Seminars and Colloquia by Series

Ergodic Averages over Shrinking Sectors of Z[i]

Series
Analysis Seminar
Time
Wednesday, October 7, 2026 - 14:00 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Christina Giannitsi – Virginia Tech

We are going to talk about  ergodic averages over Gaussian integers in angular sectors whose widths shrink with the norm cutoff. For finitely generated multiplicative actions, we prove a uniform sector–disk comparison with an explicit logarithmic shrinking range and a power-saving error, without assuming ergodicity or angular distribution of the underlying prime transformations. We then apply strong unique ergodicity to obtain convergence to the invariant integral. Our results also yield asymptotic independence of angular position and the orbit, Gaussian Liouville cancellation, and joint equidistribution of prime-factor counts associated with distinct prime classes.

The existence of dynamic stars in general relativity: Local well-posedness for the Einstein-Euler system with a physical vacuum boundary in spherical symmetry

Series
PDE Seminar
Time
Tuesday, October 6, 2026 - 14:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Marcelo Disconzi – Vanderbilt University

Astronomy is arguably the oldest scientific discipline. Precise measurements of the motion of celestial bodies date back to the ancient Babylonians, Chinese, Greeks, and indigenous peoples outside Eurasia. Starting in the 19th century, systematic applications of physical principles to the formation and dynamics of stars marked the birth of astrophysics as a subfield of physics. Present-day astrophysics employs an array of theoretical and observational tools to construct sophisticated and predictive models of the origin, evolution, and death of stars.

While stars can be largely described within Newtonian physics, some of their most interesting properties, such as bounds on their mass-radius ratio, their potential collapse into a black hole, or effects of viscosity on gravitational waves emitted by mergers of neutron stars, can only be studied via applications of general relativity. Moreover, as a matter of principle, we ought to be able to fully understand stars as general-relativistic phenomena. The mathematical treatment of stars within general relativity, however, has lagged behind. Little progress has been made on this front since the discovery of the Tolman-Oppenheimer-Volkoff (TOV) equations and the Oppenheimer-Snyder solution in the late 1930s. The TOV equations describe a static (i.e., time independent), perfectly spherically symmetric star, whilst the latter describes the collapse of a perfectly spherically symmetric star with no pressure into a black hole. Despite being landmark results in general relativity, both situations are highly idealized. Inferences about generic properties of general-relativistic stars derived from such models are, therefore, a priori unjustified.

In this talk, I will discuss the problem of formulating a sound mathematical theory of general-relativistic star evolution based on the Einstein-Euler system. After setting up the problem, I will explain its main challenges, but also discuss the rich physics and mathematics involved in its study. A fundamental difficulty involves understanding the mathematics of the fluid-vacuum interface which separates the body of the star from vacuum. This interface displays singular behavior which is not amenable to current mathematical techniques. This difficulty, however, can be circumvented if we consider stars that are spherically symmetric but not static. The resulting evolution problem corresponds to a dynamic (i.e., time-dependent) generalization of the TOV equations.

This is joint work with Jared Speck.

Stability of large cuts in random graphs

Series
Combinatorics Seminar
Time
Friday, October 2, 2026 - 15:15 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
IIay Hoshen – Tel Aviv University –
We prove that the family of largest cuts in the binomial random graph exhibits the following stability property: If $1/n \ll p \leq 1-\Omega(1)$, then, with high probability, there is a set of $n - o(n)$ vertices that is partitioned in the same manner by all maximum cuts of $G_{n, p}$. Moreover, the analogous statement remains true when one replaces maximum cuts with nearly-maximum cuts.
 
We then demonstrate how one can use this statement as a tool for showing that certain properties of $G_{n, p}$ that hold in a fixed balanced cut hold simultaneously in all maximum cuts. We provide two example applications of this tool. In this talk, we show that maximum cuts in $G_{n, p}$ typically partition the neighbourhood of every vertex into nearly equal parts; this resolves a conjecture of DeMarco and Kahn for all but a narrow range of densities $p$. We will also mention another application regarding sharp thresholds in Turán type problems.
 
This is joint work with Wojciech Samotij and Maksim Zhukovskii.

Swarm-Based Gradient Descent: A Multi-Agent Approach to Non-Convex Optimization (special Friday time)

Series
School of Mathematics Colloquium
Time
Friday, October 2, 2026 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Eitan Tadmor – University of Maryland

We discuss a novel class of swarm-based gradient descent (SBGD) methods for nonconvex optimization. Each agent in the swarm is characterized by its position and mass. 

The dynamics combines two mechanisms: persistent transfer of mass from agents positioned on “higher ground” to those with lower objective values, and a mass-dependent time-stepping protocol. This coupling creates a dynamic distinction between “leaders” and “explorers.” Heavier agents act as leaders, use small time steps to refine promising regions near local minima, while light agents take larger steps, exploring the landscape for lower objective values. The swarm dynamics adaptively balances exploitation of local refinement with global exploration. 

