Why Topology Matters for Data Science and AI

Topology offers a way to organize complex data by looking for shape, structure, and relationships. Yusu Wang, Ph.D., UC San Diego, and Gunnar E. Carlsson, Ph.D., Stanford University, discuss how ideas from algebraic topology move from pure mathematics into data science, machine learning, and AI. Carlsson explains why topological data analysis helps identify meaningful groups in large data sets, how examples and accessible tools can make abstract math useful, and why interpretability matters as AI systems grow more complex. The conversation connects collaboration across fields, customer-focused entrepreneurship, and the need to train mathematicians and AI researchers to help formulate real-world problems. Their exchange shows how mathematical thinking can make powerful technologies more understandable, usable, and accountable. Series: "Data Science Channel" [Science] [Show ID: 41487]