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

数据驱动的科学和工程需要来自不同社区的研究人员之间的密切合作和协调，包括核心科学，统计和优化。该项目将建立并扩展现有的成功的“黑客周”模型，将神经科学和地球科学的参与者与机器学习和优化专家聚集在一起。hackweeks将包括核心方法的教程，实践课程和小组活动，旨在促进对神经科学和地球科学中数据驱动的科学问题以及基本方法及其如何应用于这些科学的更深入理解和更密切的合作。特别是，研究人员计划重新设计地理黑客周和神经黑客周，这两个活动近年来每年都在华盛顿大学举行。Geo-hackweek将被重新设计，以包括地球物理数据插值和去噪，地球物理逆问题和高斯过程模型的讨论，并将这些与优化技术联系起来，包括稀疏和低秩模型，随机优化和PDE约束优化。《神经黑客周刊》将增加关于在神经成像数据分析中使用最佳传输模型和Wasserstein距离的教程。本项目旨在（1）让领域科学的参与者了解基础主题，以便他们更好地理解数据科学工具，特别是深入了解这些算法如何以及何时工作良好（或不工作）;（2）培训学员结合具体领域的问题考虑各种方法，能够确定具体领域的挑战，并批判性地思考如何有效地利用优化和机器学习工具来解决特定的问题类;（3）让具有基础背景的学生接触应用领域，了解机器学习工具应用中的实际挑战;该奖项反映了NSF的法定使命，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。

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.