY\n\nThis award supports theoretical and computational research and education to advance understanding of the factors contributing to the assembly and disassembly of virus capsids. The current pandemic shows more than ever the urgency for learning about viruses at every level. A crucial step in the “life” cycle of most viruses, whether infecting bacteria, plants or animals, involves the formation of a protein shell, called the capsid that encloses the genome molecules (RNA or DNA). To fight viruses effectively, a comprehensive understanding of the critical steps and components of viral assembly or disassembly is essential in order to disrupt their formation. Despite a huge body of work dedicated to viruses, the knowledge about the formation of viruses and the means to combat them is still rudimentary.\n\nSeveral new insightful experiments have raised basic questions relating to the role of RNA in virus assembly and disassembly. The PI aims to address these questions by developing new theories and simulations. This project is aimed to address three major objectives: For objective one, the team will investigate several recent intriguing experiments corresponding to the assembly and disassembly of empty capsids built from some mutant capsid proteins. The goal of the second objective is to study the role of the genome in the kinetic pathways of virus assembly and disassembly and in defining the symmetry of viral shells with particular attention to several newly published and ongoing experiments. It appears that nucleic acid not only changes the size of the capsid but also has an impact on its symmetry. The effect of the genome on the kinetics pathways of assembly and disassembly of viral shells will also be investigated. The team will explore how it is possible for assembly or disassembly to occur spontaneously in one instance, and not in the other. \n\nFinally, disassembly also plays an important role in the formation of infectious human immunodeficiency viruses (HIV). For objective three, the team will explore how the disassembly of immature spherical HIV particles proceeds and is coupled to the formation of mature conical shells. The PI will extend the elasticity theory developed for spherical shells to explore the interactions between pentagonal defects as a spherical capsid grows, to conical and cylindrical shells. The goal is to decipher the factors that control the rate of transformation of a spherical shell to conical and cylindrical shells and find what physical considerations define the kinetics of this transformation.\n\nThe fact that under many in vitro conditions single-stranded RNA viruses can spontaneously self-assemble by simply mixing their genome and capsid proteins, and by contrast, the change in pH or other thermodynamic parameters can result in the spontaneous disassembly of an otherwise stable virus, makes it possible to investigate the physical basis of virus assembly and disassembly in terms of the general principles of statistical mechanics and condensed matter physics. This project is aimed to extend modern methods of the statistical theory of soft matter such as elastic shells with topological defects, charged polymers of complex topologies, and supramolecular complexes to the emerging problems in physics of assembly and disassembly. \n\nThis research on the assembly and disassembly of viruses is at the interface of condensed matter physics and biology and thus can have applications in other fields such as nanotechnology, drug delivery, and gene therapy. It can also play an important role in the development of alternative antiviral strategies based on direct interference of the capsid assembly and/or disassembly, which belong to the important areas for future studies. The research also contributes to the training of students interested in working on a new area of physical virology in a multidisciplinary field. The work of the PI will have an impact on the education of high school, undergraduate and graduate students, and in general to the training of the next generation of biological and soft condensed matter physicists.\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": "9959", "attributes": { "award_id": "2205648", "title": "2022 Frontiers of Engineering Symposia (US FOE, China-America FOE, and EU-US FOE)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EFRI Research Projects" ], "program_reference_codes": [], "program_officials": [ { "id": 6682, "first_name": "Louise R.", "last_name": "Howe", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-01", "end_date": "2023-02-28", "award_amount": 110000, "principal_investigator": { "id": 6683, "first_name": "Janet", "last_name": "Hunziker", "orcid": null, "emails": "", "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": "The Frontiers of Engineering (FOE) Symposia, organized by the National Academy of Engineering, provide emerging engineering leaders from academia, industry, and government laboratories the opportunity to share research insights to facilitate an interdisciplinary transfer of knowledge and research methodologies. The nature of emerging technologies and how they fit together to create products and processes that drive economic growth makes it essential that engineers understand research developments, techniques and approaches beyond their own discipline. The FOE symposia serve as a catalyst to spur engineering innovation and foster collaborative networks of researchers within the United States and between engineers from the United States and partnering countries. The FOE symposia are also expected to increase the visibility of engineering and to highlight the key roles through which engineers serve society.