Past Events
December 2025 | |
| Fri 12 Dec 2025 14:00 - 15:00 | Seminar: Sonja Greven (HU Berlin): Principal component analysis in Bayes spaces for sparsely sampled density functions Centre for Probability, Statistics and Data Science This paper presents a novel approach to functional principal component analysis (FPCA) in Bayes spaces in the setting where densities are the object of analysis, but only few individual samples from each density are observed. We use the observed... |
| Wed 10 Dec 2025 13:00 - 14:00 | Seminar: Jeremiah Buckley (KCL): Sampling properties of the zeroes of the GEF Centre for Probability, Statistics and Data Science The GEF is a random entire function, which is (essentially) the only holomorphic Gaussian process whose zeroes are invariant with respect to automorphisms of the plane (i.e., rotations and translations). Moreover the zeroes are locally repulsive... |
| Thu 4 Dec 2025 14:00 - 15:00 | Seminar: Linqi Wang (QMUL): Multivariate AutoRegressive Smooth Liquidity (MARSLiQ) Centre for Probability, Statistics and Data Science We propose MARSLiQ (Multivariate AutoRegressive Smooth Liquidity), a new multivariate model for daily liquidity that combines slowly evolving trends with short-run dynamics to capture both persistent and transitory liquidity movements. In our... |
| Wed 3 Dec 2025 13:00 - 14:00 | Seminar: Felix Fischer (QMUL): I.I.D. Prophet Inequalities from Samples Centre for Probability, Statistics and Data Science In the prophet problem we observe a sequence of values drawn independently from known distributions and stop at one of the values without knowledge of the rest of the sequence and without recourse. Our goal is to maximize, in expectation, the value... |
| Wed 3 Dec 2025 14:00 - 15:00 | Seminar: Kim Kreienkamp (TU Berlin): Exceptional points in active non-reciprocal mixtures Centre for Probability, Statistics and Data Science Non-reciprocal couplings significantly impact the collective dynamics of mixtures. A particularly striking consequence of such couplings is the spontaneous emergence of time-dependent phases that break parity-time symmetry. Here, we study a... |
November 2025 | |
| Thu 27 Nov 2025 14:00 - 15:00 | Seminar: Kalliopi Mylona (KCL): Algorithmic techniques for finding optimal experimental designs Centre for Probability, Statistics and Data Science Searching for an optimal experimental design is computationally intensive, particularly in high-dimensional settings or in multi-objective cases. In practical applications, it is common to encounter competing design criteria that must be carefully... |
| Thu 20 Nov 2025 14:00 - 15:00 | Miguel de Carvalho (Edinburgh): Neural Statistical Modeling of Cascading Extremes Centre for Probability, Statistics and Data Science In the talk I will address the growing concern of cascading extreme events, such as an extreme earthquake followed by a tsunami, by presenting a novel risk assessment method for these domino effects. The proposed approach develops an extreme value... |
| Wed 19 Nov 2025 13:00 - 14:00 | Seminar: Mohammed Osman (QMUL): Bulk universality for sparse complex matrices Centre for Probability, Statistics and Data Science We consider large random matrices with independent complex entries whose moments decay slowly in the dimension. In particular, this model contains sparse matrices whose entries are the product of a Bernoulli random variable and an independent complex... |
| Thu 13 Nov 2025 14:00 - 15:00 | Seminar: Daniele Bianchi (QMUL): Scalable Bayesian inference for dynamic variable selection in high-dimensional regressions Centre for Probability, Statistics and Data Science We develop a variational Bayes approach for dynamic variable selection in high-dimensional regression models with time-varying parameters and predictors that exhibit a predefined group structure. Through comprehensive simulation studies, we... |
| Wed 12 Nov 2025 13:00 - 14:00 | Seminar: Alexander Watson (UCL): The Wiener-Hopf factorisation of Lévy processes Centre for Probability, Statistics and Data Science |
| Wed 12 Nov 2025 14:00 - 15:00 | Seminar: Benjamin Kellenberger (Yale University): Deep learning for species distribution Modelling: A tale of simplicity, errands, and breakdowns Centre for Probability, Statistics and Data Science Ever-increasing data sizes and advancements in modelling have greatly transformed environmental sciences in recent years. Ecology is no exception to this trend, as large machine, mostly deep learning models, have become ubiquitous in ecologically... |
| Thu 6 Nov 2025 14:00 - 15:00 | Seminar: Rebecca Killick (Lancaster): Global warming and Statistics: A statisticians view of the surge in warming debate Centre for Probability, Statistics and Data Science Global warming is always a hot topic and whatever side of the fence you sit on, you cannot deny the data that the climate is changing - regardless of attributing a cause to that. These changes have huge impacts on individual lives all over the world... |
