REU Site: Computational and Mathematical Modeling of Complex Systems

REU 网站：复杂系统的计算和数学建模

• 批准号: 1757923
• 负责人: Cristopher Moore
• 金额: $ 32.38万
• 依托单位: Santa Fe Institute
• 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: 2018-03-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1757923&HistoricalAwards=false
• 关键词: REU; Site; Computational; Mathematical; Modeling

The SFI Research Experiences for Undergraduates (REU) program is a ten-week residential research opportunity in which students develop innovative research projects in collaboration with mentors. The program asks students to discard traditional disciplinary boundaries, and learn computational modeling and data analysis techniques that can apply across the physical, natural, and social sciences. This allows students to ask big questions about real-world systems using rigorous mathematical and computational methods. Projects range from simulation to machine learning to proving theorems. The program supports the goals of science education and diversity in science by emphasizing engagement with students from non-elite institutions with limited research opportunities, women, and under-represented minorities (URMs). Early career scientists act as mentors in the program, gaining valuable experience as educators in mentoring. Research performed by SFI REUs has directly advanced the progress of science, and has focused on solving problems of direct relevance to society and the national health, including vaccination strategies for whooping cough, sustainability, economics of higher education, and social network analysis, among other topics. In every STEM field, computational and mathematical modeling are rapidly becoming essential skills: translating a real-world system into a quantitative model, designing and coding computational experiments, analyzing these experiments statistically, and comparing their results with data. Projects of this kind are an ideal opportunity for undergraduate training that builds students' technical and analytical skills, connects them with the wider scientific world, and links scientific thinking with real-world contexts and applications. The SFI REU program is designed around the strengths of being a leading transdisciplinary research center. Undergraduates are recruited from multiple departments including computer science, physics, mathematics, biology, and the social sciences, and paired with mentors from many different scientific backgrounds. Recent projects include epidemiology and public health, digital humanities and topic modeling, social network structure, cell biology and proteomics, smart cities and urban data, and statistical physics. Methods utilized range from simulation to data analysis to theorem-proving, and in many cases have produced publishable work. Throughout the summer, students are offered tutorials on the basics of data analysis, algorithms, network theory, statistics, and programming in Python and C++. Tutorials are also offered on science writing, presenting research, applying for jobs in science and picking a graduate school or industry carrer, and dealing with impostor syndrome and implicit bias.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.

SFI 本科生研究体验 (REU) 项目是一个为期十周的住宿研究机会，学生可以与导师合作开发创新研究项目。该项目要求学生放弃传统的学科界限，学习可应用于物理、自然和社会科学的计算建模和数据分析技术。这使得学生能够使用严格的数学和计算方法提出有关现实世界系统的重大问题。项目范围从模拟到机器学习再到定理证明。该计划通过强调与研究机会有限的非精英院校的学生、女性和代表性不足的少数群体 (URM) 的接触来支持科学教育和科学多样性的目标。早期职业科学家在该计划中担任导师，作为教育工作者在指导中获得宝贵的经验。 SFI REU 开展的研究直接推动了科学进步，并专注于解决与社会和国民健康直接相关的问题，包括百日咳疫苗接种策略、可持续性、高等教育经济学和社交网络分析等主题。在每个 STEM 领域，计算和数学建模正在迅速成为基本技能：将现实世界的系统转化为定量模型，设计和编码计算实验，对这些实验进行统计分析，并将其结果与数据进行比较。此类项目是本科生培训的理想机会，可以培养学生的技术和分析技能，将他们与更广泛的科学世界联系起来，并将科学思维与现实世界的背景和应用联系起来。 SFI REU 项目是围绕领先的跨学科研究中心的优势而设计的。本科生来自多个院系，包括计算机科学、物理、数学、生物学和社会科学，并与来自许多不同科学背景的导师配对。最近的项目包括流行病学和公共卫生、数字人文和主题建模、社交网络结构、细胞生物学和蛋白质组学、智慧城市和城市数据以及统计物理学。使用的方法范围从模拟到数据分析再到定理证明，并且在许多情况下已经产生了可发表的作品。整个夏天，学生都会学习有关数据分析、算法、网络理论、统计以及 Python 和 C++ 编程基础知识的教程。还提供关于科学写作、展示研究、申请科学工作、选择研究生院或行业职业生涯、以及处理冒名顶替综合症和隐性偏见的教程。该奖项反映了 NSF 的法定使命，并通过使用评估方法进行评估，认为值得支持。基金会的智力价值和更广泛的影响审查标准。

项目成果

Mentorship at a distance

远程指导

• DOI: 10.1126/science.370.6515.494
• 发表时间: 2020-10
• 期刊: Science
• 影响因子: 56.9
• 作者: Rankin, Naomi A.;Gröschel, Matthias I.;Farhat, Maha R.
• 通讯作者: Farhat, Maha R.

Scale and information-processing thresholds in Holocene social evolution

全新世社会进化的规模和信息处理阈值

• DOI: 10.1038/s41467-020-16035-9
• 发表时间: 2020-05
• 期刊: Nature Communications
• 影响因子: 16.6
• 作者: Shin, Jaeweon;Price, Michael Holton;Wolpert, David H.;Shimao, Hajime;Tracey, Brendan;Kohler, Timothy A.
• 通讯作者: Kohler, Timothy A.

Scaling of urban income inequality in the USA

美国城市收入不平等的规模

• DOI: 10.1098/rsif.2021.0223
• 发表时间: 2021-08
• 期刊: Journal of The Royal Society Interface
• 作者: Heinrich Mora, Elisa;Heine, Cate;Jackson, Jacob J.;West, Geoffrey B.;Yang, Vicky Chuqiao;Kempes, Christopher P.
• 通讯作者: Kempes, Christopher P.

Detection of local mixing in time-series data using permutation entropy

使用排列熵检测时间序列数据中的局部混合

• DOI: 10.1103/physreve.103.022217
• 发表时间: 2021-02
• 期刊: Physical Review E
• 影响因子: 2.4
• 作者: Neuder, Michael;Bradley, Elizabeth;Dlugokencky, Edward;White, James W.;Garland, Joshua
• 通讯作者: Garland, Joshua

Systematic shifts in scaling behavior based on organizational strategy in universities

基于大学组织战略的规模化行为的系统性转变

• DOI: 10.1371/journal.pone.0254582
• 发表时间: 2021-10
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Taylor, Ryan C.;Liang, Xiaofan;Laubichler, Manfred D.;West, Geoffrey B.;Kempes, Christopher P.;Dumas, Marion
• 通讯作者: Dumas, Marion