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、用可再生能源为世界提供动力、让人们过上更好的生活——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复杂性,只有先进的大规模计算才能提供这种大数据处理和分析。这次 NSF 计算远征推出了 CompSustNet (http://www.compsust.net),这是一个由美国公认的大学计算机科学项目提供支持的庞大研究网络,负责应用计算可持续性的新兴领域来解决世界上看似无法解决的资源问题。简而言之,该项目将招募计算、社会科学、自然保护、物理学、材料科学和工程学领域的一些顶尖人才,以解锁可持续的解决方案,保护我们地球的未来。计算可持续性的核心是相信:借助先进的计算技术,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,同时造福子孙后代。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,计算可持续性假设计算机设计的解决方案可以应用于世界上困难和具有挑战性的问题 - 从帮助非洲农民和牧民度过严重干旱到开发智能电力电网完全由可再生能源驱动。 CompSustNet 是一个大型的国内和国际多机构研究网络,由康奈尔大学领导,包括其他 11 个美国学术机构:鲍登学院、加州理工学院、卡内基梅隆大学、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、麻省大学、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 不仅仅是一个学术企业,它还包括专门从事保护、减贫和可再生能源领域的主要政府和非政府组织,例如大自然保护协会、世界自然基金会、国际畜牧研究所、跨非洲水文气象观测站和国家标准与技术研究所。CompSustNet 的核心使命是显着拓展计算可持续性的视野,并促进最先进的计算机科学的进步,以实现达到解决全球问题的规模。研究将重点关注应用于可持续发展挑战的交叉计算主题,例如优化、动态模型、模拟、大数据、机器学习和公民科学。 例如,计算可持续性正在被用来解决为干旱时期从北极迁徙到加利福尼亚州的滨鸟提供湿地的问题。随着加州变得越来越干燥,鸻鹬鸟无处可停、休息,也无法靠吃湿地无脊椎动物来补充能量。科学家们正在开发新的动态精确保护技术,该技术使用复杂的大数据模型来解决美国宇航局卫星图像、气象预报和公民科学的问题,这些问题以康奈尔大学鸟类学实验室的电子鸟类清单应用程序中数千个鸟类位置目击事件的形式进行。观鸟者。通过与大自然保护协会的合作,该计划预测鸻鹬类鸟类需要湿地栖息地的时间和地点,保护协会向中央谷稻农支付费用,让他们在适当的时候淹没他们的田地——在极端干旱的时候为鸟类和农民带来好处。让双方的生活都变得艰难。以类似的方式,计算可持续性项目也将努力创新自动监测网络,以保护濒临灭绝的大象种群免受偷猎者的侵害,促进发现从太阳光中获取能量的新方法,并设计算法来管理可再生能源的生成和存储在电网中。计算可持续性的进步将带来新颖、低成本、高效率的战略,以拯救濒临灭绝的物种,帮助土著人民改善他们的生活方式,并扩大可再生能源以满足21世纪的能源需求。 CompSustNet 就像种子、风险投资,帮助计算可持续性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
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
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
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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