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）计划是一个为期十周的住宿研究机会，学生与导师合作开发创新研究项目。该项目要求学生抛弃传统的学科界限，学习计算建模和数据分析技术，这些技术可以应用于物理、自然和社会科学。这允许学生使用严格的数学和计算方法来提出关于现实世界系统的大问题。项目范围从模拟到机器学习再到定理证明。该计划通过强调与来自研究机会有限的非精英机构的学生、妇女和代表性不足的少数民族（urm）的接触，支持科学教育和科学多样性的目标。早期的职业科学家在这个项目中担任导师，在指导中获得宝贵的经验。SFI REUs开展的研究直接推动了科学的进步，并专注于解决与社会和国家卫生直接相关的问题，包括百日咳疫苗接种策略、可持续性、高等教育经济学和社会网络分析等主题。在每个STEM领域，计算和数学建模正迅速成为基本技能：将现实世界的系统转化为定量模型，设计和编码计算实验，统计分析这些实验，并将结果与数据进行比较。这类项目是培养学生技术和分析能力的理想机会，将他们与更广阔的科学世界联系起来，并将科学思维与现实环境和应用联系起来。SFI REU计划是围绕成为领先的跨学科研究中心的优势而设计的。本科生从计算机科学、物理、数学、生物和社会科学等多个院系招聘，并与来自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、社会网络结构、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。使用的方法范围从模拟到数据分析再到定理证明，在许多情况下已经产生了可发表的工作。整个夏季，学生们将学习数据分析、算法、网络理论、统计学以及Python和c++编程等基础知识。还提供了关于科学写作、展示研究、申请科学工作、选择研究生院或行业职业、处理冒充者综合症和内隐偏见的教程。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

Mentorship at a distance

远程指导

• DOI: 10.1126/science.370.6515.494
• 发表时间: 2020
• 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.

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
• 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

Scale and information-processing thresholds in Holocene social evolution

• 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: Jaeweon Shin;M. Price;D. Wolpert;Hajime Shimao;Brendan D. Tracey;Timothy A. Kohler

Scaling of urban income inequality in the USA

• DOI: 10.1098/rsif.2021.0223
• 发表时间: 2021-08-04
• 期刊: JOURNAL OF THE ROYAL SOCIETY INTERFACE
• 影响因子: 3.9
• 作者: Mora, Elisa Heinrich;Heine, Cate;Kempes, Christopher P.
• 通讯作者: Kempes, Christopher P.

Simulating galactic cosmic ray effects: Synergy modeling of murine tumor prevalence after exposure to two one-ion beams in rapid sequence

• DOI: 10.1016/j.lssr.2020.01.001
• 发表时间: 2020-05-01
• 期刊: LIFE SCIENCES IN SPACE RESEARCH
• 影响因子: 2.5
• 作者: Huang, Edward Greg;Wang, Ren-Yi;Sachs, Rainer K.
• 通讯作者: Sachs, Rainer K.