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…)
EPSRC PhD in Computational Statistics and Data Science is now recruiting for its next available fully-funded home fees places to start September 2022.
We will be prioritising applicants who wish to work with the following potential supervisors:
Professor Nicky Welton – Professor Welton works in the the department of Population Health Sciences in the Bristol Medical School. Her work as a Compass supervisor can include supervision in the areas of Medical Statistics and Health Economics, in particular methods for combining evidence from multiple sources to answer healthcare policy questions.
Dr Sidarth Jaggi – Dr Jaggi is an Associate Professor in the Institute of Statistical Science and a Turing Fellow. His Compass PhD supervision can cover areas such as high-dimensional statistics, and robust machine learning.
Dr Rihuan Ke – Dr Ke is a Lecturer in the School of Mathematics. His research is on machine learning and mathematical image analysis. He has been developing statistical learning approaches and data-driven models for solving problems in computation and data science, and in particular for large scale image analysis. The typical approaches that he takes are to combine mathematical structures and statistical knowledge with modern deep learning techniques, to enable automatic analysis of the intrinsic structure of imaging data and exploiting rich information encoded in the data for the underlying tasks. In his projects, he is also interested in relevant applications in material sciences, medical imaging, and remote sensing. He is supervising PhD projects in deep learning, image analysis, and more generally data science.
Using OMIC data to predict breast cancer outcomes
Our Compass students took part in a data challenge in partnership with the MRC Integrative Epidemiology Unit based at the University of Bristol.
Academic leaders Matthew Suderman, Paul Yousefi and Josine Min challenged the students to use data collected on cancer rates, risk factors, complexity of individual cancers and potential treatments to build outcome prediction models from multi-omic data derived from hundreds of breast tumours.
In smaller teams, over a 2 week period, the students used machine learning techniques to feedback to academic staff a number of different and creative approaches to this challenge.
A huge welcome to Compass to our 3rd Cohort of CDT students. Look out for their updates during this year about their research and experiences.
Edward Milsom, Ben Griffiths, Emerald Dilworth, Hannah Sansford, Daniel Milner, Harry Tata, Dominic Broadbent, Tennessee Hickling, Josh Givens, Ettore Fincato