Past Events

In this talk we will first discuss this soon to be 100 years old conjecture, which states that the set of primes for which an integer \(a\) different from \(-1\) or a perfect square is a primitive root admits an asymptotic density among all primes...

The c2 invariant is an arithmetic graph invariant related to quantum field theory. It is defined in terms of a point count of the Kirchhoff polynomial of a graph and I will present a combinatorial technique for computing the c2 invariant by counting...

We study the effect of noise on dynamics of a single spike for the classical Gierer--Meinhardt model on a finite interval. When spatio-temporal noise is introduced in the equation for the activator, we derive a stochastic ODE that describes the...

We study a mean-field spin glass model whose coupling distribution has a power-law tail with exponent \alpha \in (0, 2). This is known as Levy spin glasses in literature. Though it is a fully connected model, many of its important characteristics are...

This talk will begin with a study on explicit bounds for ψ(x) starting with the work of Rosser in 1941. It will also cover various improvements over the years including the works of Rosser and Schoenfeld, Dusart, Faber-Kadiri, Platt-Trudgian, Büthe...

Tulips have captivated human interest for centuries, with their vibrant colors and unique shapes. Particularly striped tulips have been highly popular, leading to the “tulipomania” in the Dutch Golden Age. But how do the tulips get their stripes...

The SL_2 Kakeya problem is a special case of the three-dimensional Kakeya conjecture, and it is surprisingly connected to the restricted projection problem in geometric measure theory. In this talk, I will first discuss a proof of SL_2 Kakeya problem...

In the 1960s Shanks conjectured that the $\zeta(\rho)$, where $\rho$ is a non-trivial zero of zeta, is both real and positive in the mean. Conjecturing and proving this result has a rich history, but efforts to generalise it to higher moments have so...

Modern machine learning (ML) applications grapple with the challenges posed by high-dimensional datasets and high-dimensional parameter spaces. Stochastic gradient descent (SGD) and its variants have emerged as the go-to algorithms for optimizing in...

Given a connected undirected graph G = (V,E), where V is the set of vertices and E the set of edges (possibly with weights), denote by A the associated adjacency matrix. The Kemeny constant of the graph G is defined as the Kemeny constant of the...