\n\nThis award will provide partial support for three Frontiers of Engineering (FOE) symposia, to be held in 2022. These symposia, organized by the National Academy of Engineering (NAE) in concert with international partner organizations, are for early career researchers. The meetings to be funded by this award in 2022 are: China-America FOE (CAFOE; July, 2022; Chengdu, China); US FOE (September, 2022; Seattle, Washington); and EU-US FOE (October, 2022; Bled, Slovenia). The 2022 CAFOE will focus on: Additive and Subtractive Manufacturing; Food Safety in the Context of Big Data and Genomics; Water Sustainability; Wearable Electronics and Human Health. Topics for the 2022 US FOE symposium are: Advances in Infectious Disease Diagnostics and Treatment; The Hydrogen Economy; Technology and Racial Justice and Equity; Conversational AI. The EU-US meeting will cover: Post-Lithium Batteries; Prosthetics and AI; Supply Chain/Logistics; Sustainable Buildings. Approximately 100 outstanding young engineers for the U.S. symposium and 30 from each country for the bilateral symposia will participate. Each 2.5 day symposium will include plenary sessions, break-out and poster sessions, and opportunities for developing collaborative research partnerships. Sessions will consist of presentations on cutting-edge research, innovations, and emerging research opportunities in engineering fields relevant to the symposium. As the scheduled dates for each symposium approaches, pandemic status assessments will be conducted to determine whether to hold each meeting in-person or convert to virtual or hybrid formats. FOE symposium papers, abstracts, presentation videos, and slides will be made available on the FOE website (www.naefrontiers.org).\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": "9960", "attributes": { "award_id": "2144310", "title": "CAREER: Transforming Biosensor Reliability using Sensor Time-series Data and Physics-based Machine Learning", "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": "2022-01-15", "end_date": "2026-12-31", "award_amount": 542197, "principal_investigator": { "id": 25755, "first_name": "Blake", "last_name": "Johnson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Access to reliable biosensors could transform public health by aiding ongoing and future pandemic management. However, biosensor reliability (e.g. false positive (type 1) and false negative(type 2) diagnoses) remains a barrier to widespread industrial and clinical use. Preliminary work performed in the Investigator’s lab suggests that using biosensor time series (TS) data and physics-based supervised Machine Learning (ML), a form of artificial intelligence that makes predictions from data, can reduce the probability of these errors. Thus, the research goal of this CAREER project is to examine the integration of machine learning and chemical engineering domain knowledge for improving biosensor reliability and performance. The proposed methodology will be applied across various sensor types, sizes, form factors, and data structures. If successful, access to reliable biosensors could catalyze biomanufacturing innovations and improve the speed and accuracy of current and emerging diagnostic methods. The education goal of this project is to create an interactive Open Course Ware (OCW) platform to increase education and workforce development opportunities at the interface of healthcare and data sciences for urban-underserved students. Planned activities include Gaming-driven Simulations in Biosensing for High School Students, a Virtual Lecture and Workshop on Data Archiving for Sensor Machine Learning for Undergraduate Students and Virtual Lectures on Emerging Applications of Machine Learning in the Bioanalytical, Life, and Materials Sciences for High School and Undergraduate Students. \n\nThe investigator’s overarching career goal is to help transform biosensor performance through concepts in data-driven chemical engineering and expand the leadership of underrepresented groups in emerging data-driven life sciences industries. In keeping with this goal, the objective of this project is to transform the reliability of biosensors through the integration of physiochemical process modeling and supervised ML. The central approach is to integrate supervised machine learning and mass transfer-limited surface binding reaction theory for improving the reliability of bioanalyte quantification via biosensor time-series data. This project will test the hypothesis that integrating experimental parameters and mass transfer-limited surface binding reaction theory with supervised machine learning models for target analyte classification can reduce the extent of type 1 and 2 errors relative to state-of-the-art calibration methods. The proposed methodology will be applied to reliable biosensor-based detection of RNA, microRNA, and protein targets and benchmarked against standard clinical bioanalytical methods. This work will identify new data- and model-driven features of target binding, nonspecific binding, and biosensor drift in biosensor time-series data that can support the reliable classification of bioanalyte concentration using machine learning. If successful, identifying features of target binding and interfering inputs in biosensor time-series data could significantly improve the reliability and reproducibility of biosensors and biosensor-based controls.