A post by Shannon Williams, PhD student on the Compass programme.
My PhD focuses on the application of statistical methods to volcanic hazard forecasting. This research is jointly supervised by Professor Jeremy Philips (School of Earth Sciences) and Professor Anthony Lee. (more…)
Our third Cohort of Compass students have confirmed their PhD projects for the next 3 years and are establishing the direction of their own research within the CDT. (more…)
Our first Compass Conference was held on Tuesday 13th September 2022, hosted in the newly refurbished Fry Building, home to the School of Mathematics. (more…)
Compass student Dan Milner and his academic supervisors have published an article in Frontiers, one of the most cited and largest research publishers in the world. Dan’s work is funded in collaboration with ILRI (International Livestock Research Institute). (more…)
A post by Dan Ward, PhD student on the Compass programme.
Normalising flows are black-box approximators of continuous probability distributions, that can facilitate both efficient density evaluation and sampling. They function by learning a bijective transformation that maps between a complex target distribution and a simple distribution with matching dimension, such as a standard multivariate Gaussian distribution. (more…)
A post by Sam Stockman, PhD student on the Compass programme.
Throughout my PhD I aim to bridge a gap between advances made in the machine learning community and the age-old problem of earthquake forecasting. In this cross-disciplinary work with Max Werner from the School of Earth Sciences and Dan Lawson from the School of Mathematics, I hope to create more powerful, efficient and robust models for forecasting, that can make earthquake prone areas safer for their inhabitants.
For years seismologists have sought to model the structure and dynamics of the earth in order to make predictions about earthquakes. They have mapped out the structure of fault lines and conducted experiments in the lab where they submit rock to great amounts of force in order to simulate plate tectonics on a small scale. Yet when trying to forecast earthquakes on a short time scale (that’s hours and days, not tens of years), these models based on the knowledge of the underlying physics are regularly outperformed by models that are statistically motivated. In statistical seismology we seek to make predictions through looking at distributions of the times, locations and magnitudes of earthquakes and use them to forecast the future.
Congratulations to Ed Davis who won a poster award as part of the Jean Golding Institute’s Beauty of Data competition.
This visualisation, entitled “The World Stage”, gives a new way of representing the positions of countries. Instead of placing them based on their geographical position, they have been placed based on their geopolitical alliances. Countries have been placed such to minimise the distance to their allies and maximise the distance to their non-allies based on 40 different alliances involving 161 countries. This representation was achieved by embedding the alliance network using Node2Vec, followed by principal component analysis (PCA) to reduce it to 2D.
