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NSF I-Corps Hub (Track 1): Mid-South Region

NSF I-Corps Hub (Track 1): Mid-South Region
NSF I-Corps 中心(轨道 1):中南部地区
批准号:
2229521
负责人:
Padma Raghavan
金额:
$1500.0万
依托单位:
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-01-01 至 2027-12-31
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中文摘要
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英文摘要
The broader impact/commercial potential of this NSF I-Corps Hubs project is the development of a sustainable, inclusive, innovation ecosystem that will impart shared economic prosperity across the US Mid-South region. The Mid-South Hub’s formative, evidence-driven approach may inform how inclusive innovation ecosystems can be established in regions with nascent economic activity. This knowledge will augment team preparation, maximizing the value extracted from I-Corps training. The Hub will further amplify the downstream successes and sustainability of deep technology ventures, including grant acquisition, fundraising, sustainable revenues, and net-positive liquidity events. Success will be leveraged to influence innovation culture at regional institutions, leading to the incentivization of enterprising faculty, students, and staff. The diverse leadership team and inclusive bench of instructors will increase the Hub’s ability to connect to innovators and mentors of all backgrounds, including those who are disadvantaged or underrepresented. These efforts will include building commercialization capacity at underrepresented institutions. Evidence from the deliberate implementation of a democratized organizational structure will be evaluated for its capability to encourage and preserve the cognitive proximity of ecosystem members. This knowledge will be disseminated via the National Innovation Network to drive translational impacts and economic development, shaping the future of American innovation.This I-Corps Hubs project is based on the development of a use-inspired incubator that uses data-driven approaches to develop best practices, influence economic policy, inspire adoption of inclusive approaches to innovation, and instruct future programmatic investments. Currently, a gap exists in understanding how regional innovation clusters can unify to drive the development of a prolific innovation ecosystem. Addressing this gap will enable policymakers and government agencies to implement evidence-based approaches to inform programmatic investments and maximize technology commercialization, economic development, and overall national innovation readiness. This consortium of diverse, deep technology-producing institutions from disparate locations within the Mid-South region will leverage the I-Corps program to catalyze technological commercialization, spur economic development, and inform the future of inclusive American innovation. The Hub will prioritize a formative, longitudinal assessment to iteratively optimize key activities, including team recruitment, Regional and National I-Corps training, upstream changes in university innovation culture, downstream impacts on successful commercialization, and an inclusive innovation corridor across the Mid-South. This effort will advance technology transfer from academic institutions into entrepreneurial ventures that seed emergent, regional ecosystems. The data-driven, performance improvement approach will ensure best practices are evidence-based and create a model for other regions seeking to induce inclusive innovation cluster development.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.
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Collaborative Research: SHF: Small: Learning Fault Tolerance at Scale
  • 批准号:
    2135309
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2022
  • 负责人:
    Padma Raghavan
  • 依托单位:
SHF: Small: Embedded Graph Software-Hardware Models and Maps for Scalable Sparse Computations
  • 批准号:
    1719674
  • 项目类别:
    Standard Grant
  • 资助金额:
    $20.65万
  • 财政年份:
    2016
  • 负责人:
    Padma Raghavan
  • 依托单位:
SHF: Small: Embedded Graph Software-Hardware Models and Maps for Scalable Sparse Computations
DC: Small: Adaptive Sparse Data Mining On Multicores
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