\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": "9961", "attributes": { "award_id": "2145728", "title": "CAREER: 3DForests: Using Terrestrial Laser Scanning To Explore Forest Structure Changes Following Disturbance", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Ecosystem Science" ], "program_reference_codes": [], "program_officials": [ { "id": 7676, "first_name": "Justin", "last_name": "Lawrence", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-06-01", "end_date": "2027-05-31", "award_amount": 1100215, "principal_investigator": { "id": 25756, "first_name": "Lisa", "last_name": "Patrick Bentley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 725, "ror": "https://ror.org/04wjxkk25", "name": "Sonoma State University", "address": "", "city": "", "state": "CA", "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).\n\nWhile fire is an important ecological process in the western United States, wildfire has increased over recent decades as a result of climate change, historical fire suppression, and lack of adequate fuels management. Due to the urgency to protect life and property, this work will use state-of-the-art technology (ground-based lasers) to collect information about forests that will help inform and support both forest health management and active fire operations in California. In addition, research findings related to wildfire will be used to create virtual reality-based activities that will be used to improve retention and graduation rates of underrepresented minorities in STEM majors at a primarily undergraduate, Hispanic Serving Institution. These novel data-driven and accessible educational products will also be presented at a local nature preserve and for 4th-6th graders at a Spanish-immersion school to allow broad audiences to better understand interactions between forests and wildfire.\n\nThe goals of this project are to: 1) Use terrestrial laser scanning (TLS) and handheld mobile laser scanning to estimate understory forest structure parameters, determine impacts of forest structure on past wildfire intensity, and predict how altered forest structure affects future wildfire risk; and 2) Link temporal changes in net primary productivity to disturbance via a scaling model parameterized with TLS-collected biomass, measured leaf traits and climate data. This work also aims to use research findings related to wildfire to expand opportunities for a more diverse group of students, many of whom are often underrepresented minorities or first-generation college students. This will be accomplished through: 1) Mentoring underrepresented minority undergraduate and graduate students to create a virtual \"field trip\" focused on forest structure for an ecology course-based undergraduate research experience and NSF-funded \"The Virtual Field\" platform; and 2) Adapting the “field trip” for public outreach at a local nature preserve and for 4th-6th graders at a Spanish-immersion school using virtual reality (VR). Data collected will enable crucial characterization of ecosystem changes post-wildfire and assessment of forest sensitivity to cumulative disturbances and advance the predictability of ecological science. In addition, VR activities will broaden participation in field learning and expand opportunities for a more diverse group of students, especially during pandemic times.\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": "9962", "attributes": { "award_id": "2144556", "title": "CAREER: Multiscale Photodynamics Simulations in Solvated and Crystalline Environments", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "Chem Struct,Dynmcs&Mechansms B" ], "program_reference_codes": [], "program_officials": [ { "id": 2183, "first_name": "Tingyu", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-06-01", "end_date": "2027-05-31", "award_amount": 656008, "principal_investigator": { "id": 25757, "first_name": "Steven", "last_name": "Lopez", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "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).\n\nWith support from the Chemical Structure, Dynamics & Mechanisms-B Program of the Chemistry Division, Steven A. Lopez of Northeastern University is using computational techniques to discover new light-promoted (photochemical) reactions. Photochemical reactions are attractive to several research sectors because they avoid the need for extensive heating and expensive catalysts to access complex, high-energy molecules. Photochemical reactions occur on very fast timescales, typically less than one-millionth of a second, making observation of intermediate structures with experiments very challenging. This project aims to use computational and machine learning techniques to understand the reactivities and selectivities of these photochemical reactions. Research in the Lopez group will enable computational predictions in realistic, complex chemical environments (e.g., solvent and crystalline phase) towardsmore accurate predictions and design principles. This