TRIPODS+X: VIS: Creating an Annual Data Science Forum

该项目旨在创建一个为期一周的年度数据科学论坛（DSF），共同举办几个旨在促进知识，创建研究人员网络以及培训学生和教师的活动。 该论坛的重点将是开发下一代人工智能技术和研究人员，其基本假设是科学和工程学科的应用将成为数据驱动方法下一个重大进展的背景。 DSF汇集了第二届科学与工程机器学习研讨会（MLSE），数据科学研讨会（WDSW）和数据驱动发现研讨会（FDDD）的基础。 一个包括多个活动的论坛将为MLSE和FDDD带来更多的女性，而将MLSE与FDDD并列将鼓励领域科学家和工程师以及核心TRIPODS（数据科学原理的跨学科研究）社区之间的交流。 第二届MLSE将以一个愿景工作组结束，其中一组精选的研究人员将撰写一份关于机器学习未来的白色论文。数据科学论坛将有助于促进机器学习方法以及跨科学和工程的合作，汇集各种各样的STEM领域，将机器学习应用于基础和应用问题。 演讲将侧重于使现有的机器学习方法适应当前的研究领域，开发专门针对科学和工程的新机器学习算法，并确定只能使用数据驱动方法进行研究的新前沿。 在科学和工程领域传播机器学习方法可能会对美国的研究产生持久的影响。 该论坛将补充技术研究计划，由机器学习专家讲授各种尖端工具的短期课程，这些工具对推进这些领域至关重要。 每一条赛道都将有一个以传统领域为中心的主题，但每一条赛道的目标都是跨学科的。 并行运行这些轨道将有助于培养每个应用科学或工程轨道以及TRIPODS社区的紧密联系的研究人员社区，同时通过联合活动和共同定位促进跨领域思想的交叉施肥。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to create an annual week-long Data Science Forum (DSF), co-locating several events intended to advance knowledge, create networks of researchers, and train students and faculty. The focus of this forum will be on developing the next generation of AI technologies and researchers with the underlying hypothesis that applications across the sciences and engineering disciplines will be the context of the next major advances in data-driven approaches. DSF brings together the Second Symposium on Machine Learning in Science and Engineering (MLSE), a Women in Data Science Workshop (WDSW), and a Foundations of Data Driven Discovery workshop (FDDD). A forum including multiple events will bring more women to MLSE and FDDD, while juxtaposing MLSE with FDDD will encourage cross-fertilization among the domain scientists and engineers and the core TRIPODS (Transdisciplinary Research in Principles of Data Science) community. The 2nd MLSE will conclude with a Visioning Working Group where a select group of researchers will produce a white paper on the future of machine learning.The Data Science Forum will help catalyze machine learning methodologies and collaborations across the sciences and engineering, bringing together a diverse set of STEM fields applying machine learning to fundamental and applied problems. Presentations will focus on adapting existing machine learning methods to current research areas, developing new machine learning algorithms specific to science and engineering, and identifying new frontiers of research that may only be pursued using a data-driven approach. Disseminating machine learning methods across science and engineering could have lasting implications for US research. The forum will supplement the technical research program with short courses taught by experts in machine learning on a variety of cutting-edge tools that are critical in advancing these fields. Each track will have a theme centered in a traditional domain, but each is aiming to itself be interdisciplinary. Running these tracks in parallel will help foster tight knit communities of researchers in each of the applied science or engineering tracks, as well as the TRIPODS community, while fostering cross-fertilization of ideas across fields through joint events and co-location.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.