We present convergence results and numerical experiments illustrating the effectiveness of SBGD for global optimization.

Exponential Rank Bounds for Random Matrices

Series
Stochastics Seminar
Time
Thursday, October 1, 2026 - 15:30 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Achintya Polavarapu – Georgia Tech –

A basic question in random matrix theory is how likely a matrix is to have a large rank. For many classical models, this probability decays extremely quickly, but the strongest results often rely on the entries being identically distributed. In this talk, I will discuss how to obtain the same exponential scale for matrices with fully independent, non-identically distributed entries under only a uniform anti-concentration assumption and explain the main ideas that make this possible.

Smallest Singular Values of Weighted Random Matrices

Series
Analysis Seminar
Time
Wednesday, September 30, 2026 - 14:00 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Achintya Raya Polavarapu – Georgia Institute of Technology

How close is a random matrix to being singular? This question measures the stability of random linear systems. Classical models treat every direction equally, but in many settings different directions have different scales or variances. We study what happens when a random matrix is deformed by a fixed invertible matrix. Even a diagonal deformation raises a basic question: is stability controlled by the weakest direction, or by the collective geometry of all directions? I will explain why standard estimates miss this geometry and how random subspaces lead to sharper bounds.

Resolution of the Detection Threshold Conjecture for Sparse Random Geometric Graphs

Series
Probability Working Seminar
Time
Tuesday, September 29, 2026 - 15:30 for 1.5 hours (actually 80 minutes)
Location
Skiles 006
Speaker
Cheng Mao – Georgia Tech –

(second of two talks; the first was on Sep. 22)

A random geometric graph (RGG) is generated by first sampling $n$ latent points independently and uniformly from the unit sphere in $R^d$, and then connecting each pair of points if their inner product exceeds a threshold. We study the sharp detection threshold---the largest dimension at which the RGG can be statistically distinguished from the Erdős--Rényi graph with the same edge density $p$. This threshold is conjectured to be $d \asymp (n h(p))^3$, where $h(p)$ is the binary entropy function. Previous works proved this conjecture for dense graphs with constant $p$ and, up to polylogarithmic factors, very sparse graphs with constant average degrees. In this series of two talks, I will discuss a resolution of this conjecture. This is based on joint work with Hang Du, Nike Sun, Yihong Wu, and Jiaming Xu.

Asymptotic stability of the degree-one vortex in the 2D abelian Yang-Mills-Higgs model under equivariant perturbations

Series
PDE Seminar
Time
Tuesday, September 29, 2026 - 14:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Jonas Lührmann – University of Cologne –

The abelian Yang-Mills-Higgs model on (1 + 2)-dimensional Minkowski space is a classical relativistic field theory, where a scalar complex field is coupled to an electromagnetic field. It admits topological soliton solutions called vortices. We present a proof of the asymptotic stability of the degree-one vortex under equivariant perturbations in the self-dual case.

Ribbon minimal knots and links

Series
Geometry Topology Seminar
Time
Monday, September 28, 2026 - 14:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Gary Dunkerley – UGA

It has been observed that the relationship of ribbon concordance induces monotonic behavior across several link invariants. 

Resolving a famous open question of Gordon, Agol unified these observations by showing that ribbon concordance induces a partial order on knots in the 3-sphere.

Agol's argument can be extended to show that ribbon concordance likewise induces a partial order on arbitrary links in the 3-sphere.

Perhaps motivated by the slice-ribbon conjecture, it is natural and interesting to ask which knots and links are minimal with respect to this partial order. 

Leveraging recent developments in link Floer homology, I will share new examples of ribbon minimal knots and links. 

Some of this work is joint with Jaewon Lee and Alessio Di Prisa.

Inference-Time Learning Through Context

Series
Applied and Computational Mathematics Seminar
Time
Monday, September 28, 2026 - 14:00 for 1 hour (actually 50 minutes)
Location
Skiles 005 and https://gatech.zoom.us/j/94954654170
Speaker
Zhihui Zhu – Ohio State University –

Large language models exhibit a remarkable ability to learn and adapt from context without updating their parameters. In this talk, I will present our recent work on both understanding how foundation models extract task information from context and designing contexts that enable continual improvement during inference. I will first discuss a geometric analysis on in-context learning, providing new insights into how task representations emerge and evolve across layers. I will then discuss how these insights motivate a broader paradigm of inference-time learning, in which context is actively constructed rather than passively consumed. Building on iterative refinement frameworks such as AlphaEvolve, we view context as an evolving memory that stores hypotheses, intermediate solutions, and feedback. Drawing inspiration from optimization and sequential Monte Carlo, we develop principled approaches for designing and updating context over time. Overall, understanding how models read context and how we can systematically write and evolve context may provide a foundation for the next generation of adaptive AI systems.

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