work is at the intersection of data science, organic, and physical chemistry and will support the interdisciplinary training of young scientists at all levels. Dr. Lopez and his team will create \"pandemic-proof\" Summer Research Experiences for community college students across the United States and engage with the Alliance for Diversity in Science and Engineering to parallelize the outreach impact. \n\nSteven A. Lopez and his research group plan to apply and develop computational and machine learning techniques to predict photochemical reaction outcomes, mechanisms, and stereoselectivities in complex environments (e.g., molecular solids and solvated systems). Unlike thermal reactions, structure-property relationships are more complex and difficult to understand for photochemical reactions. The general lack of excited-state structural information has limited structure-reactivity relationships and slowed the discovery of high-yielding, selective reactions. Experimental and computational techniques cannot resolve dynamic excited-state structures of short-lived molecular excited states (nano- to femtosecond scale). This project will enable the comprehensive exploration of the reactivities and stereoselectivities of gas-evolving reactions with multiconfigurational quantum chemical calculations and machine-learning-accelerated non-adiabatic molecular dynamics simulations. The research group will focus on parent and substituted triazolines, pyrazolines, and diazirines. This project aims to resolve the mechanisms and structures of molecular excited states, thusly targeting a knowledge gap towards structure-reactivity relationships. A second phase will evaluate the role of the chemical environment on the excited- and ground-state components of these reactions, enabled by an open-access machine learning code Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD). The anticipated mechanistic insights have the potential to enable future design of light-responsive frameworks (e.g. covalent organic frameworks and metal-organic frameworks, COFs and MOFs) and molecular machines in the longer term.\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": "9963", "attributes": { "award_id": "2207028", "title": "Conference: Ecology and Evolution of Infectious Diseases 2022: Pandemics, Social Justice and Science Communication", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Ecology of Infectious Diseases" ], "program_reference_codes": [], "program_officials": [ { "id": 599, "first_name": "Samuel", "last_name": "Scheiner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-01", "end_date": "2023-02-28", "award_amount": 43495, "principal_investigator": { "id": 25758, "first_name": "Jacobus", "last_name": "de Roode", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 4908, "first_name": "Micaela E", "last_name": "Martinez", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 265, "ror": "https://ror.org/03czfpz43", "name": "Emory University", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "The 2022 Ecology and Evolution of Infectious Diseases meeting will be held at Emory University, 6-9 June 2022. The meeting will be held in hybrid format (in-person and online) and will provide a workshop for graduate students and early-career postdocs on Pandemic Scenario Modeling and Science Communication. In this workshop, participants will learn the latest applications of models to study outbreak scenarios, and further incorporate economics and host behavior into these models to determine the impacts of social aspects on infectious disease transmission. Moreover, trainees will obtain training in effective science communication. Conference sessions will include these same themes, and additionally address the importance of Social Justice and Infectious Disease. A final session will focus on Infectious Diseases Across Scales, with a focus on understanding how interactions between biological, spatial and\ntemporal scales affect transmission of pathogens and parasites of a wide diversity of hosts, including wildlife, livestock and humans. The meeting will bring together scientists from around the world to discuss the latest research on pressing issues, including the role of climate change in driving infectious disease, and the importance of racial and other social disparities in causing inequity and preventing effective control of disease. The explicit focus on social justice and infectious disease will showcase the crucial integration of biomedical science, social science, public health and ecology.\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": "9964", "attributes": { "award_id": "2138896", "title": "ERI: Tool Grasping Compliance and Stability of Underactuated Hands in Model-Mediated Telemanipulation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "FRR-Foundationl Rsrch Robotics" ], "program_reference_codes": [], "program_officials": [ { "id": 8531, "first_name": "Siddiq", "last_name": "Qidwai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-06-01", "end_date": "2024-05-31", "award_amount": 199995, "principal_investigator": { "id": 25759, "first_name": "Long", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1113, "ror": "https://ror.org/02z43xh36", "name": "Stevens Institute of Technology", "address": "", "city": "", "state": "NJ", "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).