My research spans approximate inference. I am interested in constructing approximate stochastic algorithms for intractable problems and in characterizing the tractability of such algorithms using complexity theory.

Topical questions in approximate inference that interest me include learning with big data or with high-dimensional models, approximate uncertainty quantification for deep learning, the construction of doubly stochastic processes using the notion of random environment, and the taxonomy of probabilistic complexity classes in relation to the P and NP classes.

Interests

- Approximate inference
- Complexity theory
- Mathematics of deep learning
- Monte Carlo methods
- Artificial intelligence
- Machine learning for genetics

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 approximate inference, complexity theory and mathematics of deep learning. Teaching topics associated with data science.

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.

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