When: 27 January, 2022, 17:00-18:30 UTC+1.
Recording: here
Abstract: This presentation will be divided into two talks.
First speaker: Anna Seigal
Title: Algebraic Structure in Data
Abstract: I will give an overview of how algebra can be used to reveal structures in data. I will describe the role of matrix and tensor decompositions, focusing on some examples from biology, statistics, and the study of signature tensors of paths.
Second speaker: Neriman Tokcan
Title: Algebraic Methods for Tensor Data
Abstract: We will discuss how algebraic tools can be utilized to study/analyze multimodal data. We will borrow tools from algebraic geometry and invariant theory to tackle important problems, such as the “curse of dimensionality”, missing data, and noise. We will go over some promising applications of tensor-based frameworks in cancer genomics and drug repurposing. We will discuss multimodal, high-dimensional datasets in biology with open questions in these areas.
