REU Site: Algorithms and Optimization for Sustainability and Biology

现代跨学科问题越来越需要包含复杂算法技术的复杂解决方案。需要既能理解现实世界问题又能进行抽象算法开发的实践者。这个项目的目标是让本科生了解协作算法开发和问题解决。参与这个项目的学生将发展广泛适用的研究技能，这些技能在学术和商业工作中都很有价值。研究项目将集中在优化和可持续发展的广泛主题上，具体主题是计算生物学、几何学和碳捕获技术应用的优化。一个首要目标是提高本科生对创新研究的兴趣和参与，增加攻读计算机科学和/或相关学科本科生和研究生学位的学生(特别是代表性不足的群体)的数量和多样性。该REU网站的智力重点是为现实世界的问题开发算法和优化方法。所考虑的问题在计算上具有挑战性(大多数是NP-Hard)，因此需要创新的算法和优化方法来在实践中解决它们。因此，REU计划的活动集中在以下一般主题领域：1.碳捕获基础设施优化，2.组合和几何优化，以及3.计算生物学的图形算法。碳捕获基础设施优化需要新的算法来智能地设计满足工业二氧化碳减排目标的管道网络。组合优化和几何优化是一个传统的计算机科学研究领域，然而，几何优化的一个新趋势是考虑更现实的约束。最后，生物学中的数据革命在算法和计算方面带来了各种新的研究课题，包括DNA组装问题和表征基因相互作用和网络。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Modern interdisciplinary problems increasingly require complex solutions that incorporate sophisticated algorithmic techniques. Practitioners are needed who are able to both understand real-world problems and undertake abstract algorithm development. The goal of this project is to expose undergraduate students to collaborative algorithm development and problem solving. Students who participate in this project will develop broadly-applicable research skills that are valuable in both academic and commercial workplaces. Research projects will be centered on the broad themes of optimization and sustainability with specific topics in computational biology, geometry and optimization for applications of carbon capture technology. An overarching goal is to heighten undergraduate student interest and participation in innovative research and to increase the number and diversity of students (especially underrepresented groups) pursuing undergraduate and graduate degrees in computer science and/or related disciplines.The intellectual focus of this REU site is in developing algorithms and optimization approaches for real-world problems. The problems considered are computationally challenging (most are NP-hard), so innovative algorithms and optimization approaches are needed to solve them in practice. Accordingly, the activities of the REU program are centered on the following general topic areas: 1. Carbon capture infrastructure optimization, 2. Combinatorial and geometric optimization, and 3. Graph algorithms for computational biology. Carbon capture infrastructure optimization requires novel algorithms to intelligently design pipeline networks that meet industrial carbon dioxide emission mitigation targets. Combinatorial and geometric optimization is a traditional computer science research area, however a recent trend in geometric optimization is to consider more realistic constraints. Finally, the data revolution in biology has led to a variety of new research topics in algorithms and computation, including DNA assembly problems and characterizing gene interactions and networks.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.