课题基金 / 基金详情

NRT-HDR: Modeling and Understanding Human Behavior: Harnessing Data from Genes to Social Networks

NRT-HDR: Modeling and Understanding Human Behavior: Harnessing Data from Genes to Social Networks
NRT-HDR:建模和理解人类行为:利用从基因到社交网络的数据
批准号:
1829071
负责人:
Andrea Bertozzi
金额:
$300.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-09-01 至 2024-08-31

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中文摘要
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英文摘要
A confluence of technologies is transforming the biological, environmental, and social sciences into data-intensive sciences. Indeed, with the data now produced every day, there exists an unprecedented opportunity to revolutionize the journey of scientific discovery. By harnessing these data, one can advance the understanding of human conditions, behaviors, and their underlying mechanisms and social outcomes, enabling a spectrum of new and transformative research and practice. Fundamental new approaches across computing, mathematics, engineering, and sciences are critically needed, and future scientists must be accordingly trained in these emergent cutting-edge methods. This National Science Foundation Research Traineeship (NRT) award to the University of California, Los Angeles will address this demand by training graduate students at the intersections of data science, mathematics, cryptography, artificial intelligence, genomics, behavior science, and social science. The traineeship program anticipates training one hundred twenty (120) PhD students, including fifty (50) funded trainees, from the social, biological, mathematical and computational sciences and engineering, through a unique and comprehensive training opportunity. This cross-disciplinary traineeship program has four research areas: genomics and genetics; brain imaging and image analysis; mobile sensing and individual behaviors; and social networks. These areas are interconnected through three core themes: mathematical modeling and network analysis, scalable machine learning and big data analytics, and biomedical applications and social outcomes. At the nexus of these research areas and core themes, this traineeship program provides novel interdisciplinary graduate education to advance both graduate student training and scientific research. Key features of the traineeship include novel curricula; cross-disciplinary laboratory rotations between engineering, life science, and social science; new foundational classes at the intersections of data science, mathematics, artificial intelligence, behavior science, and social science; summer internships at research institutes, big data firms, and hospitals and translational clinical settings; career, ethics, and technical communication skills development; and outreach to minority, women, and high school students with a distinct focus on groups traditionally underrepresented in STEM PhD programs. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.
期刊论文(12)
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会议论文
DOI: 10.1002/nla.2458
发表时间: 2021-10
期刊: Numerical Linear Algebra with Applications
影响因子: 4.3
作者: [Yotam Yaniv;Jacob D. Moorman;W. Swartworth;Thomas K. Tu;Daji Landis;D. Needell]
通讯作者: Yotam Yaniv;Jacob D. Moorman;W. Swartworth;Thomas K. Tu;Daji Landis;D. Needell
DOI: 10.1016/j.ymeth.2019.03.003
发表时间: 2019-08-15
期刊: METHODS
影响因子: 4.8
作者: [LaPierre, Nathan, Ju, Chelsea J. -T., Wang, Wei]
通讯作者: Wang, Wei
DOI: 10.1371/journal.pgen.1008773
发表时间: 2020-05-01
期刊: PLOS GENETICS
影响因子: 4.5
作者: [Agrawal, Aman, Chiu, Alec M., Sankararaman, Sriram]
通讯作者: Sankararaman, Sriram
DOI: 10.1186/s12864-019-5699-9
发表时间: 2019-06-06
期刊: BMC GENOMICS
影响因子: 4.4
作者: [LaPierre, Nathan, Mangul, Serghei, Eskin, Eleazar]
通讯作者: Eskin, Eleazar
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