TBA by Parker Evans
- Series
- Geometry Topology Seminar
- Time
- Monday, March 9, 2026 - 14:00 for 1 hour (actually 50 minutes)
- Location
- Skiles 006
- Speaker
- Parker Evans – Washington University of St. Louis
Please Note: There will be a pre-seminar at 10:55-11:25 in Skiles 005.
TBA
We study the almost sure convergence of the Stochastic Approximation algorithm to the fixed point $x^\star$ of a nonlinear operator under a negative drift condition and a general noise sequence with finite $p$-th moment for some $p > 1$. Classical almost sure convergence results of Stochastic Approximation are mostly analyzed for the square-integrable noise setting, and it is shown that any non-summable but square-summable step size sequence is sufficient to obtain almost sure convergence. However, such a limitation prevents wider algorithmic application. In particular, many applications in Machine Learning and Operations Research admit heavy-tailed noise with infinite variance, rendering such guarantees inapplicable. On the other hand, when a stronger condition on the noise is available, such guarantees on the step size would be too conservative, as practitioners would like to pick a larger step size for a more preferable convergence behavior. To this end, we show that any non-summable but $p$-th power summable step size sequence is sufficient to guarantee almost sure convergence, covering the gap in the literature.
Our guarantees are obtained using a universal Lyapunov drift argument. For the regime $p \in (1, 2)$, we show that using the Lyapunov function $\|x-x^\star\|^p$ and applying a Taylor-like bound suffice. For $p > 2$, such an approach is no longer applicable, and therefore, we introduce a novel iterate projection technique to control the nonlinear terms produced by high-moment bounds and multiplicative noise. We believe our proof techniques and their implications could be of independent interest and pave the way for finite-time analysis of Stochastic Approximation under a general noise condition. This is a joint work with Quang D. T. Nguyen, Duc Anh Nguyen, and Prof. Siva Theja Maguluri.
Many problems in number theory boil down to bounding the size of a set contained in a certain set of residue classes mod $p$ for various sets of primes $p$; and then sieve methods are the primary tools for doing so. Motivated by the inverse Goldbach problem, Green–Harper, Helfgott–Venkatesh, Shao, and Walsh have explored the inverse sieve problem: if we let $S \subseteq [N]$ be a maximal set of integers in this interval where the residue classes mod $p$ occupied by $S$ have some particular pattern for many primes $p$, what can one say about the structure of the set $S$ beyond just its size? In this talk, I will give a gentle introduction to inverse sieve problems, and present some progress we made when $S$ mod $p$ has rich additive structure for many primes $p$. In particular, in this setting, we provide several improvements on the larger sieve bound for $|S|$, parallel to the work of Green–Harper and Shao for improvements on the large sieve. Joint work with Ernie Croot and Junzhe Mao.
In this talk, we will discuss the construction of exotic 4-manifolds using Lefschetz fibrations over S^2, which are obtained by finite order cyclic group actions on Σg. We will first apply various cyclic group actions on Σg for g>0, and then extend it diagonally to the product manifolds ΣgxΣg. These will give singular manifolds with cyclic quotient singularities. Then, by resolving the singularities, we will obtain families of Lefschetz fibrations over S^2. Following the resolution process, we will determine the configurations of the singular fibers and the monodromy of the total space. In some cases, deformations of the Lefschetz fibrations give rise to nice applications using the rational blow-down operation, which provides exotic examples. This is a joint work with A. Akhmedov and M. Bhupal.
TBA
The degeneracy of a graph is a measure of sparseness that appears in many contexts throughout graph theory. In extremal graph theory, it is known that graphs of bounded degeneracy have Ramsey number which is linear in their number of vertices (Lee, 2017). Also, the degeneracy gives good bounds on the Turán exponent of bipartite graphs (Alon--Krivelevich--Sudokav, 2003). Extending these results to hypergraphs presents a challenge, as it is known that the naïve generalization of these results -- using the standard notion of hypergraph degeneracy -- are not true (Kostochka--Rödl 2006). We define a new measure of sparseness for hypergraphs called skeletal degeneracy and show that it gives information on both the Ramsey- and Turán-type properties of hypergraphs.
Based on joint work with Jacob Fox, Maya Sankar, Michael Simkin, and Yunkun Zhou
I'll review a few known results about quantum graphs that maximize or minimize eigenvalues or combinations of eigenvalues, and will then concentrate on ratios of eigevalues under topological constraints on the graph. In particular a new discovery with James Kennedy and Gabriel Ramos is that the largest ratio of the first two eigenvalues of the Laplacian on a finite tree graph with Dirichlet conditions at the ends is achieved by equilateral stars. Some related, Weyl-sharp estimates of arbitrary eigenvalue ratios can be obtained using similar ideas. If time permits, I will also describe some optimal results about differences and other combinations of eigenvalues.
Each year, millions complete brackets to predict the outcomes of the NCAA men’s and women’s basketball tournaments—an activity centered on a fundamental question in sports analytics: Who is number one? Ranking algorithms provide mathematical frameworks for addressing this question and are widely used in postseason selection and predictive modeling.
This talk examines two influential rating systems—the Colley Method and the Massey Method—both of which compute team rankings by solving systems of linear equations based on game outcomes. We discuss extensions that incorporate factors such as late-season momentum and home-field advantage, and we evaluate their impact on predictive performance.
Applications across sports, including basketball and soccer, will be presented, with particular attention to NCAA tournament bracket construction. Research-driven implementations of these methods have produced brackets that outperformed over 90% of millions of ESPN submissions. The talk concludes with open questions and broader applications of ranking methodology.
Bio:
Bio: Dr. Tim Chartier is the Joseph R. Morton Professor of Mathematics and Computer Science at Davidson College, where he specializes in data analytics. He has consulted with ESPN, The New York Times, the U.S. Olympic & Paralympic Committee, and teams in the NBA, NFL, MLB, and NASCAR. He founded and grew a sports analytics group to nearly 100 student researchers annually. The group, now student-run, provides analytics for Davidson College athletic teams.
His scholarship and leadership have been recognized nationally through service in the Mathematical Association of America (MAA) and with multiple honors, including an Alfred P. Sloan Research Fellowship, the MAA Southeastern Section Distinguished Teaching Award, and the MAA’s Euler Book Prize. He has also collaborated with educational initiatives at Google and Pixar and served as the 2022–23 Distinguished Visiting Professor at the National Museum of Mathematics.