I am interested in the problem of speeding up computation. My research spans approximate inference and the study of data with topological techniques, to enable computation in high dimensions. Bayesian deep learning and topological deep learning are the two main areas of my work. Details about my current projects can be found here.

The theory of computation, quantum computing and neuromorphic computing appeal to me, as they may provide computational speedup. Healthcare is the domain of application that I aspire to support with computational advancements.

Interests

- Computing
- 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

Reader in the mathematics of data science

Research on Bayesian deep learning and topological deep learning. Teaching 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

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

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

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

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

Conducted research on variance reduction for differential geometric Markov chain Monte Carlo methods.

Research associate in statistics

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

Researched epidemiological associations between alcohol abstinence and personality disorders.

Access and search all publications.

Approximate blocked Gibbs sampling for Bayesian neural networks.
In *Statistics and Computing*.

(2023).
(2023).
(2022).
Challenges in Markov chain Monte Carlo for Bayesian neural networks.
In *Statistical Science*.

(2022).
Distributions.jl: definition and modeling of probability distributions in the JuliaStats ecosystem.
In *Journal of Statistical Software*.

(2021).
Wide neural networks with bottlenecks are deep Gaussian processes.
In *Journal of Machine Learning Research*.

(2020).
Multiphase MCMC sampling for parameter inference in nonlinear ordinary differential equations.
In *Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics*.

(2018).
The controlled thermodynamic integral for Bayesian model evidence evaluation.
In *Journal of the American Statistical Association*.

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

(2016).
Zero variance differential geometric Markov chain Monte Carlo algorithms.
In *Bayesian Analysis*.

(2014).