NRT-DESE: NRT in Integrated Computational Entomology (NICE)
NRT-DESE:综合计算昆虫学 (NICE) 中的 NRT
基本信息
- 批准号:1631776
- 负责人:
- 金额:$ 272.11万
- 依托单位:
- 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
- 起止时间:2016-09-15 至 2022-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
这项授予加州大学河滨分校(UCR)的国家科学基金会研究培训(NRT)奖将使来自计算机科学/工程和昆虫学/生命科学的研究团队能够培养下一代科学家和工程师,通过整合计算机科学和昆虫学生物学的学科,利用数据的不合理有效性来理解昆虫。综合计算昆虫学(NICE)的NRT将培养学生在生物领域的计算方面走在科学的前沿,为生物科学家提供计算技术的基础,为工程师了解关键的昆虫学和生态学问题。该项目预计将培训至少四十(40)名MS和博士生,其中包括二十(20)名来自生命科学、计算机科学和工程的资助博士生。该项目将是世界上首个此类项目,并将满足创新的高标准,同时提供昆虫学/生命科学方面的高要求培训结构,与机器学习、数据挖掘和统计方面的计算技术相结合。NICE计划承认并推动计算昆虫学作为一个新兴的跨学科领域。计算昆虫学作为一门学科认识到昆虫学和生态问题产生了大量的数据,充分利用这些数据将需要个人的知识跨越两个原本不同的领域。拟议项目的培训和研究结构力求弥合在培训、语言、方法、观点和知识方面的巨大差距,这些差距继续将工程/信息学和生命科学学科分开。通过课程作业和与政府机构和公司的联合项目,学员将体验将研究成果转化为已实施的公共政策或农业/医疗产品和服务。该项目将扩大到包括UCR接受NRT支持的研究生实习生和那些没有获得资助的研究生,并将在UCR可持续发展,因为新课程将纳入参与的院系和学位课程。该项目还将为其他机构提供可复制的计算昆虫学教育和培训模式。NSF研究培训(NRT)计划旨在鼓励开发和实施大胆的、具有潜在变革性的、可扩展的STEM研究生教育培训模式。培训路径致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合培训模式,在高度优先的跨学科研究领域对STEM研究生进行有效培训。
项目成果
期刊论文数量(16)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 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:
- 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:
- 影响因子:0
- 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:
- 影响因子:0
- 作者:Nakamura, Takaaki;Imamura, Makoto;Mercer, Ryan;Keogh, Eamonn
- 通讯作者:Keogh, Eamonn
A Computational System to Support Fully Automated Mark-Recapture Studies of Ants
支持蚂蚁全自动标记重新捕获研究的计算系统
- DOI:10.1109/csci49370.2019.00285
- 发表时间:2019
- 期刊:
- 影响因子:0
- 作者:Yoon, Carey;Madrid, Frank;West, Mari;Keogh, Eamonn
- 通讯作者:Keogh, Eamonn
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Eamonn Keogh其他文献
Irrevocable-choice algorithms for sampling from a stream
- DOI:
10.1007/s10618-016-0472-z - 发表时间:
2016-06-30 - 期刊:
- 影响因子:4.300
- 作者:
Yan Zhu;Eamonn Keogh - 通讯作者:
Eamonn Keogh
Beyond one billion time series: indexing and mining very large time series collections with $$i$$ SAX2+
- DOI:
10.1007/s10115-012-0606-6 - 发表时间:
2013-02-16 - 期刊:
- 影响因子:3.100
- 作者:
Alessandro Camerra;Jin Shieh;Themis Palpanas;Thanawin Rakthanmanon;Eamonn Keogh - 通讯作者:
Eamonn Keogh
Correction to: Domain agnostic online semantic segmentation for multi-dimensional time series
- DOI:
10.1007/s10618-019-00618-2 - 发表时间:
2019-02-14 - 期刊:
- 影响因子:4.300
- 作者:
Shaghayegh Gharghabi;Chin-Chia Michael Yeh;Yifei Ding;Wei Ding;Paul Hibbing;Samuel LaMunion;Andrew Kaplan;Scott E. Crouter;Eamonn Keogh - 通讯作者:
Eamonn Keogh
Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases
- DOI:
10.1007/pl00011669 - 发表时间:
2001-08-01 - 期刊:
- 影响因子:3.100
- 作者:
Eamonn Keogh;Kaushik Chakrabarti;Michael Pazzani;Sharad Mehrotra - 通讯作者:
Sharad Mehrotra
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
- DOI:
10.1007/s10618-016-0483-9 - 发表时间:
2016-11-23 - 期刊:
- 影响因子:4.300
- 作者:
Anthony Bagnall;Jason Lines;Aaron Bostrom;James Large;Eamonn Keogh - 通讯作者:
Eamonn Keogh
Eamonn Keogh的其他文献
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{{ truncateString('Eamonn Keogh', 18)}}的其他基金
III: Medium: Collaborative Research: Scaling Time Series Analytics to Massive Seismology Datasets
III:媒介:协作研究:将时间序列分析扩展到海量地震数据集
- 批准号:
2103976 - 财政年份:2021
- 资助金额:
$ 272.11万 - 项目类别:
Continuing Grant
Discovery Projects - Grant ID: DP210100072
发现项目 - 拨款 ID:DP210100072
- 批准号:
ARC : DP210100072 - 财政年份:2021
- 资助金额:
$ 272.11万 - 项目类别:
Discovery Projects
RI: Medium: Machine Learning for Agricultural and Medical Entomology
RI:媒介:农业和医学昆虫学的机器学习
- 批准号:
1510741 - 财政年份:2015
- 资助金额:
$ 272.11万 - 项目类别:
Standard Grant
REU Site: RE-ICE: Research Experiences in Integrated Computational Entomology
REU 网站:RE-ICE:综合计算昆虫学的研究经验
- 批准号:
1452367 - 财政年份:2015
- 资助金额:
$ 272.11万 - 项目类别:
Standard Grant
III: Medium: Hardware/Software Accelerated Data Mining for Real-Time Monitoring of Streaming Pediatric ICU Data
III:媒介:用于实时监控流式儿科 ICU 数据的硬件/软件加速数据挖掘
- 批准号:
1161997 - 财政年份:2012
- 资助金额:
$ 272.11万 - 项目类别:
Continuing Grant
Tools to Mine and Index Trajectories of Physical Artifacts
挖掘和索引物理文物轨迹的工具
- 批准号:
0803410 - 财政年份:2008
- 资助金额:
$ 272.11万 - 项目类别:
Continuing Grant
III-CXT-Large: Collaborative Research: Interactive and intelligent searching of biological images by query and network navigation with learning capabilities
III-CXT-Large:协作研究:通过具有学习能力的查询和网络导航对生物图像进行交互式和智能搜索
- 批准号:
0808770 - 财政年份:2008
- 资助金额:
$ 272.11万 - 项目类别:
Standard Grant
CAREER: Efficient Discovery of Previously Unknown Patterns and Relationships in Massive Time Series Databases
职业:在海量时间序列数据库中有效发现以前未知的模式和关系
- 批准号:
0237918 - 财政年份:2003
- 资助金额:
$ 272.11万 - 项目类别:
Continuing Grant
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