Upcoming Events

The synthetic analogue of the bar comonad controls the universal differentials in the bar spectral sequence of algebras over spectral operads. This can be viewed as a deformation of Koszul duality of such algebras. I will explain ongoing work with...

Let $G$ be a graph with $n$ vertices. Let $A(G)$ be its adjacency matrix. Let $\lambda_1(G), \lambda_2(G)$ denote the largest and second largest eigenvalues of the adjacency matrix. Bollob\'{a}s and Nikiforov (2007) conjectured that for any graph $G...

In topology, cohomology is an invariant we can assign to spaces. In algebra, there are also cohomology theories for various algebraic structures, such as group cohomology, Lie algebra cohomology, and André-Quillen cohomology of commutative rings...

An $H$-intersecting family of 3-uniform hypergraphs on $n$ labelled vertices is a family of hypergraphs $\mathcal{F}$ such that for any pair of hypergraphs $G_1, G_2 \in \mathcal{F}$, the intersection $G_1 \cap G_2$ contains a copy of $H$ as a...

Are you in the field of AI, looking to enhance your career, network with like-minded professionals, and contribute to a more inclusive AI community? Look no further! Join us for an exciting event designed just for you. The Women in AI Career Fair...

Let us ask: What is a "strong" matrix property and why is such a property useful for studying eigenvalues of graphs? During this talk we will attempt to address both questions above by first wandering through some of the developed strong matrix...

In 1973, assuming the Riemann hypothesis (RH), Montgomery studied the vertical distribution of zeta zeros, and conjectured that they behave like the eigenvalues of some random matrices. We will discuss some models for zeta zeros - starting from the...

TBA

Single particle cryo-EM is an increasingly popular technique for determining 3-D molecular structures at high resolution. The 2017 Nobel Prize in Chemistry was awarded to three of the pioneers of cryo-EM, and already in the early stages of the global...

Operator learning is an emerging field at the intersection of machine learning, physics, and mathematics, that aims to discover properties of unknown physical systems from experimental data. Popular techniques exploit the approximation power of deep...