Georgia Institute of Technology College of Sciences

This talk will be an introduction to the theory of surfaces, some tools we use to study surfaces, and some uses of surfaces in "real life". In particular, we will discuss the mapping class group and the curve complex. This talk will be aimed at an audience with a minimal background in low-dimensional topology. 

Suppose f is a Laurent polynomial in n variables with degree d, exactly (n+2) monomial terms, and all its coefficients in {-H,...,H} for some positive integer H. Suppose further that the exponent vectors of f do not all lie in an affine hyperplane: Such a set of exponent vectors is referred to as a circuit. We prove that the positive zero set of f is isotopic to the real zero set of an explicit n-variate quadric q, and give a fast algorithm to explicitly compute q: The bit complexity is (log(dH))^O(n). The best previous bit-complexity bounds were of the form (dlog(H))^{\Omega(n)} (to compute a data structure called a roadmap). Our results also extend to real zero sets of n-variate exponential sums over circuits. Finally, we discuss how to approach the next case up: n-variate polynomials with exactly (n+3) terms.

Ion channels are a class of proteins embedded in biological membranes, acting as biological devices or 'valves' for cells and playing a critical role in controlling various biological functions. To compute macroscopic ion channel kinetics, such as Gibbs free energy, electric currents, transport fluxes, membrane potential, and electrochemical potential, Poisson-Nernst-Planck ion channel (PNPIC) models have been developed as systems of nonlinear partial differential equations. However, they are difficult to solve numerically due to solution singularities, exponential nonlinearities, multiple physical domain issues, and the requirement of ionic concentration positivity. In this talk, I will present the recent progress we made in the development of finite element methods for solving PNPIC models. Specifically, I will introduce our improved PNPIC models and describe the mathematical and numerical techniques we utilized to develop efficient finite element iterative methods. Additionally, I will introduce the related software packages we developed for a voltage-dependent anion-channel protein and a mixture solution of multiple ionic species. Finally, I will present numerical results to demonstrate the fast convergence of our iterative methods and the high performance of our software package. This work was partially supported by the National Science Foundation through award number DMS-2153376 and the Simons Foundation through research award 711776.

The fine curve graph of a surface S was introduced by Bowden–Hensel–Webb in 2019 to study the diffeomorphism group of S. We consider a variant of this graph, called the fine 1-curve graph, whose vertices are essential simple closed curves and edges connect curves that intersect in at most one point. Building on the works of Long–Margalit–Pham–Verberne–Yao and Le Roux–Wolff, we show that the automorphism group of the fine 1-curve graph is isomorphic to the homeomorphism group of S. This is joint work with Katherine W. Booth and Daniel Minahan.

We study the global existence of classical solutions to the incompressible viscous MHD system without magnetic diffusion in 2D and 3D. The lack of resistivity or magnetic diffusion poses a major challenge to a global regularity theory even for small smooth initial data. However, the interesting nonlinear structure of the system not only leads to some significant challenges, but some interesting stabilization properties, that leads to the possibility of the theory of global existence of classical and/or strong solutions. This talk is based on joint works with Yi Zhou, Yi Zhu, Shijin Ding, Xiaoying Zeng, and Jingchi Huang.

I’ll present a quantitative version of a stability estimate
for the Sobolev Inequality improving previous results of Bianchi
and Egnell. The estimate has the correct dimensional dependence
which leads to a stability estimate for the Logarithmic Sobolev inequality.
This is joint work with Dolbeault, Esteban, Figalli and Frank.

In the early 80's, Freedman discovered that the Whitney trick could be performed in 4-dimensions which quickly led to a complete classification of closed, simply connected topological 4-manifolds. With gauge theory, Donaldson showed that 4-manifolds differ greatly from their higher dimensional counterparts which uncovered the stark differences between topological and smooth results in dimension 4. In this introductory talk, we will give a brief overview this classification and why dimension 4 is so unique. Then, we will describe handlebody decompositions of 4-manifolds and draw several Kirby pictures representing some basic 4-mfds.

Computing integrals against a high-dimensional posterior is the major computational bottleneck in Bayesian inference. A popular technique to reduce this computational burden is to use the Laplace approximation, a Gaussian distribution, in place of the true posterior. Despite its widespread use, the Laplace approximation's accuracy in high dimensions is not well understood.  The body of existing results does not form a cohesive theory, leaving open important questions e.g. on the dimension dependence of the approximation rate. We address many of these questions through the unified framework of a new, leading order asymptotic decomposition of high-dimensional Laplace integrals. In particular, we (1) determine the tight dimension dependence of the approximation error, leading to the tightest known Bernstein von Mises result on the asymptotic normality of the posterior, and (2) derive a simple correction to this Gaussian distribution to obtain a higher-order accurate approximation to the posterior.

In this talk, we present recent results on the geometry of centrally-symmetric random polytopes generated by N independent copies of a random vector X. We show that under minimal assumptions on X, for N>Cn, and with high probability, the polytope contains a deterministic set that is naturally associated with the random vector - namely, the polar of a certain floating body. This solves the long-standing question on whether such a random polytope contains a canonical body. Moreover, by identifying the floating bodies associated with various random vectors we recover the estimates that have been obtained previously, and thanks to the minimal assumptions on X we derive estimates in cases that had been out of reach, involving random polytopes generated by heavy-tailed random vectors (e.g., when X is q-stable or when X has an unconditional structure). Finally, the structural results are used for the study of a fundamental question in compressive sensing - noise blind sparse recovery. This is joint work with Olivier Guédon (University of Paris-Est Marne La Vallée), Christian Kümmerle (UNC Charlotte), Shahar Mendelson (Sorbonne University Paris), and Holger Rauhut (LMU Munich).

Bio: Felix Krahmer received his PhD in Mathematics in 2009 from New York University under the supervision of Percy Deift and Sinan Güntürk. He was a Hausdorff postdoctoral fellow in the group of Holger Rauhut at the University of Bonn, Germany from 2009-2012. In 2012 he joined the University of Göttingen as an assistant professor for mathematical data analysis, where he has been awarded an Emmy Noether Junior Research Group. From 2015-2021 he was assistant professor for optimization and data analysis in the department of mathematics at the Technical University of Munich, before he was tenured and promoted to associate professor in 2021. His research interests span various areas at the interface of probability, analysis, machine learning, and signal processing including randomized sensing and reconstruction, fast random embeddings, quantization, and the computational sensing paradigm.