Minisymposium (ID: SS-GTMDS)

Geometric and Topological Methods in Data Science

Maia Fraser (University of Ottawa), Tanya Schmah (University of Ottawa)

Geometry and topology are increasingly used in data analysis, in combination with statistics and machine learning. Two notable approaches are: topological data analysis (TDA), which extracts topological and geometric features from a dataset for visualization or later analyses; and Riemannian shape analysis, which uses Riemannian geometry on spaces of shapes or images. Both approaches are applied in many areas where data is high-dimensional and has a complex structure, including medical image analysis, computer vision, and many more. This session will bring together researchers using a variety of geometric and topological approaches to data science.

Please note the ID code assigned to your presentation (identical to the ID code of your accepted abstract). It is required for submitting your paper for the AMMCS-2019 Proceedings. Submission is not mandatory. All submitted papers will be refereed and only accepted papers will be published in the AMMCS-2019 Proceedings.

If you intend to submit your paper, please go to the AMMCS Proceedings Page. Follow exactly the Author Instructions accessible from that page.