Theodore Papamarkou

Theodore Papamarkou

Reader in maths of data science

The University of Manchester

About me

My research spans Bayesian deep learning, approximate Monte Carlo methods, and mathematics of deep learning. By conducting research in these areas, I am interested in addressing respective questions in uncertainty quantification for deep learning, approximate inference with big data or with high-dimensional models, and function approximation.

  • Bayesian deep learning
  • Approximate Monte Carlo
  • Mathematics of deep learning
  • Uncertainty quantification
  • Approximate inference
  • Function approximation
  • PhD in statistics, 2009

    University of Warwick

  • MSc in statistics, 2005

    University of Warwick

  • BSc in mathematics, 2004

    University of Ioannina


Reader in the mathematics of data science
Oct 2020 – Present Manchester, UK
Research on approximate inference, complexity theory and mathematics of deep learning. Teaching topics associated with data science.
Research scientist
Apr 2019 – Sep 2020 Oak Ridge, Tennessee, USA
Strategic hire in artificial intelligence. Principal investigator of two-year laboratory directed research and development (LDRD) project ‘Scalable Bayesian uncertainty quantification for neural networks’. Conducted research on Bayesian inference for neural networks.
Assistant professor in statistics
Sep 2015 – Mar 2019 Glasgow, UK
Conducted research on Markov chain Monte Carlo methodology. Taught three courses, namely ‘big data analytics’, ‘data analysis’ and ‘statistical methods’. Advisor of studies of twenty-one undergraduate students. Head of taught postgraduate programme in statistics and data analytics.
Research fellow in statistics
Jul 2014 – Aug 2015 Coventry, UK
Conducted research on two projects. One project was related to Bayesian modelling of single-cell RNA sequencing data. The other project was related to Bayesian inference for rough differential equations.
Research associate in statistics
Jan 2014 – Jun 2014 Coventry, UK
For 80% of work time, conducted research on Bayesian model selection via population Markov chain Monte Carlo for a biochemical pathway of Ewing sarcoma. For the remaining 20% of work time, administrated the UK-wide network on computational statistics and machine learning (NCSML).
Research associate in statistics
Dec 2011 – Dec 2013 London, UK
Conducted research on variance reduction for differential geometric Markov chain Monte Carlo methods.
Research associate in statistics
Feb 2010 – Nov 2011 London, UK
Performed statistical analysis of big genomic data sets to identify genetic determinants of blood lipid levels. Provided support for data filtering, bioinformatics and computing tasks.
Research statistician
Jul 2009 – Oct 2009 London, UK
Researched epidemiological associations between alcohol abstinence and personality disorders.