Dr Yuanwei Xu

Dr Yuanwei Xu

Institute of Cancer and Genomic Sciences
Research Fellow

Contact details

Address
Centre for Computational Biology
Institute of Cancer and Genomic Sciences
University of Birmingham
Edgbaston
Birmingham
B15 2TT

Dr Xu is a Research Fellow in Statistical Computing. He studied applied mathematics and further researched in computational chemistry. He has worked on TB outbreak reconstruction with bacterial genomics and epidemiology data, using mathematical modelling and Bayesian statistics. His research interests are computational biology, biomedical informatics and machine learning applications in healthcare.  

Qualifications

  • PhD in Scientific Computing, University of Warwick, 2016
  • MSc in Financial Mathematics and Computation, University of Leicester, 2011

Biography

Dr Xu obtained his PhD in Scientific Computing. His research involved the development and application of novel Monte Carlo method to the simulation of aggregation of lattice proteins, where the energy landscape was complex and posed significant difficulty in effective sampling of the conformational space. The novel Monte Carlo method aimed to improve the efficiency of sampling and the estimation of the transition temperature between aggregated and dispersed state of the system of proteins. After his PhD, Dr Xu joined the Centre for Mathematics of Precision Healthcare at Imperial College London as a Research Associate, working on Bayesian outbreak reconstruction and transmission inference of infectious disease, in particular TB, from pathogen genomic sequence and epidemiology data, and further integration with other patient covariates in order to better understand transmission patterns. He studied the joint estimation of transmission events on many genetically delineated clusters and contributed to the development of a software package, TransPhylo.

Currently at the Centre for Computational Biology, Dr Xu is mainly working on:

  •  how machine learning methods can aid in patient triage in major incident settings and
  • microbiome data analysis and association with host phenotype.