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：
Eamonn Keogh
金额：
$ 272.11万
依托单位：
University of California-Riverside
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：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1631776&HistoricalAwards=false
关键词：
NRT DESE Integrated Computational Entomology

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.

美国国家科学基金会授予加州大学河滨分校 (UCR) 的研究实习生 (NRT) 奖将使来自计算机科学/工程和昆虫学/生命科学的研究人员团队能够培养下一代科学家和工程师，以利用不合理的有效性通过整合计算机科学和昆虫生物学的学科来了解昆虫的数据。综合计算昆虫学 (NICE) 的 NRT 将培养学生走在生物领域计算科学的最前沿，为生物科学家提供计算技术的基础，并为工程师提供对关键昆虫学和生态问题的理解。该项目预计培训至少四十 (40) 名硕士和博士生，其中包括二十 (20) 名来自生命科学、计算机科学和工程领域的受资助博士生。该项目将是世界上第一个此类项目，将满足创新的高标准，同时提供与机器学习、数据挖掘和统计计算技术相结合的昆虫学/生命科学的高要求培训结构。 NICE 项目承认并推进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会产生大量数据，并且充分利用这些数据需要知识跨越两个不同领域的个人。拟议项目的培训和研究结构旨在弥合培训、语言、方法、观点和知识方面的巨大差距，这些差距继续划分工程/信息学和生命科学学科。通过课程作业以及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实施的公共政策或农业/医疗产品和服务。该项目将扩大规模，将接受 NRT 支持的 UCR 研究生学员和未获得资助的研究生学员纳入其中，并且随着新课程将纳入参与的院系和学位课程，该项目将在 UCR 可持续发展。该项目也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) 计划旨在鼓励为 STEM 研究生教育培训开发和实施大胆的、具有潜在变革性的新可扩展模型。培训课程致力于通过创新、循证且符合不断变化的劳动力和研究需求的综合培训模式，对高度优先的跨学科研究领域的 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：
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：
0
作者：
Mercer, R;Alaee, S;Abdoli, A;Singh, S;Murillo, A;Keogh, E.
通讯作者：
Keogh, E.

Matrix Profile XX: Finding and Visualizing Time Series Motifs of All Lengths using the Matrix Profile

DOI：
10.1109/icbk.2019.00031
发表时间：
2019-01-01
期刊：
2019 IEEE International Conference on Big Knowledge (ICBK)
影响因子：
0
作者：
Madrid, Frank;Imani, Shima;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：
2019 International Conference on Computational Science and Computational Intelligence (CSCI
影响因子：
0
作者：
Yoon, Carey;Madrid, Frank;West, Mari;Keogh, Eamonn
通讯作者：
Keogh, Eamonn