Over the past two decades, high-throughput data collection technologies have become commonplace in most fields of science and technology, and with them an ever-increasing amount of big high dimensional data is being generated by virtually every real-world system. While such data systems are highly diverse in nature, the underlying data analysis and exploration task give rise to common challenges at the core of modern representation learning. For example, even though modern real-world data typically have high dimensional ambient measurement spaces, they often exhibit low dimensional intrinsic structures that can be uncovered by geometry-oriented methods, such as the ones encountered in manifold learning, graph signal processing, geometric deep learning, and topological data analysis. As a result, recent years have seen significant interest and progress in geometric and topological approaches to representation learning,whichenabletractableexploratoryanalysisbydomainexpertswhoareoftennotcomputationoriented. Our overarching goal in the proposed workshop is to deepen our understanding of the challenges and opportunities in this field, while breaking the barriers between the typically disjoint computational approaches (or communities) that work in this field, with emphasis on the domains of topological data analysis, graph representation learning, and manifold learning, on which we shall subsequently briefly comment.

Website: https://gt-rl.github.io/