C3

Theta operators are weight-shifting differential operators on  automorphic forms. They play an important role in studying congruences between Hecke eigenforms and their p-adic variation. For instance, the classical theta operator, which acts on q-expansions of modular forms as q·(d/dq), is used crucially in Edixhoven’s proof of the weight part of Serre’s conjecture, Katz’s construction of p-adic L-functions over CM fields, and Coleman’s classicality theorem.

Recent years have witnessed extensive works on understanding theta operators over general Shimura varieties, from both geometric and representation-theoretic perspectives. In this talk, I will hint at some aspects of this fascinating area of research. If time permits, I will discuss my ongoing work on overconvergent theta operators over Siegel Shimura varieties.

We will begin with an overview of Stark's conjectures before discussing the case of imaginary quadratic fields, covering both the limit formula and the existence of elliptic units. The classical expositions of these are at times lacking in intuition, but thanks to Kato's deep insights 20 years ago, we can present more geometric and illuminating proofs of both results.

Fix an Artin representation rho. Work in progress by Emmanuel Lecouturier and Loïc Merel claims that the special values L(f,rho,1) for certain modular forms f see some global data related to the L-function attached to rho. We first give a brief exposition on Mazur’s Eisenstein ideal, which lies at the heart of their work. We then describe this conjectural phenomenon in a few simple cases, the last being related to a conjecture of Harris and Venkatesh.

Existential decidability of a ring is the question as to whether an algorithm exists which determines whether a given system of polynomial equations and inequations has a solution. It is a classical result (``Hilbert's 10th problem'') that the ring of integers is not existentially decidable. Over the years there has been many results related to Hilbert 10th problem over different fields. For instance, the existential decidability of a Henselian valued field of mixed characteristic and finite ramification can be reduced to the positive existential decidability of its residue field, plus some additional structure.

An example of a mixed characteristic Henselian field is the fraction field of Witt Vectors. It is a construction analogous to the construction of the p-adic numbers from $\mathbb{F}_p$, and it takes a perfect field $F$ of characteristic $p$ and constructs a field with value group $\mathbb{Z}$ and residue field $F$. We will look at the existential decidability of the Henselian valued fields arising from finite extensions of the Witt vectors over a positive characteristic Henselian valued field. I will report on our progress so far, the problems that we have encountered, and the goals we are working toward.

Networks are fascinating because of their ability to describe complex structures found in a broad variety of systems, from arts and humanities, via the life sciences to the physical science and mathematics. Perhaps even more startling is the variety of approaches that different disciplines have contributed to the study of networks. All of these approaches have a common goal: finding simplicity in complexity. Yet complexity science has no single overarching theory of what simplicity means and how and why it can be found. In this talk I will present some well known methods and results to highlight different approaches to finding simplicity that computer science, physics and mathematics have developed. I will then highlight some less-known connections and argue that an overarching theory of simplicity may be within reach. 

Many systems in the natural sciences and beyond exhibit complex relational structure that changes over time. Social networks evolve as relationships change, traffic patterns vary throughout the day, and protein–protein interactions shift with cellular conditions. Learning these dynamics from data is a challenging problem. A recent approach in this area, Graph Neural Controlled Differential Equations, extends Neural CDEs from paths on Euclidean domains to paths on graph domains. In this talk, we discuss an extension of this framework that respects the geometry of the underlying set and is equivariant to permutations of the node ordering. We will discuss empirical advantages of this modification, as well as benefits of the formulation as a continuous-time model. 

Researchers across disciplines rely on clustering to uncover meaningful patterns in noisy similarity data. Standard two-step pipelines reduce noise before clustering, introducing arbitrary parameters that often produce misleading structure. We unite noise reduction and clustering through Bayesian community detection, using information theory to balance model complexity and fit. This one-step approach automatically determines the number of clusters, avoids detecting patterns in random data, and makes full use of limited samples. Testing on synthetic benchmarks and gene expression data shows the approach yields more reliable and interpretable results than widely used alternatives, improving data-driven discovery across scientific disciplines where samples are limited or expensive.

Robustness to perturbation is a key topic in the study of complex systems occurring across a wide variety of applications from epidemiology to biochemistry. In this talk I will examine the eigenspectrum of the Jacobian matrices associated to a general class of networked dynamical systems, which contains information on how perturbations to a stationary state develop over time. I will show that stability is always determined by a spectral outlier, but with pronounced differences to the corresponding eigenvector in different regimes. Depending on model details, instability may originate in nodes of anomalously low or high degrees, or may occur everywhere in the network at once. Our results have potentially useful applications in network monitoring to predict or prevent catastrophic failures.

Recurrent neural networks (RNNs) provide a theoretical framework for understanding computation in biological neural circuits, yet classical results, such as Hopfield's model of associative memory, rely on symmetric connectivity that restricts network dynamics to gradient-like flows. In contrast, biological networks support rich time-dependent behaviour facilitated by their asymmetry. In this talk, I will introduce a general framework, known as ‘drift-diffusion matching’, for training continuous-time RNNs to represent arbitrary stochastic dynamical systems within a low-dimensional latent subspace. Allowing asymmetric connectivity, I will show that RNNs can embed the drift and diffusion of an arbitrary stochastic differential equation, including nonlinear and nonequilibrium dynamics such as chaotic attractors. As an application, we have constructed RNN realisations of stochastic systems that transiently explore various attractors through both input-driven switching and autonomous transitions driven by nonequilibrium currents, which we interpret as models of associative and sequential (episodic) memory. To elucidate how these dynamics are encoded in the network, I will introduce decompositions of the RNN based on its asymmetric connectivity and its time-irreversibility. These results extend attractor neural network theory beyond equilibrium, showing that asymmetric neural populations can implement a broad class of dynamical computations within low-dimensional manifolds, unifying ideas from associative memory, nonequilibrium statistical mechanics, and neural computation.

Real complex systems exhibit rich collective behavior, yet identifying which components of an interaction network drive such dynamics remains a central challenge. Here, we show that complexity itself can resolve this problem. In large random and empirical networks, structural disorder and heterogeneity induce spectral localization, causing Laplacian modes to concentrate on small subsets of nodes. This converts global modes into identifiable dynamical units tied to specific structural components. Exploiting this principle, we develop a node-resolved stability framework that predicts instability onsets, identifies the nodes responsible for collective transitions, and restores interpretability in systems where classical modal theories fail. In heterogeneous reaction networks, the same mechanism enables collective states beyond those usually associated with homogeneous assumptions. More broadly, our results show that complexity can be revealed, rather than obscure, the microscopic drivers of macroscopic dynamics.