课题基金 / 基金详情

HDR DSC: Data Science for Energy Transition

HDR DSC: Data Science for Energy Transition
HDR DSC:能源转型的数据科学
批准号:
2123247
负责人:
Mikyoung Jun
金额:
$149.3万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-10-01 至 2024-09-30

项目摘要

项目成果

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中文摘要
翻译
随着公众对气候变化的认识日益增强,世界能源之都休斯顿正在努力引领能源转型,以实现更可持续和更清洁的环境。迫切需要一支具备广泛理解能力的数据科学员工队伍,以优化传统能源并引领能源转型。该项目的重点是培训来自不同背景的本科生和硕士研究生,以获得对未来能源行业劳动力至关重要的广泛知识和技能。大休斯顿的五所主要公立大学与多个能源行业合作伙伴合作,从大型石油和天然气公司到能源行业数据分析方面的初创公司。鉴于参与大学强调多样性和公平,以及它们所服务的学生群体的多样性,该项目扩大了在科学、技术、工程和数学(STEM)领域历来代表性不足的群体的培训机会。休斯顿大学(UH)主校区、UH-Downtown、UH-Victoria、UH-Clear Lake和Sam Houston State University的统计学家、计算机科学家、地球物理学家和社会科学家正在合作开展为期一年的教育活动,主题是能源转型,从传统的石油和天然气相关问题转向可再生能源。课程包含一系列重要主题,包括对基础科学和工程的理解,统计学和机器学习方法的本质,以及编程和数据可视化的实用技能集。关于能源转型及其对社会的影响的社会科学方面也包括在课程中。该计划包括:(1)为期五周的夏令营,包括统计/机器学习、CS/编程、地球物理和地球科学、公共政策和工程等教育模块;(2)参与大学在接下来的秋季学期提供的高级课程和微证书培训;(3)为期一学期的团队研究项目,由行业合作伙伴在春季学期提供;以及(4)暑期实习和/或会议演示机会,以结束该计划。每年约有40名学生参加该项目,总计约120名学生参与,超过3个队列。开发了一个可以开展研究项目的框架。该项目还促进了该地区学术界和产业界之间的互动与合作,为教育和研究合作开辟了更多机会。所有项目数据都有很好的文件记录,并免费提供。学生和项目级别的成果经过正式评估,结果通过发表在同行评议的期刊和会议报告上发布。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
With the growing public awareness of climate change, Houston, the energy capital of the world, is undertaking efforts to lead the energy transition for a more sustainable and cleaner environment. There is urgent demand for a data science workforce equipped with broad understanding for optimizing conventional energy and leading the energy transition. This project focuses on training undergraduates and Masters-level students from diverse backgrounds to achieve a wide range of knowledge and skill sets essential for the future energy industry workforce. Five major public universities in greater Houston are teamed up with multiple energy industry partners, ranging from large oil and gas companies to startups on energy sector data analytics. Given the emphasis of participating universities on diversity and equity and the diverse student populations they serve, this project broadens training opportunities for groups historically underrepresented in science, technology, engineering, and mathematics (STEM).Statisticians, computer scientists, geophysicists, and social scientists at the University of Houston (UH) main campus, UH-Downtown, UH-Victoria, UH-Clear Lake, and Sam Houston State University are collaborating to develop year-long educational activities under the theme of energy transition, from traditional oil- and gas-related problems to renewable energy. The curriculum contains an array of important topics including fundamental science and engineering understanding, essence of statistics and machine learning methods, and practical skill sets of programming and data visualization. Social science aspects on the energy transition and its implications for society are also included in the curriculum. The program consists of: (1) five-week summer boot camps with educational modules on statistics/machine learning, CS/programming, geophysics and earth sciences, public policy, and engineering; (2) advanced courses and micro-credential training offered by participating universities in the following fall semester; (3) semester-long team research projects on problems provided by industry partners in the spring semester; and (4) summer internships and/or conference presentation opportunities to conclude the program. Each year, about 40 students participate in the program, totaling about 120 student participants over 3 cohorts. A framework in which research projects can be carried out is developed. The project also promotes interaction and collaboration between academia and industry in the region, opening up more opportunities for education and research collaboration. All project data is well documented and made freely available. The student and program-level outcomes are formally assessed, with results disseminated through publication in peer-reviewed journals and conference presentations.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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ATD: Global Terrorism Threat Detection by Nonstationary, Spatio-Temporal Hawkes Process Models
  • 批准号:
    2105847
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2020
  • 负责人:
    Mikyoung Jun
  • 依托单位:
ATD: Global Terrorism Threat Detection by Nonstationary, Spatio-Temporal Hawkes Process Models
  • 批准号:
    1925119
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2019
  • 负责人:
    Mikyoung Jun
  • 依托单位:
Spatio-temporal Point Process models on a global scale and their application to global lightning occurrences
  • 批准号:
    1613003
  • 项目类别:
    Standard Grant
  • 资助金额:
    $12.0万
  • 财政年份:
    2016
  • 负责人:
    Mikyoung Jun
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In-depth development and assessment of covariance models for multivariate nonstationary processes on a sphere
  • 批准号:
    1208421
  • 项目类别:
    Continuing Grant
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    $10.0万
  • 财政年份:
    2012
  • 负责人:
    Mikyoung Jun
  • 依托单位:
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DSC2功能缺失在原发性右心室扩张型心肌病的作用及机制研究
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  • 负责人:
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  • 项目类别:
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  • 资助金额:
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  • 批准年份:
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  • 负责人:
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