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

University Of Washington, Seattle WA

Investigators

Abstract

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.