Collaborative Research: CompSustNet: Expanding the Horizons of Computational Sustainability

合作研究:CompSustNet:拓展计算可持续性的视野

基本信息

  • 批准号:
    1521687
  • 负责人:
  • 金额:
    $ 140万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2015
  • 资助国家:
    美国
  • 起止时间:
    2015-12-15 至 2021-11-30
  • 项目状态:
    已结题

项目摘要

Poverty, saving species, repowering the world with renewable energy, lifting people up to live better lives - there are no easy answers to guiding our planet on the path toward sustainability. Complex problems require sophisticated solutions. They involve intricacy beyond human capabilities, the kind of big-data processing and analysis that only advanced large-scale computing can provide. This NSF Expedition in Computing launches CompSustNet (http://www.compsust.net), a vast research network powered by the nation's recognized university computer science programs, charged with applying the emerging field of computational sustainability to solving the world's seemingly unsolvable resource problems. Put simply, the project will enlist some of the top talents in computing, social science, conservation, physics, materials science, and engineering to unlock sustainable solutions that safeguard our planet's future.Computational Sustainability is, at its core, the belief that with sufficiently advanced computational techniques, we can devise sustainable solutions that meet the environmental, societal, and economic needs of today while providing for future generations. In much the same way IBM's supercomputer Watson could defeat any challenger in Jeopardy!, computational sustainability posits that a computer-engineered solution can be applied to world's difficult and challenging problems - from helping farmers and herders in Africa survive severe droughts to developing a smart power grid fueled entirely by renewable energy. CompSustNet is a large national and international multi-institutional research network led by Cornell University and including 11 other US academic institutions: Bowdoin, Caltech, CMU, Georgia Tech, Howard University, Oregon State, Princeton, Stanford, UMass, University of South California, and Vanderbilt University, as well as collaborations with several international universities. But CompSustNet is not just an academic enterprise, as it also includes key governmental and non-governmental organizations that specialize in conservation, poverty mitigation, and renewable energy, such as The Nature Conservancy, The World Wildlife Fund, The International Livestock Research Institute, The Trans-African Hydro-Meteorological Observatory, and the National Institute of Standards and Technology.CompSustNet's core mission is to significantly expand the horizons of computational sustainability and foster the advancement of state-of-the-art computer science to achieve the scale to tackle global problems. Research will focus on cross-cutting computational topics such as optimization, dynamical models, simulation, big data, machine learning, and citizen science, applied to sustainability challenges. For example, computational sustainability is being put to work to resolve the problem of providing wetlands for shorebirds that migrate from the Arctic through California during a time of drought. As California gets drier, the shorebirds have nowhere to stop, rest, and refuel by eating wetland invertebrates. Scientists are developing new dynamic precision conservation techniques that use complex, big-data models to tackle the problem with NASA satellite imagery, meteorological forecasts, and citizen science in the form of thousands of bird location sightings from the Cornell Lab of Ornithology's eBird checklisting app for birdwatchers. Through partnership with The Nature Conservancy, the program forecasts when and where wetland habitat would be needed for shorebirds, and the Conservancy pays Central Valley rice farmers to flood their fields at opportune times - providing benefits for birds and farmers at a time when extreme drought is making life tough for both. In similar ways, computational sustainability projects will also be hard at work innovating automated monitoring networks to protect endangered elephant population from poachers, promoting the discovery of novel ways to harvest energy from sun light, and designing algorithms to manage the generation and storage of renewable energy in the power grid. Advancements in computational sustainability will lead to novel, low-cost, high-efficiency strategies for saving endangered species, helping indigenous peoples improve their way of life, and scaling renewables up to meet 21st century energy demand. CompSustNet is like the seed, the venture capital, to help the field of computational sustainability achieve what's possible.
贫困、拯救物种、用可再生能源重振世界、提升人们的生活水平--要引导我们的星球走上可持续发展的道路,没有简单的答案。复杂的问题需要复杂的解决方案。它们涉及超出人类能力的错综复杂的问题,即只有先进的大规模计算才能提供的那种大数据处理和分析。美国国家科学基金会的计算探险推出了CompSustNet(http://www.compsust.net),),这是一个庞大的研究网络,由美国公认的大学计算机科学项目提供支持,负责将新兴的计算可持续发展领域应用于解决世界上看似无法解决的资源问题。简而言之,该项目将招募一些在计算、社会科学、保护、物理学、材料科学和工程学方面的顶尖人才,以解开保障我们星球未来的可持续解决方案。计算可持续发展的核心是相信,通过足够先进的计算技术,我们可以设计出可持续的解决方案,满足当今的环境、社会和经济需求,同时为子孙后代提供服务。就像IBM的超级计算机沃森可以击败《危险边缘》中的任何挑战者一样,计算可持续发展假设计算机工程解决方案可以应用于世界上困难和具有挑战性的问题--从帮助非洲的农民和牧民度过严重的干旱,到开发完全由可再生能源供电的智能电网。CompSustNet是一个由康奈尔大学领导的大型国内和国际多机构研究网络,包括其他11个美国学术机构:鲍登大学、加州理工大学、CMU、佐治亚理工学院、霍华德大学、俄勒冈州立大学、普林斯顿大学、斯坦福大学、马萨诸塞州大学、南加州大学和范德比尔特大学,以及与几所国际大学的合作。但CompSustNet不仅仅是一家学术企业,因为它还包括主要的政府和非政府组织,专门从事保护、减轻贫困和可再生能源,如自然保护协会、世界野生动物基金会、国际牲畜研究所、泛非水文气象天文台和国家标准与技术研究所。CompSustNet的核心使命是显著扩大计算可持续发展的视野,促进最先进的计算机科学的进步,以达到解决全球问题的规模。研究将集中在应用于可持续发展挑战的交叉计算主题,如优化、动态模型、模拟、大数据、机器学习和公民科学。例如,正在利用计算可持续性来解决为干旱期间从北极经由加州迁徙的滨鸟提供湿地的问题。随着加州变得越来越干燥,滨鸟没有地方停下来休息,也没有地方吃湿地无脊椎动物来补充燃料。科学家们正在开发新的动态精度保护技术,使用复杂的大数据模型来解决NASA卫星图像、气象预报和公民科学的问题,这些模型来自康奈尔鸟类学实验室为观鸟者提供的eBird核对清单应用程序,其中包括数千次鸟类位置目击。通过与自然保护协会的合作,该项目预测了滨鸟需要湿地栖息地的时间和地点,并向中央山谷的稻农支付费用,让他们在适当的时候淹没自己的田地--在极端干旱使鸟类和农民的生活变得艰难的时候,为鸟类和农民提供好处。以类似的方式,计算可持续发展项目也将努力创新自动化监测网络,以保护濒危大象种群免受偷猎者的伤害,促进发现从阳光中获取能源的新方法,并设计算法来管理电网中可再生能源的产生和储存。计算可持续性的进步将导致拯救濒危物种的新的、低成本、高效率的战略,帮助土著人民改善他们的生活方式,并扩大可再生能源的规模,以满足21世纪的能源需求。CompSustNet就像种子,风险资本,帮助计算可持续发展领域实现可能的目标。

