Upcoming Events

  • (03/11, 5-6 PM) Alex Kokot will be giving a follow-up presentation on kernel thinning. Hybrid in CS&SS Conference Room (Padelford Hall lower level) or via Zoom link here

About Geometric Data Analysis Group

We are a group of faculty and students in the Department of Statistics at the University of Washington interested in Geometric Data Analysis. Our research is focused on analyzing the underlying geometric structure of data. You can add yourself to the group mailing list at https://mailman12.u.washington.edu/mailman/listinfo/geometry

You can see announcements about the group, including meeting dates, times and locations, in the News tab. Our topics of interest Geometric Data Analysis include:

  • Topological Data analysis
  • Manifold learning algorithms -- are they "correct"? How to detect and remove algorithmic artefacts.
  • Higher order Laplacians and the analysis of vector fields on manifolds
  • Interpretable manifold coordinates
  • Non-parametric statistics on the sphere

Everyone is welcome to join! For student participants: if you plan to volunteer for a presentation or leading a discussion, you can sign up for 1 stat 600 credit with one of the faculty organizers.

Organizers: Marina Meila , Yen-Chi Chen, Jerry Wei, James Buenfil

Resources

The following are good introductory materials for geometric data analysis:

The following is an incomplete list of active members of the group.
  • Marina Meila
  • Yen-Chi Chen
  • Yikun Zhang
  • Jerry Wei
  • Yidan Xue
  • James Buenfil
  • Alex Kokot
  • Weicheng Wu
  • Vlad Murad
Former GDA group members
  • Yu-Chia Chen
  • Hanyu Zhang
  • Samson Koelle
  • Zhenman Yuan
  • Alon Milchgrub
The following funding sources have supported this research group.
  • UCLA IPAM/ NSF DMS-1925919
  • NSF DMS 2015272
  • NSF DMS 1810975
  • NSF CCF 2019844
  • DE-EE0008563
  • DE-EE0009351
  • GO-MAP Graduate Excellence Award
  • ARCS Award
  • NSF IGERT Data Science Fellowship
  • NSF DMS - 1810960
  • NSF DMS - 1952781
  • NSF DMS - 2112907