Projects
Adaptive multilevel MCMC sampling
Developing an MCMC algorithm for efficient Bayesian inference in multilevel models
Shocks and resilience
Measuring policy impact in the COVID-19 crisis and building resilience against future shocks
Uncertainty quantification for black box models
Developing methods for the quantification of uncertainty in complex numerical models
Diffuse multiple scattering analysis
Analysing complex multi-phase materials using X-ray scattering and machine learning
Project Odysseus – Understanding London ‘busyness’ and exiting lockdown
Capturing activity over London to better understand 'busyness' and aid effective policy-making strategies for exiting the pandemic lockdown
QUIPP - Quantifying utility and preserving privacy in synthetic data sets
Understanding the balance between utility, privacy and the uncertainty associated with synthetic data sets
People
Professor Nick Hawes
AI (Robotics) Group Leader
Dr Scott Hosking
Senior Research Fellow
Dr Peter Foster
Research Data Scientist
Dr Jon Cockayne
Research Associate
Konstantinos Zinelis
Enrichment Student
Dr Toni Karvonen
Research Fellow
Takuo Matsubara
Doctoral Student
Dr Hussein Rappel
Research AssociateEvents
Statistics and computation
Monday 13 Jan 2020 - Tuesday 14 Jan 2020
Time: 10:00 - 17:00
Machine learning meets statistics
Monday 25 Mar 2019 - Tuesday 26 Mar 2019
Time: 09:30 - 16:15
Workshop on probabilistic numerical methods
Wednesday 11 Apr 2018 - Friday 13 Apr 2018
Time: 09:00 - 17:00
Short Talk: Bayesian inference for the alpha-stable distribution
Thursday 03 Aug 2017
Time: 18:37
Optimal Experimental Design and Inverse Problems
Tuesday 14 Mar 2017 - Wednesday 15 Mar 2017
Time: 17:37
Short Talks: Bayesian models for sparse regression analysis, Topological data analysis and Monte Carlo Algorithms
Thursday 09 Mar 2017
Time: 17:37
Articles
Impact stories
Publications and software
Psychometric Analysis of Forensic Examiner Behavior
Forensic science often involves the comparison of crime-scene evidence to a known-source sample to...
Luby, A., Mazumder, A. and Junker, B., 2019. Psychometric Analysis of Forensic Examiner Behavior. arXiv preprint arXiv:1910.07447.
Data Study Group Final Report: AstraZeneca
Machine learning for enhanced understanding of cell culture bioprocess development
Data Study Group team. (2019, August 13). Data Study Group Final Report: AstraZeneca. Zenodo. http://doi.org/10.5281/zenodo.3367412
On the marginal likelihood and cross-validation
In Bayesian statistics, the marginal likelihood, also known as the evidence, is used to...
Fong, Edwin & Holmes, Chris. (2019). On the marginal likelihood and cross-validation. arXiv:1905.08737.
Multi-resolution Multi-task Gaussian Processes
We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions...
Hamelijnck. O., Damoulas. T., Wang. K. & Girolami. M. (2019). Multi-resolution Multi-task Gaussian Processes. In Advances in Neural Information Processing Systems 32 (NeurIPS'19).
Structured Variational Inference in Continuous Cox Process Models
We propose a scalable framework for inference in an inhomogeneous Poisson process modeled by...
Aglietti. V., Bonilla. E., Damoulas. T. & Cripps. S. (2019). Structured Variational Inference in Continuous Cox Process Models. In Advances in Neural Information Processing Systems 32 (NeurIPS'19).
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
Increasingly complex datasets pose a number of challenges for Bayesian inference. Conventional posterior sampling...
Edwin Fong, Simon Lyddon, Chris Holmes; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1952-1962, 2019.
On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case
We consider the problem of sampling from a target distribution which is not necessarily...
On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case Chau, N. H.; Moulines, É.; Rásonyi, M.; Sabanis, S.; Zhang, Y. 2018. arXiv:1905.13142 [math.ST]
Generalized Variational Inference
This paper introduces a generalized representation of Bayesian inference. It is derived axiomatically, recovering...
Knoblauch, J., Jewson, J. and Damoulas, T. (2019). Generalized Variational Inference. arXiv:1904.02063.
Bayesian statistical learning for big data biology
Bayesian statistical learning provides a coherent probabilistic framework for modelling uncertainty in systems. This...
Yau, C. & Campbell, K. Biophys Rev (2019). https://doi.org/10.1007/s12551-019-00499-1
Interest groups
AI in resource- and data-constrained environments
How can we build effective predictive models on hardware platforms that have limited computational resources and/or when the model training data are by definition always limited in amount?
Machine learning and dynamical systems
How do we analyse dynamical systems on the basis of observed data, rather than attempt to study them analytically?
