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: $ 300万
• 依托单位: University of California-Los Angeles
• 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
• 起止时间: 2018-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1829071&HistoricalAwards=false
• 关键词: NRT; HDR; Modeling; Understanding; Human

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.

技术的融合正在将生物科学、环境科学和社会科学转变为数据密集型科学。事实上，随着每天产生的数据，存在着一个前所未有的机会来彻底改变科学发现的旅程。通过利用这些数据，人们可以促进对人类状况、行为及其潜在机制和社会结果的理解，从而实现一系列新的、变革性的研究和实践。计算机、数学、工程和科学领域的基本新方法是迫切需要的，未来的科学家必须在这些新兴的前沿方法中接受相应的培训。这项授予加州大学洛杉矶分校的国家科学基金会研究培训（NRT）奖将通过培养数据科学、数学、密码学、人工智能、基因组学、行为科学和社会科学交叉领域的研究生来满足这一需求。该培训计划预计将通过一个独特而全面的培训机会，培训120名博士生，其中包括50名获得资助的博士生，他们来自社会、生物、数学和计算科学与工程。这个跨学科的实习项目有四个研究领域：基因组学和遗传学；脑成像及图像分析；移动感知与个体行为；还有社交网络。这些领域通过三个核心主题相互关联：数学建模和网络分析，可扩展的机器学习和大数据分析，以及生物医学应用和社会成果。在这些研究领域和核心主题的联系下，该培训计划提供了新颖的跨学科研究生教育，以推进研究生培训和科学研究。实习的主要特点包括课程新颖；在工程、生命科学和社会科学之间进行跨学科的实验室轮转；数据科学、数学、人工智能、行为科学和社会科学交叉的新基础课程；在研究机构、大数据公司、医院和转化临床机构的暑期实习；职业、道德和技术沟通技能的发展；向少数族裔、女性和高中生伸出援手，重点关注传统上在STEM博士项目中代表性不足的群体。美国国家科学基金会研究实习生（NRT）计划旨在鼓励开发和实施大胆的、具有潜在变革性的STEM研究生教育培训新模式。该项目致力于通过创新、循证、适应不断变化的劳动力和研究需求的综合培训模式，在高优先级跨学科研究领域对STEM研究生进行有效培训。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Selectable Set Randomized Kaczmarz

• 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

MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction

• 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

Scalable probabilistic PCA for large-scale genetic variation data

• 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

MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples

• 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

Learning to Predict Human Stress Level with Incomplete Sensor Data from Wearable Devices

• DOI: 10.1145/3357384.3357831
• 发表时间: 2019-11
• 期刊: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
• 作者: Jyun-Yu Jiang;Zehan Chao;A. Bertozzi;Wei Wang;S. Young;D. Needell