Upcoming Events
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(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:
- A Tutorial on Kernel Density Estimation and Recent Advances by Yen-Chi Chen
- Chapters 1-4 of Introduction to Smooth Manifold by John M. Lee
- Video lectures on Manifold Learning by Marina Meila Video Lecture 1, Video Lecture 2, Video Lecture 3, annotated slides from Lecture 1 and from Lectures 2-3, and unannotated Lecture slides with additional definitions and notes
- Manifold learning: what, how and why by Marina Meila and Hanyu Zhang
- Marina Meila
- Yen-Chi Chen
- Yikun Zhang
- Jerry Wei
- Yidan Xue
- James Buenfil
- Alex Kokot
- Weicheng Wu
- Vlad Murad
- Yu-Chia Chen
- Hanyu Zhang
- Samson Koelle
- Zhenman Yuan
- Alon Milchgrub
- 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