Doodling is a creative and fundamentally human activity, resulting in doodles iwth intricate and often hidden implicit structure. We will treat doodles as an example for how mathematics is done - by starting with some doodles, we will ask ourselves some natural questions and see where they take us. They will lead us to some unexpected places, and to some sophisticated mathematics.

Victor H. Moll is a professor of mathematics at Tulane University. During the process of learning calculus one observes that there is a well-defined list of rules to compute derivatives: product, quotient, and chain rules are among the first taught in every class. On the other hand, when one tries... Continue reading...

Professor Zixuan Cang, North Carolina State University Title: "Optimal Transport and Topological Data Analysis for single-cell biology" Abstract: Single-cell and spatial transcriptomics data examines high-throughput gene expression profiles at fine resolutions providing an unprecedented opportunity to elucidate the underlying complex biological processes. Optimal transport and Topological Data Analysis has proven to be an effective tool... Continue reading...

Nonlinear polynomial equations naturally arise throughout mathematics, science, and engineering with their solutions describing various phenomena including the motion of a mechanical linkage such as a robotic arm and steady states of a dynamical system arising from a biochemical reaction network. Polynomials are central to some of the deepest mathematics and have been studied for millennia.

The Census is our most fundamental tool for measuring who lives in the United States, and where. Ever since the founding of the country, the categories have reflected how Americans think about ourselves, and the data gets used for everything from funding allocations to political districting. So, it might be surprising that the most recent Decennial Census included intentional injections of random numbers to noise the data for privacy protection. This was hugely controversial! In this talk, Moon Duchin will explain some of the history and the impacts of how we enumerate the country.

This talk explores how to construct meaningful features from noisy, high-dimensional data by leveraging geometric and invariant structures. First, we introduce a geometric framework for dimension reduction using a power-weighted path metric, which effectively de-noises high-dimensional data while preserving its intrinsic geometric structure. This framework is particularly useful for analyzing single-cell RNA data and for multi-manifold clustering, and we provide theoretical guarantees for the convergence of the associated graph Laplacian operators.