Theodore Papamarkou

Theodore Papamarkou

Distinguished professor

Zhejiang Normal University

About me

I work as a distinguished professor at the College of Mathematical Medicine, Zhejiang Normal University (ZJNU). My research lab at ZJNU will be hiring for research roles on Bayesian deep learning (BDL), topological deep learning (TDL), and applications of machine learning on biomedical research and healthcare. The new roles will be advertised in the near future. At the same time, I have ventured into entrepreneurship research as the founder and co-CEO of PolyShape, a startup company with the mission to improve healthcare through the power of machine learning, research consultancy, and research and development. PolyShape aims to become a multinational corporation.

Bayesian inference and topological data analysis provide established machine learning tools. However, deep learning poses scalability challenges to these tools. My research focuses on BDL and TDL, which aim to make Bayesian and topological approaches to deep learning feasible. In the contemporary era of AI, BDL models can quantify uncertainties and assess risks, and TDL models can extract structure from big data. Healthcare is a safety-critical domain of application that interests me, as uncertainty quantification and elicitation of structure from biomedical data matter.

Interests
  • Bayesian deep learning
  • Topological deep learning
  • Computing for healthcare
Education
  • PhD in statistics, 2009

    University of Warwick

  • MSc in statistics, 2005

    University of Warwick

  • BSc in mathematics, 2004

    University of Ioannina

Experience

 
 
 
 
 
Professor in the mathematics of data science
October 2023 – Present Manchester, UK
Research on Bayesian deep learning and topological deep learning. Supervising undergraduate and postgraduate students. Advisor of studies of undergraduate and postgraduate students. Board member of the Data Science and Artificial Intelligence Institute.
 
 
 
 
 
Reader in the mathematics of data science
October 2020 – September 2023 Manchester, UK
Conducted research on Bayesian deep learning and topological deep learning. Taught topics associated with data science. Lead of the advanced mathematics cluster of the Centre for Digital Trust and Security. Chair of the Mathematics Department forum.
 
 
 
 
 
Research scientist
April 2019 – September 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
September 2015 – March 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
July 2014 – August 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
January 2014 – June 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
December 2011 – December 2013 London, UK
Conducted research on variance reduction for differential geometric Markov chain Monte Carlo methods.
 
 
 
 
 
Research associate in statistics
February 2010 – November 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
July 2009 – October 2009 London, UK
Researched epidemiological associations between alcohol abstinence and personality disorders.

Posts

Events in 2024
Events in 2024

List of events I will attend in 2024.

Publications

Access and search all publications.
(2023). Towards efficient MCMC sampling in Bayesian neural networks by exploiting symmetry. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), Research Track.

Cite Source Document

(2023). Approximate blocked Gibbs sampling for Bayesian neural networks. In Statistics and Computing.

Cite Source Document

(2023). Depth-2 neural networks under a data-poisoning attack. In Neurocomputing.

Cite Source Document

(2022). A random persistence diagram generator. In Statistics and Computing.

Cite Source Document

(2022). Challenges in Markov chain Monte Carlo for Bayesian neural networks. In Statistical Science.

Cite Source Document

(2021). Distributions.jl: definition and modeling of probability distributions in the JuliaStats ecosystem. In Journal of Statistical Software.

PDF Cite Source Document

(2020). Wide neural networks with bottlenecks are deep Gaussian processes. In Journal of Machine Learning Research.

PDF Cite Code Source Document

(2018). Multiphase MCMC sampling for parameter inference in nonlinear ordinary differential equations. In Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS).

PDF Cite Source Document

(2016). The controlled thermodynamic integral for Bayesian model evidence evaluation. In Journal of the American Statistical Association.

Cite Source Document

(2016). RNA editing generates cellular subsets with diverse sequence within populations. In Nature Communications.

Cite Source Document

Contact