Temporal Data Analysis Using Category Theory and the Hidden Connection Between Persistence and Accumulation:

In this seminar, Wilmer Leal presents an innovative approach to temporal data analysis using category theory. Through tools like pullbacks, pushouts, and sheaves, he introduces a formal framework that models not only data at different time steps but also the transformations between them. This enables the identification of patterns that persist and accumulate over time.

With applications in computational chemistry, engineering, and data science, the talk explores how the categorical language can unify and structure diverse forms of temporal data in a rigorous and flexible way. The seminar also includes practical examples and insights into implementing these ideas in environments like AlgebraicJulia.