Dr Christopher Yau

Dr Christopher Yau

Institute of Cancer and Genomic Sciences
Reader in Computational Biology

Contact details

Centre for Computational Biology
Rm 324, Haworth Building (Y2)
University of Birmingham
B15 2TT

Christopher Yau is a Reader in Computational Biology at the Institute of Cancer and Genomic Sciences where he is based at the Centre for Computational Biology and leads the Statistical Machine Learning BioHealth group. 

He is an expert in statistical methodologies for machine learning and data science and specialises in genomic science particularly cancer. His research ranges from mathematical and statistical algorithm development to collaborations with experimental scientists and clinicians involving modelling real world biomedical data sets. He leads a diverse group of researchers who specialise in both experimental and computational modelling and regularly gives talks and lectures around the world on data science. 

His goal is to conceive of a computational intelligence framework that will provide the foundation of learning health systems that will support novel health-related research from the molecular scale through to whole populations. 

His work has been funded by the Medical Research Council, Engineering and Physical Sciences Research Council, Wellcome Trust, Ovarian Cancer Action and Cancer Research UK. 

Christopher is playing a leading role in the development of Statistical Machine Learning for the Genomics England 100,000 Genomes project as sub-domain lead in Machine Learning for the Quantitative Methods, Machine Learning and Functional Genomics Clinical Interpretation Partnership. He sits on the committee of the Statistical Computing Section for the Royal Statistical Society and currently serves as a Career Development Task Force member for the Academy of Medical Sciences.


  • D.Phil (Oxon), Statistics, University of Oxford, 2009
  • M.A. (Hons)/M.Eng. (Cantab), Information and Computer Engineering, University of Cambridge, 2004
  • Fellow of The Royal Statistical Society


Christopher read Engineering at Trinity College, University of Cambridge graduating in 2004. He subsequently read for a doctoral in Statistics at The Queens’ College, University of Oxford where he was awarded the best doctoral research prize in 2009.

He subsequently undertook postdoctoral research as a Medical Research Council Research Fellow in Biomedical Informatics until 2012 when he was appointed Lecturer in Statistics at Imperial College London. In 2014 he returned to Oxford, where he led the Genomic Medicine research group at the Wellcome Trust Centre for Human Genetics and was awarded the title of Associate Professor in 2016. 

He joined the University of Birmingham in 2017 as Reader in Computational Biology to start a research programme in Statistical Machine Learning for BioHealth. He is a Fellow of the Alan Turing Institute.


Bioinformatics MSc/Diploma/Certificate

Postgraduate supervision

The Statistical Machine Learning in BioHealth is seeking postgraduate researchers with interests in any of the following areas:

  • Computational Statistics/Machine Learning/Artificial Intelligence – applicants should have a strong quantitative background in maths, physics, computer science or engineering
  • Quantitative genomic sciences – applicants should have an interest in developing a joint quantitative and experimental research project under a co-supervision arrangement between Christopher Yau and an appropriate experimental investigator 
Specific opportunities and further information can be found on the lab website. Specific enquiries should be directed to Dr Christopher Yau.


Other activities

  • Fellow, Alan Turing Institute 
  • Leadership Programme Task Force, Academy of Medical Sciences 
  • Statistical Computing Section Committee, Royal Statistical Society 
  • Statistical Machine Learning sub-domain lead, Genomics England 100,000 Genomes Project


CAMPBELL, K, YAU, C. (2018). Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data. Nature Communications, 9(1):2442. (https://www.nature.com/articles/s41467-018-04696-6

RUKAT, T., HOLMES, C., YAU, C. (2018). Probabilistic Boolean Tensor Decomposition, International Conference on Machine Learning, 4410-4419 (http://proceedings.mlr.press/v80/rukat18a/rukat18a.pdf

HU, Z., YAU, C., AHMED, A. (2017). A pan-cancer genome-wide analysis reveals tumour dependencies by induction of nonsense-mediated decay. Nature Communications 8;15943 (https://www.nature.com/articles/ncomms15943)

CAMPBELL, K, YAU, C. (2016). Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference. PLoS Comput Biol, 12 (11), pp. e1005212 

HELLNER K, MIRANDA F, FOTSO CHEDOM D, HERRERO-GONZALEZ S, HAYDEN DM, TEARLE R, ARTIBANI M, KARAMINEJADRANJBAR M, WILLIAMS R, GAITSKELL K et al. 2016. Premalignant SOX2 overexpression in the fallopian tubes of ovarian cancer patients: Discovery and validation studies. EBioMedicine, 10 pp. 137-149.

TITSIAS MK, HOLMES CC, YAU C. 2016. Statistical Inference in Hidden Markov Models Using k-Segment Constraints Journal of the American Statistical Association.111 (513), pp. 200-215.

PIERSON E, YAU C. 2015. ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol, 16 (1), pp. 241. 

YAU C, PAPASPILIOPOULOS O, ROBERTS GO, HOLMES C. 2011. Bayesian Nonparametric Hidden Markov Models with application to the analysis of copy-number-variation in mammalian genomes. J R Stat Soc Series B Stat Methodol, 73 (1), pp. 37-57. 

YAU C, MOURADOV D, JORISSEN RN, COLELLA S, MIRZA G, STEERS G, HARRIS A, RAGOUSSIS J, SIEBER O, HOLMES CC. 2010. A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data. Genome Biol, 11(9), pp. R92 

YAU C. 2013. OncoSNP-SEQ: a statistical approach for the identification of somatic copy number alterations from next-generation sequencing of cancer genomes. Bioinformatics, 29 (19), pp. 2482-2484. 

COLELLA S, YAU C, TAYLOR JM, MIRZA G, BUTLER H, CLOUSTON P, BASSETT AS, SELLER A, HOLMES CC, RAGOUSSIS J. 2007. QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data. Nucleic Acids Res, 35 (6), pp. 2013-2025. 

A full publication list can be found on the website link.