Probabilistic Graphical Models

In our group we study the foundations and applications of Probabilistic Graphical Models (PGMs). The group's research is centred around two research lines: PGMs for decision support systems and Foundations of stochastic computing. The first line focuses on explainability, trustworthiness, maintainability, online or federated learning etc. in Bayesian networks and other PGM models, particularly with an application in clinical decision suport systems. The second research line focuses on topics such as approximate Bayesian inference, (parameterized) complexity classes for stochastic computing, and realization of probability distributions and computations on them in novel materials and computing architectures. Our primary research methods are conceptual, computational, and formal modeling, algorithm design and analysis, and mathematical analysis. In our research we value explanations that are both scientifically rigorous and societally relevant; that scale up to 'real world settings' embodied and embedded in a realistic environment, and that 'advance the theory' as well as 'explain the data'. We are committed to fostering an open, safe, and inclusive research environment for our group members.
Group members
  • Erwin de Wolff - former PhD candidate
  • Dr Danaja Rutar - Research Associate, University of Cambridge; former PhD candidate, graduated Feb 28th, 2023
  • Dr Maria Otworowska - Founder of Skin Bliss; former PhD candidate, graduated Oct 4th, 2018
Current projects
Previous projects
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