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June 13th Lightning Speakers

Flexible and scalable modelling of event sequences, penalised likelihoods, and statistical software by Professor Ioannis Kosmidis

In this talk, Professor Kosmidis will showcase activity on recent collaborative work focusing on three research streams:

  • Flexible point process models and scalable learning algorithms (with applications in team sports and identity systems)

  • Penalised likelihood methods for improving estimation and inference from general statistical models

  • Development and maintenance of statistical software for delivering theoretical and methodological advances to the Data Science community.

Find out more about Ioannis' research.

Distributable computing with sequential Monte Carlo by Professor Adam Johansen

About: Professor Johansen's interests include many areas of computational statistics, broadly interpreted.

In this lightning talk, he will touch on a few areas in which a non-standard variant of a simulation-based method popular in engineering and computational statistics can be employed to tackle difficult estimation problems in a way amenable to distributed implementation.

Find out more about Adam's research.

DSSGx2022 by Professor Colm Connaughton

The DSSG programme gives not-for-profit organisations and government bodies unprecedented access to inspiring, top-tier data science talent. This helps build their capacity to use cutting-edge quantitative methods to address societal challenges in areas such as education, health, energy, public safety, transportation and economic development.

Find out more about Colm's research.

Statistics data science in science and engineering problems by Dr Julia Brettschneider

About: This talk will highlight central challenges in using data science methods to contribute to the solution of problems in science and engineering from a statistical perspective.

In particular, Dr Brettschneider will demonstrate the use of spatial statistics (e.g. in checking the state of digital X-ray detectors and in microscopic imaging) and highlight pillars of data quality.

Find out more about Julia's research.

Gamma-convergence of Onsager--Machlup functionals and MAP estimation in non-parametric Bayesian inverse problems by Tim Sullivan

The Bayesian solution to a statistical inverse problem can be summarised by a mode of the posterior distribution, i.e.\ a MAP estimator. The MAP estimator essentially coincides with the (regularised) variational solution to the inverse problem, seen as minimisation of the Onsager--Machlup functional of the posterior measure. An open problem in the stability analysis of inverse problems is to establish a relationship between the convergence properties of solutions obtained by the variational approach and by the Bayesian approach. To address this problem, we propose a general convergence theory for modes that is based on the $\Gamma$-convergence of Onsager--Machlup functionals, and apply this theory to Bayesian inverse problems with Gaussian and edge-preserving Besov priors.

Find out more about Tim's research.

Professor Giovanni Montana

Professor Hakan Ferhatosmanoglu