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About
President
Governance
Partner Institutions
Visit
People
Management
Faculty
Postdocs
Visiting Scholars
Administration
Academic Support
Research
Research Groups
Courses
Seminars
Join Us
Faculty
Postdocs
Students
Events
Conferences
Workshops
Forum
Life @ BIMSA
Accommodation
Transportation
Facilities
Tour
News
News
Announcement
Downloads
Qiuzhen College, Tsinghua University
Yau Mathematical Sciences Center, Tsinghua University (YMSC)
Tsinghua Sanya International  Mathematics Forum (TSIMF)
Shanghai Institute for Mathematics and  Interdisciplinary Sciences (SIMIS)
BIMSA > BIMSA Topology Seminar Persistent Symmetries in Data Science
Persistent Symmetries in Data Science
Organizers
Matthew Burfitt , Jingyan Li , Jie Wu
Speaker
Jian Liu
Time
Thursday, November 6, 2025 2:00 PM - 3:00 PM
Venue
A3-4-101
Online
Zoom 928 682 9093 (BIMSA)
Abstract
Symmetry is a fundamental concept in understanding both natural phenomena and mathematical structures. This topic develops a comprehensive theory for analyzing the persistent symmetries and degrees of asymmetry in finite point configurations within metric spaces. Using category theory and span categories, we define persistent symmetry groups and introduce new invariants, namely symmetry barcodes and polybarcodes, which capture the birth, death, persistence, and reappearance of symmetries as parameters evolve. We establish metrics and stability theorems for these invariants. The concept of symmetry types is formalized through the action of isometry groups on configuration spaces. To quantitatively characterize symmetry and asymmetry, we introduce measures such as the degree of symmetry and symmetry defect, the latter establishing connections with approximate group theory in Euclidean spaces.
Beijing Institute of Mathematical Sciences and Applications
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