Simple statistical models can reliably infer the peak of infections and subsequent deaths from the virus, according to a Bristol statistician. Professor Simon Wood used simple models with few assumptions, together with Imperial College’s estimate of the distribution of times from disease onset to death for fatal cases, to infer the time course of fatal infections […]
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Population density as experienced by the average person better explains variations in the rate of spread of COVID-19
A preprint by a team including Bristol statistician Professor Oliver Johnson shows the importance of ‘lived population density’ as a measure to track the spread of the disease The preprint demonstrates that population density is important in the spread of COVID-19, but that non-standard ways of measuring density do a better job of explaining it […]
Pierre Jacob visit to University of Bristol
We were delighted to welcome Pierre Jacob, Associate Professor of Statistics at the University of Harvard, to the University of Bristol in March. Pierre delivered lectures for the COMPASS CDT students, which were also open for the staff in the School of Maths to attend. In his lectures, Pierre covered couplings, total variation and optimal transport. He […]
Jethro Browell visit to the University of Bristol
The School of Mathematics was pleased to welcome Dr Jethro Browell, Research Fellow at the University of Strathclyde. His part of the Data Science Seminar Series, giving a talk on the topic of ‘Challenging predictions in energy forecasting.’ The series is a collaboration between with the Heilbronn Institute for Mathematical Research and the Jean Golding Institute. Dr […]
Heilbronn Data Science Visitor Alain Durmus
Alain Durmus, Associate Professor at ENS Paris-Saclay, visited the University of Bristol in January as part of the Data Science Seminar series and will again visit later in the year, in March, for Heilbronn Institute’s Hypcoercivity Workshop. The workshop aims to bring together researchers from different communities with an interest in hypocoercivity ideas and techniques […]
Eric Moulines – visit to the School of Mathematics
Eric Moulines from Ecole Polytechnique will be visiting the Heilbronn Institute of Mathematical Research as a Data Science Visitor from 27 January- 31 January. He will present a mini-series of lectures which will give an introduction to convex optimization and its applications in statistical learning. Professor Moulines will also be involved in the Heilbronn Institute’s […]
Pierre Alquier – Visit to School of Mathematics
Pierre Alquier, Research Scientist Riken AIP project, Tokyo, visited the University of Bristol School of Mathematics from November 25 to December 6 2019. As a visitor to the Heilbronn Institute he gave a series of data science lectures to COMPASS students on 27 November 2019 on: Introduction to the variational approach and examples: Mixture models, […]
Women and Non-Binary People in Mathematics event
On 5 and 6 November the school hosted its annual Women and Non-Binary People in Mathematics event. This two-day event held in the Fry Building, aimed at encouraging women and non-binary people to consider continuing their studies to PhD level, welcomed participants from around the country, as well as outside of the UK. The event […]
Bristol Data Science Seminar – Jonathan Huggins
The School of Mathematics was pleased to welcome Jonathan Huggins from Harvard University on 23 October 2019, to kick off the first event of the Bristol Data Science Seminars. He delivered a two-part talk on ‘Using bagged posteriors for robust model-based inference.’ The seminar series is part of the collaboration between the Jean Golding Institute and […]
Modern Challenges in Spectral Analysis of Time Series
The workshop ‘Modern Challenges in Spectral Analysis of Time Series’ brought together a group of leading experts from universities in the UK, the US, Belgium, Cyprus and Germany, and from a variety of career stages, to discuss cutting edge developments in wavelet and Fourier methods in the context of high dimensional time series, functional time […]
