Fall 2024 Schedule

• (10/21, 5-6 PM) James Buenfil will give his Practice General Exam presentation with the title "Asymmetric Canonical Correlation Analysis of Riemannian and High-Dimensional Data". Paper link Hybrid in CS&SS Conference Room (Padelford Hall lower level) or via Zoom link here

• (11/04, 5-6 PM) Tony Zeng (PhD student in Mathematics) will give a presentation about "Zigzag Persistence and Fractals". Hybrid in CS&SS Conference Room (Padelford Hall lower level) or via Zoom link here

• (11/18, 5-6 PM) Paizhe Xie will give a presentation about "Estimation and Quantization of Expected Persistence Diagrams". Hybrid in CS&SS Conference Room (Padelford Hall lower level) or via Zoom link here

• (12/02, 5-6 PM) Vlad Murad will present his research work. 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 Riemannian manifolds

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, James Buenfil, Yikun Zhang

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

The following is an incomplete list of active members of the group.

• Marina Meila
• Yen-Chi Chen
• Yikun Zhang
• James Buenfil
• Alex Kokot
• Tony Zeng
• Paizhe Xie
• Wenyu Bo
• Woorim Lee
• Yanjiao Yang
• Vlad Murad

Former GDA group members

• Yu-Chia Chen
• Hanyu Zhang
• Samson Koelle
• Jerry Wei
• Yidan Xu
• Zhenman Yuan
• Weicheng Wu
• 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