Targeted learning
Wednesday 04 Mar 2020
Time: 18:00 - 20:30
Search
Non-parametric & semi-parametric methods
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
Turing Data Science Class: Statistical analysis of networks (Part 1&2)
Monday 29 Jan 2018
Time: 10:15 - 12:30
Projects
Multimorbidity phenotyping in cardiovascular disease
Developing statistical machine learning tools to investigate longitudinal multi-disease trajectories
Causal inference for improved cybersecurity threat detection
Understanding the causal links in the tactics and techniques of cyberattacks
Synthetic data generation for finance and economics
Generating artificial business, finance, and economic data to enable data sharing, scenario simulation, and model validation
Statistical machine learning for randomised clinical trials (MRC CTU)
Developing and applying machine learning methods to improve the design, conduct and analyses of clinical trials
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
Fundamentals of statistical machine learning
Developing statistical machine learning tools to keep up with the growing needs of the engineering sciences
Publications and software
Nonstationary Nonseparable Random Fields
We describe a framework for constructing nonstationary nonseparable random fields based on an infinite...
'Wang, K., Hamelijnck, O., Damoulas, T., & Steel, M. (2020). Nonstationary Nonseparable Random Fields. In Proceedings of the 37th International Conference on Machine Learning (ICML), Online.'
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.
Efficient Inference in Multi-task Cox Process Models
We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point...
Aglietti, V., Damoulas, T. & Bonilla, E. (2019). Efficient Inference in Multi-task Cox Process Models. The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019).
Events
Targeted learning
Wednesday 04 Mar 2020
Time: 18:00 - 20:30
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
Turing Data Science Class: Statistical analysis of networks (Part 1&2)
Monday 29 Jan 2018
Time: 10:15 - 12:30
People
Victor Btesh
Enrichment Student
Skyler Xie
Enrichment Student
Luke Hardcastle
Enrichment Student
Lara Johnson
Enrichment Student
Lars Henry Berge Olsen
Enrichment Student
Jakob Wessel
Enrichment Student
Hollan Haule
Enrichment Student
Daniel Guerrero Romero
Enrichment Student