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.

这项国家科学基金会研究学员（NRT）授予加利福尼亚大学Riverside（UCR）将使来自计算机科学/工程和昆虫学/生命科学的研究人员能够为下一代科学家和工程师的准备工作做好准备，以利用数据的不合理有效性，以通过整合计算机科学和元素学生物学的昆虫来理解数据的不合理有效性。综合计算昆虫学（NICE）中的NRT将培训学生在生物领域计算中处于科学的最前沿，为生物学科学家提供了计算技术和工程师的基础，对关键的昆虫学和生态问题有了了解。该项目预计至少培训了至少40（40）个MS和博士学位学生，包括来自生命科学，计算机科学和工程学的20（20）名资助的博士学位。该项目将是世界上任何地方的同类计划中的第一个计划，并将符合高标准的创新标准，同时提供与机器学习，数据挖掘和统计学中计算技术相结合的昆虫学/生命科学培训的结构。 NICE计划将计算昆虫学视为新兴的跨学科领域。计算昆虫学作为一门学科认为，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将需要知识跨越两个原本不同领域的个人。拟议项目的培训和研究结构旨在弥合培训，语言，方法，观点和知识的巨大差距，以继续将工程学/信息学和生命科学学科分开。通过与政府机构和公司的课程和联合项目，学员将体验研究成果的转化为实施的公共政策或农业/医疗产品和服务。该项目将扩展为包括UCR接受NRT支持和未获得资金的研究生学员，并且在UCR中将是可持续的，因为新课程将在参与部门和学位课程中成立。该项目还将作为其他机构的可复制计算昆虫学教育和培训模型。 NSF研究训练（NRT）计划旨在鼓励开发和实施用于STEM研究生教育培训的大胆，新的潜在可变性可扩展模型。通过全面的跨学科研究领域的STEM研究生，培训轨道致力于有效地培训STEM研究生，通过全面的培训模型，该模型具有创新，基于循证的，并且与不断变化的员工队伍和研究需求保持一致。

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.

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：
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：
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