Research seminars

Start at 15:30 via Zoom (joining instructions follow below)

ABSTRACT: Kernel based methods are widely used in both machine learning and Bayesian statistics. Standard kernels, like Gaussian and Matern kernels, are both stationary and separable. We introduce a flexible method to create nonstationary and non-separable kernels based on an infinite mixture of convolved stochastic processes. When the mixing process is stationary but the convolution function is nonstationary we arrive at non-separable kernels with constant non-separability that are available in closed form. When the mixing is nonstationary and the convolution function is stationary we arrive at non-separable random fields that have varying non-separability and better preserve local structure. We show how a single Gaussian process (GP) with these random fields can computationally and statistically outperform both separable and existing nonstationary non-separable approaches such as treed GPs and deep GP constructions.

Zoom link: https://bruneluniversity.zoom.us/j/99894030917
- Meeting ID: 998 9403 0917
- Passcode: 2108619563

 

Start at 15:30 via Zoom (joining instructions follow below).

ABSTRACT: In quantum mechanics, the direction of the probability density flow is not necessarily the same as that of velocity. One manifestation of this counter-intuitive fact is the so-called quantum backflow effect, in which the probability of finding a particle "on the left" increases with time despite the particle's velocity pointing "to the right." In my talk, I will provide a brief review of the quantum backflow problem, and will show how it can be generalized to quantum states with position-momentum correlations.
References: A. Goussev, arXiv:2002.03364 (2020); A. Goussev, PRA 99, 043626 (2019)

Zoom link: https://bruneluniversity.zoom.us/j/94925713575
- Meeting ID: 949 2571 3575
- Password: 8988773309

Start at 15:30 via Zoom (joining instructions follow below).

ABSTRACT: In quantum mechanics, the direction of the probability density flow is not necessarily the same as that of velocity. One manifestation of this counter-intuitive fact is the so-called quantum backflow effect, in which the probability of finding a particle "on the left" increases with time despite the particle's velocity pointing "to the right." In my talk, I will provide a brief review of the quantum backflow problem, and will show how it can be generalized to quantum states with position-momentum correlations.
References: A. Goussev, arXiv:2002.03364 (2020); A. Goussev, PRA 99, 043626 (2019)

Zoom link: https://bruneluniversity.zoom.us/j/94925713575
- Meeting ID: 949 2571 3575
- Password: 8988773309

Start at 15:30 via Zoom (joining instructions follow below).

ABSCTRACT: In this talk, I will propose a continuous-time stochastic intensity model, namely the two-phase dynamic contagion process, for modelling the epidemic contagion of COVID-19. The dynamic contagion process is a branching process which is an extension of the classical Hawkes process commonly used for modelling earthquakes and more recently financial contagion. In contrast to most existing models, this model allows randomness to the infectivity of individuals rather than a constant reproductive rate as assumed by standard models. Key epidemiological quantities such as the distribution of final epidemic size and expected epidemic duration have been derived and estimated based on real data for various regions and countries. The time that governmental intervention became effective is estimated and the results are consistent with the actual time of intervention and incubation time of the disease. The purpose of this talk will be to introduce the model and to show that this model could potentially be a valuable tool in the modelling of COVID-19 infection. Similar variations of this model could also be used in more general epidemiological modelling.

Join Zoom Meeting: https://bruneluniversity.zoom.us/j/98209870169
- Meeting ID: 982 0987 0169
- Password: 6659222909

 

Start at 15:30 via Zoom (joining instructions follow below).

ABSTRACT: Linear wave scattering problems (e.g. for acoustic, electromagnetic and elastic waves) are ubiquitous in science and engineering applications. However, conventional numerical methods for such problems (e.g. FEM or BEM with piecewise polynomial basis functions) are prohibitively expensive when the wavelength of the scattered wave is small compared to typical lengthscales of the scatterer (the so-called "high frequency" regime). This is because the solution possesses rapid oscillations which are expensive to capture using conventional approximation spaces. In this talk we outline recent progress in the development of "hybrid numerical-asymptotic" methods. These methods use approximation spaces containing oscillatory basis functions, carefully chosen to capture the high frequency asymptotic behaviour, leading to a significant reduction in computational cost.

Join Zoom Meeting: https://bruneluniversity.zoom.us/j/92367721405
- Meeting ID: 923 6772 1405
- Password: 7129906480

Start at 15:30 via Zoom (joining instructions follow below).

ABSTRACT: This presentation is split into three parts. The first part is a brief tutorial on how a commodity futures market operates, and the role played by stochastics in pricing of financial derivatives, including commodity futures. In the second part, I will introduce a recursive conditional mean estimator for linear Gaussian dynamic systems, popularly called the Kalman filter (KF), and its application in modelling the movement of crude oil futures prices.  Finally, I will describe how a KF-based model can be modified using quantitative measures of macroeconomic news sentiment to enhance the futures price prediction. I will also present a summary of our findings from numerical experiments on modelling and predicting crude oil futures prices using the Kalman filter and news sentiment scores.

Join Zoom Meeting at https://bruneluniversity.zoom.us/j/98340190032
- Meeting ID: 983 4019 0032
- Password: 0154229543

Start at 16:00 via Teams.

ABSCTRACT: I will be discussing a new methodology that permits the Bayesian supervised learning of generic tensor-valued functional relationships between two random variables, at least one of which is high-dimensional and distributed discontinuously. The ultimate aim is the prediction of either variable, at test data on the other. We will model the sought function as a random realisation from a tensor-variate, non-stationary stochastic process, each parameter of the covariance structure of which, is made to adapt to the discontinuity of the sought function. This is proven to be permitted by compounding multiple scalar-valued stationary processes with the mother tensor-valued non-stationary process. That 2 layers suffice in this learning strategy, is proven in the method. The talk will be motivation-heavy.