项目成果

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Thomas Dietterich其他文献

Thomas Dietterich的其他文献

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{{ truncateString('Thomas Dietterich', 18)}}的其他基金

III: Medium: Collaborative Research: Algorithms and Cyberinfrastructure for High-Precision Automated Quality Control of Hydro-Meteo Sensor Networks
III:媒介:合作研究:Hydro-Meteo 传感器网络高精度自动化质量控制的算法和网络基础设施
  • 批准号:
    1514550
  • 财政年份:
    2015
  • 资助金额:
    $ 140万
  • 项目类别:
    Continuing Grant
CyberSEES: Type 2: Computing and Visualizing Optimal Policies for Ecosystem Management
Cyber​​SEES:类型 2:计算和可视化生态系统管理的最佳策略
  • 批准号:
    1331932
  • 财政年份:
    2013
  • 资助金额:
    $ 140万
  • 项目类别:
    Standard Grant
Collaborative Research: AVATOL - Next Generation Phenomics for the Tree of Life
合作研究:AVATOL - 生命之树的下一代表型组学
  • 批准号:
    1208272
  • 财政年份:
    2012
  • 资助金额:
    $ 140万
  • 项目类别:
    Standard Grant
Collaborative Research: CDI-Type II: BirdCast: Novel Machine Learning Methods for Understanding Continent-Scale Bird Migration
合作研究:CDI-Type II:BirdCast:用于理解大陆规模鸟类迁徙的新型机器学习方法
  • 批准号:
    1125228
  • 财政年份:
    2011
  • 资助金额:
    $ 140万
  • 项目类别:
    Standard Grant
II-EN: A compute cluster and software tools for Monte-Carlo methods in artificial intelligence
II-EN:人工智能中蒙特卡罗方法的计算集群和软件工具
  • 批准号:
    0958482
  • 财政年份:
    2010
  • 资助金额:
    $ 140万
  • 项目类别:
    Standard Grant
Collaborative Research: Computational Sustainability: Computational Methods for a Sustainable Environment, Economy, and Society
合作研究:计算可持续性:可持续环境、经济和社会的计算方法
  • 批准号:
    0832804
  • 财政年份:
    2008
  • 资助金额:
    $ 140万
  • 项目类别:
    Continuing Grant
RI: Machine Learning for Robust Recognition of Invertebrate Specimens in Ecological Science
RI:机器学习在生态科学中对无脊椎动物标本的鲁棒识别
  • 批准号:
    0705765
  • 财政年份:
    2007
  • 资助金额:
    $ 140万
  • 项目类别:
    Standard Grant
SGER: Exploiting Contextual Knowledge to Design Input Representations for Machine Learning
SGER:利用上下文知识设计机器学习的输入表示
  • 批准号:
    0335525
  • 财政年份:
    2003
  • 资助金额:
    $ 140万
  • 项目类别:
    Standard Grant
Off-the-shelf Learning Algorithms for Structural Supervised Learning
用于结构监督学习的现成学习算法
  • 批准号:
    0307592
  • 财政年份:
    2003
  • 资助金额:
    $ 140万
  • 项目类别:
    Continuing Grant
Student Participant Support for the International Conference on Machine Learning 2003
2003 年国际机器学习会议的学生参与者支持
  • 批准号:
    0331758
  • 财政年份:
    2003
  • 资助金额:
    $ 140万
  • 项目类别:
    Standard Grant

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