Georgia Institute of Technology College of Sciences

Similar to how matroids can be viewed as a combinatorial abstraction of linear subspaces, a flag matroid can be viewed as a combinatorial abstraction of a nested sequence of linear subspaces. In this talk, we will discuss forbidden minor results that describe precisely when a flag matroid is representable and when it is graphic. 

Vanishing cycles of Lefschetz fibrations give examples of Lagrangian spheres in the fiber. A natural question, first raised by Donaldson, is whether all Lagrangian spheres arise this way. We focus on this problem for positive rational surfaces, which were shown to admit a geometric structure called almost toric fibrations. I will talk about a work-in-progress showing all Lagrangian spheres here are visible in an almost toric fibration and thus are vanishing cycles of a nodal degeneration.

Machine learning (ML) has achieved unprecedented empirical success in diverse applications. It now has been applied to solve scientific and engineering problems, which has become an emerging field, Scientific Machine Learning (SciML). However, many ML techniques are highly complex and sophisticated, often requiring extensive trial-and-error experimentation and specialized problem-dependent tricks to implement effectively. This complexity frequently leads to significant challenges, such as reproducibility and rigorness, for scientific research. This talk explores mathematical approaches, offering more principled and reliable methodologies in SciML. The first part will present recent efforts advancing the predictive power of physics-informed machine learning through robust training/optimization methods. This includes an effective training method for multivariate neural networks, namely, Active Neuron Least Squares (ANLS) and a two-step training method for deep operator networks. The second part is about how to embed the first principles of physics into neural networks. I will present a general framework for designing NNs that obey the first and second laws of thermodynamics. The framework not only provides flexible ways of leveraging available physics information but also results in expressive NN architectures. I will also present an intriguing phenomenon of this framework when it is applied in the context of latent space dynamics identification where a correlation appears between an entropy production rate in the latent space and the behaviors of the full-state solution.

Let $G = (V,E)$ be a graph on $n$ vertices, and let $c : E \to P$, where $P$ is a set of colors. Let $\delta^c(G) = \min_{v \in V} \{ d^{c}(v) \}$ where $d^c(v)$ is the number of colors on edges incident to a vertex $v$ of $G$.  In 2011, Fujita and Magnant showed that if $G$ is a graph on $n$ vertices that satisfies $\delta^c(G)\geq n/2$, then for every two vertices $u, v$ there is a properly-colored $u,v$-path in $G$. We show that for sufficiently large graphs $G$ the same bound for $\delta^c(G)$ implies that any two vertices are connected by a rainbow path. This is joint work with Andrzej Czygrinow.

An important class of problems at the intersection of harmonic analysis and geometric measure theory asks how large the Hausdorff dimension of a set must be to ensure that it contains certain types of geometric point configurations. We apply these tools to study configurations associated to the problem of bounding the VC-dimension of a naturally arising class of indicator functions on fractal sets.

One way to understand the concentration of the norm of a random matrix X with Gaussian entries is to apply a standard concentration inequality, such as the one for Lipschitz functions of i.i.d. standard Gaussian variables, which yields subgaussian tail bounds on the norm of X. However, as was shown by Tracy and Widom in 1990s, when the entries of X are i.i.d. the norm of X exhibits even sharper concentration. The phenomenon of a function of many i.i.d. variables having strictly smaller tails than those predicted by classical concentration inequalities is sometimes referred to as «superconcentration», a term originally dubbed by Chatterjee. I will discuss novel results that can be interpreted as superconcentration inequalities for the norm of X, where X is a Gaussian random matrix with independent entries and an arbitrary variance profile. We can also view our results as a nonhomogeneous extension of Tracy-Widom-type upper tail estimates for the norm of X.

Abstract: Critical points significantly affect the behavior of gradient-based dynamics. Numerous works have been done for global minima of neural networks. Thus, the recent work characterizes non-global critical points. With the idea that gradient-based methods of neural networks favor “simple models”, this work focuses on the set of low-complexity critical points, i.e., those representing underparameterized network models. Specifically, we investigate: i) the existence and ii) geometry of such sets, iii) the output functions they represent, iv) saddles in them. The talk will discuss these results based on a simple example. The general theorems will also be included. No specific knowledge in neural networks is required. 

In this talk, the speaker will present three applications of information theory in applied spaces. No background on information theory, hypergraphs, or RF signals analysis will be assumed. Bill Kay is a pure mathematician in combinatorics by training who now lives in an applied space at Pacific Northwest National Laboratory.