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NRT-DESE: NRT in Integrated Computational Entomology (NICE)

NRT-DESE: NRT in Integrated Computational Entomology (NICE)
NRT-DESE:综合计算昆虫学 (NICE) 中的 NRT
批准号:
1631776
负责人:
Eamonn Keogh
金额:
$272.11万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-15 至 2022-08-31

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中文摘要
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英文摘要
This National Science Foundation Research Traineeship (NRT) award to the University of California, Riverside (UCR) will enable a team of investigators from Computer Science/Engineering and Entomology/Life Sciences to prepare the next generation of scientists and engineers to exploit the unreasonable effectiveness of data to understand insects by integrating the disciplines of computer science and entomological biology. The NRT in Integrated Computational Entomology (NICE) will train students to be at the forefront of science in computing for biological domains, providing biological scientists a foundation in computing techniques and engineers an understanding of critical entomological and ecological issues. The project anticipates training at least forty (40) MS and PhD students, including twenty (20) funded PhD trainees from the life sciences, computer science and engineering. The project will be the first program of its kind, anywhere in the world, and will meet high standards for innovation while offering a structure for demanding training in entomology/life sciences integrated with computational techniques in machine learning, data mining, and statistics. The NICE program recognizes and advances Computational Entomology as an emerging interdisciplinary field. Computational Entomology as a discipline recognizes that entomological and ecological problems generate enormous amounts of data, and that fully exploiting this data will require individuals whose knowledge spans two otherwise disparate fields. The training and research structure of the proposed project seeks to bridge large gaps in training, language, approach, perspective and knowledge that continue to divide the engineering/informatics and life sciences disciplines. Through coursework and joint projects with government agencies and companies, trainees will experience the translation of research outcomes into implemented public policy or agricultural/medical products and services. This project will scale to include graduate student trainees at UCR receiving NRT support and those not receiving funding, and will be sustainable at UCR as the new curriculum will become incorporated across the participating departments and degree programs. This project will also serve as a replicable Computational Entomology education and training model for other institutions. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative scalable models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.
期刊论文(16)
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科研奖励(0)
会议论文
A Fast Adaptive k-means with No Bounds
无界限的快速自适应 k 均值
DOI: 10.1109/tpami.2020.3008694
发表时间: 2020
期刊: IEEE Transactions on Pattern Analysis and Machine Intelligence
影响因子: 23.6
作者: [Shuyin Xia, Daowan Peng, Deyu Meng, Changqing Zhang, Guoyin Wang, Elisabeth Giem, Wei, Zizhong Chen]
通讯作者: Zizhong Chen
Worker task organization in incipient bumble bee nests
早期熊蜂巢中的工人任务组织
DOI: 10.1016/j.anbehav.2021.12.005
发表时间: 2022
期刊: Animal behaviour
影响因子: 2.5
作者: [Fisher, K, Sarro, E, Miranda, C, B Guillen, B, Woodard, SH.]
通讯作者: Woodard, SH.
Matrix profile xxiii: Contrast profile: A novel time series primitive that allows real world classification
矩阵配置文件 xxiii:对比度配置文件:一种新颖的时间序列基元,允许现实世界分类
DOI: 10.1109/icdm51629.2021.00151
发表时间: 2021
期刊: IEEE International Conference on Data Mining workshops
影响因子: --
作者: [Mercer, R, Alaee, S, Abdoli, A, Singh, S, Murillo, A, Keogh, E.]
通讯作者: Keogh, E.
MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives
MERLIN:海量时间序列档案中任意长度异常的无参数发现
DOI: 10.1109/icdm50108.2020.00147
发表时间: 2020
期刊: ICDM 2020
影响因子: --
作者: [Nakamura, Takaaki, Imamura, Makoto, Mercer, Ryan, Keogh, Eamonn]
通讯作者: Keogh, Eamonn
11
    III: Medium: Collaborative Research: Scaling Time Series Analytics to Massive Seismology Datasets
    • 批准号:
      2103976
    • 项目类别:
      Continuing Grant
    • 资助金额:
      $80.0万
    • 财政年份:
      2021
    • 负责人:
      Eamonn Keogh
    • 依托单位:
    Discovery Projects - Grant ID: DP210100072
    • 批准号:
      ARC : DP210100072
    • 项目类别:
      Discovery Projects
    • 资助金额:
      $40.8万
    • 财政年份:
      2021
    • 负责人:
      Eamonn Keogh
    • 依托单位:
    RI: Medium: Machine Learning for Agricultural and Medical Entomology
    • 批准号:
      1510741
    • 项目类别:
      Standard Grant
    • 资助金额:
      $110.0万
    • 财政年份:
      2015
    • 负责人:
      Eamonn Keogh
    • 依托单位:
    REU Site: RE-ICE: Research Experiences in Integrated Computational Entomology
    • 批准号:
      1452367
    • 项目类别:
      Standard Grant
    • 资助金额:
      $38.96万
    • 财政年份:
      2015
    • 负责人:
      Eamonn Keogh
    • 依托单位:
    海外基金