Personal profile

Research interests

Bayesian inference and topological data analysis provide established machine learning tools. However, deep learning poses scalability challenges to these tools. My research focuses on Bayesian deep learning (BDL) and topological deep learning (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.

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 10 - Reduced Inequalities

Research Beacons, Institutes and Platforms

  • Digital Futures


Dive into the research topics where Theodore Papamarkou is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years

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