| Tue 4 Nov 2025 10:00 - 16:00 |  AI Collaborative WorkshopCentre for Probability, Statistics and Data Science We aim to bring together experts in AI and those using (or hoping to use) AI methods in their research, from all the five Schools of the Faculty of Science and Engineering. We envisage that the workshop will strengthen the ties between Schools, build a research community around the development and use of AI and develop teams and ideas well ahead of funding calls. Participation by registration: Registration is now closed. Organisers: Marcella Bona, Thomas Roelleke, Kostas Papafitsoros,... |
October 2025 | |
| Thu 30 Oct 2025 14:00 - 15:00 | Seminar: Sebastian Kühnert (Bochum, Germany): Pivotal inference for linear predictions in stationary processes Centre for Probability, Statistics and Data Science In this paper we develop pivotal inference for the final (FPE) and relative final prediction error (RFPE) of linear forecasts in stationary processes. Our approach is based on a novel self-normalizing technique and avoids the estimation of the... |
| Wed 29 Oct 2025 14:00 - 15:00 | Seminar: Olivia Vanessa Auster (QMUL) : Limit Theorems for Non-Hermitian Ensembles Centre for Probability, Statistics and Data Science The distributions of the extreme eigenvalue moduli are investigated for the complex Ginibre ensemble and its generalisation, the complex induced Ginibre ensemble, in the limit of very large dimensions of random matrices. The left and right tail... |
| Thu 23 Oct 2025 14:00 - 15:00 | Seminar: Ka Man (Ambrose) Yim (University of Cardiff): An Empirical Study of Dimension Estimation Methods Centre for Probability, Statistics and Data Science In this talk, we present findings from an empirical study of popular dimension estimators. The first half of the talk will give an overview of common mathematical approaches in dimensions estimation (including topological data analysis based... |
| Wed 22 Oct 2025 13:00 - 14:00 | Seminar: Alexander Gnedin (QMUL): Optimal stopping with rank-dependent payoffs: history and challenges Centre for Probability, Statistics and Data Science |
| Thu 16 Oct 2025 14:00 - 15:00 | Seminar: Adrien Corenflos (Warwick): A coupling-based approach to f-divergences diagnostics for Markov chain Monte Carlo Centre for Probability, Statistics and Data Science A long-standing gap exists between the theoretical analysis of Markov chain Monte Carlo convergence, which is often based on statistical divergences, and the diagnostics used in practice. We introduce the first general convergence diagnostics for... |
| Wed 15 Oct 2025 12:00 - 13:00 | Seminar: Norberto Lucero-Azuara (QMUL): Modelling the movements of organisms by stochastic theory in a comoving frame
Centre for Probability, Statistics and Data Science How do living organisms move in their environment? While random walk models provide a foundation for understanding biological movement, they fall short in capturing the full complexity of movement patterns generated by living organisms. This study... |
| Fri 10 Oct 2025 14:00 - 15:00 | Seminar: Lida Kanari (Oxford): From neurons to complex human networks using algebraic topology Centre for Probability, Statistics and Data Science Abstract: Topological data analysis (TDA), and in particular persistent homology, has provided robust results for numerous applications, such as protein structure, cancer detection, and material science. In neuroscience, TDA methods have proven... |
| Wed 8 Oct 2025 14:00 - 15:00 | Seminar: Ohad Vilk (Racah Institute of Physics, Hebrew University of Jerusalem): From Diffusion to Strong Anomalous Diffusion: Modeling Movement of Free-Ranging Birds
Centre for Probability, Statistics and Data Science Abstract: Anomalous diffusion, characterized by a nonlinear dependence of the mean-squared displacement on measurement time, is prevalent in nature, ranging from molecular motion at microscopic scales to the large-scale trajectories of migrating... |
| Thu 2 Oct 2025 14:00 - 15:00 | Seminar: Eleni Matechou (QMUL): Applications of Bayesian nonparametric models to ecological data
Centre for Probability, Statistics and Data Science Abstract: Ecological surveys often track individuals from wildlife populations using methods such as traps or nets, aiming to estimate population size and to monitor processes such as migration patterns, behavioural change, or life state transitions.... |
| Wed 1 Oct 2025 14:00 - 15:00 | Seminar: Luca Giuggioli (University of Bristol): First-passage dynamics of a stochastic searcher in disordered systems
Centre for Probability, Statistics and Data Science Abstract: Understanding how spatial disorder affects the random search of a diffusing particle or agent is fundamental to a myriad of applications across disciplines. To quantify how spatial heterogeneities may affect the dynamics of first-passage... |
May 2025 | |
| Wed 28 May 2025 14:00 - 15:00 |  Seminar: IEEE Distinguished Lecture on Sensor Location Optimisation for Effective and Robust BeamformingCentre for Electronics Online Teams Link Summary In many applications, the sensor array's geometrical layout is assumed to be fixed and given in advance. However, it is possible to change the geometrical layout of the array including adjacent sensor spacing and these additional spatial degrees of freedom (DOFs) can be exploited to improve the performance in terms of either beamforming or direction finding or both. With the development of compressive sensing (CS) or the sparsity maximization framework, a new CS... |