\n\nThis Engineering Research Initiation (ERI) grant supports research that will contribute new knowledge related to a manufacturing process, promoting the progress of science, and advancing national prosperity. Robot manipulators are known for high precision and repeatability. They have been widely used in well-defined and fully automated industrial settings. However, ad-hoc, highly uncertain, and complex manipulation tasks still require on-site presence and assistance from human crew. Many such tasks present hazardous and challenging environments, such as in the case of factory operations during a pandemic. This project aims to empower the workforce by enabling their versatile and dexterous capabilities for remotely operated manipulation tasks. A novel model-mediated telemanipulation framework will be developed for the workforce, to extend their physical reach, their hands-on manipulation dexterity, and their situational awareness intelligence, from local to a remote site. These advanced telemanipulation technologies will have substantial impact on society and the economy, helping facilitate safe manufacturing working environments for the workforce. From an educational perspective, this project promotes diversity in engineering education by inspiring and preparing students in under-served communities for science, technology, engineering, and mathematics (STEM) careers at the high school and college levels.\n\nThis project seeks to develop enhanced teleoperation methods to bridge the current gaps of using grasped tools in remotely operated manipulation tasks, such as, fastening a screw with a grasped screwdriver. The key research objective of this project is to investigate tool grasping compliance and stability in force-controlled manufacturing tasks and explore its usage in model-mediated telemanipulation. First, a framework is developed for estimating tool grasping compliance matrices of underactuated robotic hands using combined feedback from hand sensors (joint angles and proprioception) and wrist-mounted force and torque sensors. Then, the estimated compliance is used to infer grasping status and stability and to develop novel force control strategies. Finally, the tool grasping information is incorporated in model-mediated telemanipulation to design and implement a novel grasping-informed virtual fixture haptic assistance. Thereby, a complete solution for user-driven remotely operated manipulation tasks can be delivered.\n\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).\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": "9965", "attributes": { "award_id": "2153249", "title": "CRII: HCC: 3D Hand & Full-Body Pose Estimation in Telehealth for Children with Autism", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "HCC-Human-Centered Computing" ], "program_reference_codes": [], "program_officials": [ { "id": 1801, "first_name": "William", "last_name": "Bainbridge", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-06-01", "end_date": "2024-05-31", "award_amount": 174368, "principal_investigator": { "id": 25760, "first_name": "Kevin", "last_name": "Desai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "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": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). \n\nThe overall objective of this project is to provide efficient full-body interaction in virtual reality systems that do not use head-mounted displays. This project aims to create accurate and real-time 3D hand and body pose estimation, in the highly significant application area of children with autism. A novel synthetic hand data generation framework will generate 3D hand poses with increased diversity in terms of hand distance from camera, hand size, camera viewpoint, occlusion, background, and skin color. The outcome will be a novel 3D synthetic hand dataset consisting of realistic and kinematically accurate hand models with articulated poses that will advance current and future research endeavors in 3D hand pose estimation research. The project will advance the state-of-the-art in 3D body pose estimation for humans present further away from the camera at room-scale distances. The synthetic dataset, algorithms, and programming libraries will be made publicly available for wide-spread adoption, thereby advancing pose estimation research.\n\nThis research will have broad societal impact because it will improve the usability and interaction in human centered telehealth applications, initially helping with the applied behavior analysis for children with autism. Existing systems that employ head-mounted displays or wearable sensors for tracking the user's hand and body movements are not suitable for children with autism, and have disadvantages in many other application areas. Therefore, by enabling 3D hand and full-body pose estimation, this project will advance a plethora of 3D immersive applications such as education, virtual STEM laboratories, tele-rehabilitation, tele-operation, military training, entertainment, and communication. The need for real-time, remote and interactive human motion sensing exists now more than ever, considering the increase in virtual activities because of the recent pandemic.\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 } } ], "meta": { "pagination": { "page": 1393, "pages": 1419, "count": 14184 } } }