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EAGER: Bridging the Last Mile; Towards an Assistive Cyberinfrastructure for Accelerating Computationally Driven Science

EAGER：弥合最后一英里；

基本信息

批准号：
1945347
负责人：
Rajiv Ramnath
金额：
$ 29.97万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1945347&HistoricalAwards=false
关键词：
EAGER Bridging Last Mile Towards

项目摘要

Even though computational scientists have high-speed computers and powerful software tools at their disposal, their ability to make scientific discovery is held back because much of their time is inefficiently spent in data exploration, manipulation and visualization, preliminary analyses, and hit-and-miss attempts to find the right settings for their software tools to be used effectively, rather than in running their computations at scale. For example, researchers doing gene sequencing (a process in which the individual base nucleotides in an organism's DNA are identified) have to figure what sequencing method to use, which software package - out of several - to use for that sequencing method, what settings to use for the software package, and so on. Researchers would greatly benefit by learning from prior use of the software tools at their disposal, that is, from past experiences and mistakes, both their own, and those of other researchers in the field. However, only a small percentage of them are able to do so. This exploratory project will investigate whether effective, intelligent guidance can be extracted from past experience on specific applications using artificial intelligence techniques and then provided in a tailored manner to the individual researcher. This will be done within the software tools being used by the researcher, and provided by the underlying cyberinfrastructure itself, so that anyone who uses these tools can benefit. This project will explore the feasibility of AI-based approaches for providing such assistance, and prototype, pilot and validate a range of capabilities, and widely disseminate results.Current research and development in cyberinfrastructure(CI) for science focuses on scaling the CI: developing algorithms that scale better, automating pipelines, building scalable computational and data systems, and optimizing system resource allocation. This project, on the other hand, focuses on scaling the individual researcher, i.e. making her more effective, through the application of a portfolio of techniques, from observational studies of scientist-CI interactions, to end-to-end instrumentation for recording and tracking these interactions across the CI, building machine-learned models from these interactions, and embedding and experimenting with these models within human-machine teaming paradigms. These techniques will be given the best chance to succeed by applying them in a methodological and technical framework that focuses on specific applications. The proposed methods will vary participant expertise, test hypotheses of performance, and use transparent ?guide me? methodologies to establish dimensions of individual differences in designing guidance. The exemplar domain science is genomics, where the goal is to sequence and assemble the complete DNA of selected species, and where the work in this project could be particularly transformative. The project team is integrative, interdisciplinary and convergent; the investigators have expertise in genomics, software engineering, systems, data science, project management, and human-machine systems, and are working with a key CI provider in the Ohio Supercomputer Center, and collectively advising a small team of graduate students. The project is aligned with the NSF Big Ideas of Harnessing the Data Revolution, Growing Convergence Research and the Future of Work, and the Office of Advanced Infrastructure criteria for software cyberinfrastructure, since it is domain and computer sciences-driven, innovative, collaborative and convergent, strategically managed, and building on significant prior investments by NSF - a clear path to sustainability. This work could make computational scientists from many science domains transformatively more productive, leading to accelerated discovery. Additional broader impacts are through educational case-studies for computational science, contributions to instrumentation standards, and observational and empirical study methods for CI. A side contribution of this work will be in assessing the usability of CI tools . This will directly enable tool designers to build more usable tools. Broadening participation has been emphasized: one of the principal investigators is a woman. All three PIs have a history of recruiting and working with women students. At least one of the funded graduate students will be an incoming female student. The project team will additionally be closely collaborating with two women students and a woman post-doctoral researcher in the genomics laboratory.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.

尽管计算科学家拥有高速计算机和强大的软件工具，但他们进行科学发现的能力却受到了阻碍，因为他们的大部分时间都低效地花在了数据探索、操作和可视化、初步分析以及为有效使用软件工具而寻找正确设置的尝试上，而不是大规模地运行他们的计算。例如，进行基因测序的研究人员（一个识别生物体DNA中单个碱基核苷酸的过程）必须弄清楚使用哪种测序方法，在几个软件包中使用哪个软件包，在软件包中使用什么设置，等等。研究人员将从他们使用的软件工具的先前使用中学习，也就是说，从过去的经验和错误中学习，包括他们自己的和该领域其他研究人员的经验和错误。然而，只有一小部分人能够做到这一点。这个探索性项目将研究是否可以从过去使用人工智能技术的特定应用的经验中提取有效的智能指导，然后以量身定制的方式提供给个别研究人员。这将在研究人员使用的软件工具中完成，并由底层网络基础设施本身提供，因此使用这些工具的任何人都可以受益。该项目将探索基于人工智能的方法提供此类援助的可行性，并对一系列能力进行原型、试点和验证，并广泛传播结果。目前科学领域网络基础设施（CI）的研究和发展重点是扩展CI：开发更好扩展的算法，自动化管道，构建可扩展的计算和数据系统，以及优化系统资源分配。另一方面，该项目侧重于扩展个体研究人员，即通过应用一系列技术，从科学家-CI交互的观察研究，到记录和跟踪CI中这些交互的端到端仪器，从这些交互中构建机器学习模型，并在人机团队范例中嵌入和试验这些模型，使她更有效。通过在侧重于具体应用的方法和技术框架中应用这些技术，这些技术将获得成功的最佳机会。所提出的方法将改变参与者的专业知识，测试绩效假设，并使用透明的？指导我吗?在设计指南中建立个体差异维度的方法。典型的领域科学是基因组学，其目标是对选定物种的完整DNA进行排序和组装，这一项目的工作可能特别具有变革性。项目团队具有综合性、跨学科、融合性；研究人员拥有基因组学、软件工程、系统、数据科学、项目管理和人机系统方面的专业知识，并与俄亥俄超级计算机中心的一个关键CI提供商合作，并共同为一个小型研究生团队提供建议。该项目符合美国国家科学基金会（NSF）利用数据革命、日益融合的研究和未来工作的大构想，以及美国高级基础设施办公室（Office of Advanced Infrastructure）对软件网络基础设施的标准，因为它是由领域和计算机科学驱动的、创新的、协作的、融合的、战略管理的，并建立在美国国家科学基金会（NSF）的重大前期投资基础上——一条通向可持续性的清晰道路。这项工作可以使许多科学领域的计算科学家更具生产力，从而加速发现。其他更广泛的影响是通过计算科学的教育案例研究，对仪器标准的贡献，以及CI的观察和实证研究方法。这项工作的另一个贡献是评估CI工具的可用性。这将直接使工具设计人员能够构建更多可用的工具。已强调扩大参与：主要研究人员之一是妇女。这三所私立学校都有招收女学生和与女学生合作的历史。获资助的研究生中至少有一名是即将入学的女学生。该项目团队还将与基因组学实验室的两名女学生和一名女博士后研究员密切合作。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。