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Theodore Papamarkou

Reader in Mathematics of Data Science
What is the focus of your current research?

Research interests: Bayesian deep learning, approximate Monte Carlo, mathematics of data science, uncertainty quantification, approximate inference.

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

What are some projects or breakthroughs you wish to highlight?

1) advances in neural network Gaussian processes

2) Bayesian deep learning using Monte Carlo methods

3) deep learning on topological spaces.

What memberships/awards/roles do you hold/have you held in the past?

Current: associate editor at the ‘Journal of Statistical Software’ and at the ‘Foundations of Data Science’ journal

Past: strategic hire in artificial intelligence at the Oak Ridge National Laboratory.

What is the biggest challenge in Data Science and AI right now?

Biggest challenge in data science and AI: trade-off between stability and accuracy.

What real world challenges do you see Data Science and AI meeting in the next 25 years?

1) human-level artificial intelligence

2) elimination and eradication of disease

3) lengthening of lifespan

4) scalable quantum computing.

Find out more about Theodore’s research at Research Explorer.