TRIPODS+X:EDU: Foundational Training in Neuroscience and Geoscience via Hackweeks

TRIPODS X:EDU：通过 Hackweeks 进行神经科学和地球科学基础培训

• 批准号: 1839291
• 负责人: Maryam Fazel
• 金额: $ 17.62万
• 依托单位: University of Washington
• 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: 2018-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839291&HistoricalAwards=false
• 关键词: TRIPODS+X; EDU; Foundational; Training; Neuroscience

Data-driven science and engineering requires close collaboration and coordination among researchers from different communities, including core sciences, statistics, and optimization. This project will build on and broaden the successful existing "hackweek" model to bring together participants from neuroscience and geoscience with experts in machine learning and optimization. The hackweeks will incorporate tutorials on core methods, hands-on sessions, and group activities designed to promote deeper understanding and closer collaboration of both data-driven scientific problems in neuroscience and geoscience, as well as fundamental methodologies and how they apply to these sciences. In particular, the investigators plan to redesign geo-hackweek and neuro-hackweek, two events that the have been held annually at the University Washington by two of the PIs in recent years. Geo-hackweek will be redesigned to include the discussion of geophysical data interpolation and denoising, geophysical inverse problems, and Gaussian process models, and connecting these to techniques in optimization, including sparse and low-rank models, stochastic optimization, and PDE-constrained optimization. Neuro-hackweek will be augmented to include tutorials on the use of optimal transport models and Wasserstein distances in the analysis of neuroimaging data. This project aims to(1) Expose participants from domain sciences to foundational topics, so they better understand data science tools, and in particular gain insight into how and when these algorithms work well (or do not work well);(2) Train participants to consider methods in the context of domain-specific problems, be able to identify domain-specific challenges, and think critically about how to effectively leverage optimization and machine learning tools for specific problem classes;(3) Expose students with foundations background to application domains, to understand practical challenges in application of machine learning tools;(4) Generate pedagogical material that can used in similar events;(5) Encourage collaborations between domain experts and experts on theory and methods.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.

数据驱动的科学和工程需要来自不同社区的研究人员（包括核心科学，统计和优化）之间的密切合作和协调。该项目将基于并扩大成功的现有“ hackweek”模型，以将神经科学和地球科学的参与者与机器学习和优化专家联系在一起。黑客将纳入有关核心方法，动手会议和小组活动的教程，旨在促进对神经科学和地球科学中数据驱动的科学问题的更深入理解和更紧密的合作，以及基本方法论以及如何应用这些科学。特别是，调查人员计划重新设计Geo-Hackweek和Neuro-Hackweek，这两项活动近年来每年在华盛顿大学举行了两次PIS。 Geo-Hackweek将重新设计，以包括对地球物理数据插值和去核的讨论，地球物理逆问题以及高斯工艺模型，并将其连接到优化方面的技术，包括稀疏和低级别模型，随机优化，随机优化，以及PDE构成的优化。 Neuro-Hackweek将被增强，以包括有关使用最佳传输模型的教程，以及在神经成像数据分析中使用Wasserstein距离。该项目的目的是（1）将参与者从领域科学曝光到基础主题，以便他们更好地了解数据科学工具，特别是可以深入了解这些算法如何以及何时运行良好（或不奏效）；（2）培训参与者在领域特定的问题上考虑在领域特定的问题上考虑特定的特定挑战，并有效地识别型号的范围，并有效地识别出效力；揭露学生在应用领域的基础背景，以了解机器学习工具的应用方面的实际挑战；（4）生成可以在类似事件中使用的教学材料；（5）鼓励在理论和方法方面的域专家与专家之间的合作。该奖项反映了NSF的立法任务，并通过评估基础的知识效果和广泛的评论，并被视为通过评估来进行评估。