About me
I am the founder of PolyShape, a research-driven startup developing health intelligence to advance biomedical research and healthcare. Moreover, I am a visiting professor at the School of Applied Mathematical and Physical Sciences (SEMFE) at the National Technical University of Athens (NTUA). My work combines scientific inquiry with entrepreneurial focus to bring new ideas from research into practice.
My research interests span Bayesian, categorical and topological aspects of deep learning and of agentic AI. I am also interested in applications of these methodological areas in biomedical research and healthcare.
Interests
- Bayesian deep learning
- Topological deep learning
- Categorical deep learning
- Computing for healthcare
- Agentic AI
Education
PhD in statistics, 2009
University of Warwick
MSc in statistics, 2005
University of Warwick
BSc in mathematics, 2004
University of Ioannina
Experience
Founder & CEO
Conducting R&D dedicated to developing health intelligence for innovative healthcare solutions. Providing statistical and machine learning consulting to biomedical and healthcare-focused organizations.
Visiting professor
Research on Bayesian, categorical and topological aspects of deep learning and of agentic AI.
Distinguished professor
Conducted research on Bayesian deep learning and topological deep learning. Taught the course ‘probabilistic machine learning’.
Professor in the mathematics of data science
Conducted research on Bayesian deep learning and topological deep learning. Supervised 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
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
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.
Events
Publications
Access and search all publications.
(2024).
TopoX: a suite of Python packages for machine learning on topological domains.
In Journal of Machine Learning Research.
(2024).
Connecting the dots: is mode-connectedness the key to feasible sample-based inference in Bayesian neural networks?.
In International Conference on Machine Learning (ICML).
(2024).
Position: Bayesian deep learning is needed in the age of large-scale AI.
In International Conference on Machine Learning (ICML).
(2024).
Position: topological deep learning is the new frontier for relational learning.
In International Conference on Machine Learning (ICML).
(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.
(2023).
Approximate blocked Gibbs sampling for Bayesian neural networks.
In Statistics and Computing.
(2022).
Challenges in Markov chain Monte Carlo for Bayesian neural networks.
In Statistical Science.
(2021).
Distributions.jl: definition and modeling of probability distributions in the JuliaStats ecosystem.
In Journal of Statistical Software.
(2020).
Wide neural networks with bottlenecks are deep Gaussian processes.
In Journal of Machine Learning Research.
(2016).
The controlled thermodynamic integral for Bayesian model evidence evaluation.
In Journal of the American Statistical Association.