Copies of our course handbook are available as a reference point for potential students.

StatML Course Handbook 2020 

StatML Course Handbook 2019 

Student Research

Our students are undertaking a wide range of research.  Here are some ‘five minute’ research presentations that will give you a flavour of the projects that some of them have been working on.   

Statistical Models for Hotspot Detection by Andrea Brizzi, Imperial.

5 Minute Research: ‘Your Brain Is More Connected Than You Think’ by Anna Menacher, Oxford.

5 Minute Research: ‘Are You Driving the COVID-19 Epidemic?’  by Melodie Monod, Imperial. 

5 Minute Research: ‘Improving Google’s Cookies’ by Jose Pablo Falck, Imperial.

Publications 

Students across the cohort have been producing some great research, many of them turning their focus to COVID related projects.  Here are a few links to publications from some of our students.

Age groups that sustain resurging COVID-19 epidemics in the United States (science.org)

Melodie Monod (Imperial, 2019 cohort).  Science, Vol.371, Issue 6536, eabe8372

Effectiveness and resource requirements of test, trace and isolate strategies for COVID in the UK

Michael Hutchinson (Oxford, 2019 cohort).  Royal Society Open Science

Bayesian Probabilistic Numerical Integration with Tree-Based Models 

Harrison Zhu (Imperial, 2019 cohort). https://arxiv.org/abs/2006.05371

Some of the thesis topics undertaken by our students

Statistical Monitoring and Control of Nuclear Fusion Systems.

Bayesian spatiotemporal modelling with application to the HIV epidemic.

Improving Experimentation and Measurement for Online Products and Services.

Bayesian Methods in Astronomy.

Bayesian reconstruction of epidemic transmission chains from viral deep-sequence data.

Applications of deep learning to financial markets.

Application of machine learning methods to wavelet-lifted GNAR models.

Bayesian non parametrics for aggregate modelling.

End-to-end Probabilistic Modelling of Electronic Health Records.

Deep and Probablisitc Geometric Learning.

Scalable Bayesian Statistical Machine Learning Methods for the Analysis of Neurodegenerative Diseases.

Bayesian semiparametric estimation in latent variable models.

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