Foundations of Natural and Stochastic Computing

In our group we study the foundations of natural and stochastic computing. The group's research is centered around two research lines: Foundations and applications of probabilistic graphical models and Foundations of neuromorphic computing. In the first line we study topics such as computational complexity, approximate inference algorithms, Bayesian statistics, non-parametric Bayes, explainable AI (in particular in the healthcare domain), and computational modeling of cognition, focused on the Predictive Processing account in neuroscience. In the second line we study topics such as computational complexity, neuromorphic/hybrid algorithm design (in particular with respect to Green ICT), hardware-software co-design in neuromorphic engineering, brain-inspired models of computation, and philosophy of neuromorphic computation. Our primary research methods are computational and formal modeling, conceptual analysis, algorithm design, and mathematical analysis of information representation and processing in frameworks such as Bayesian networks and spiking neural networks. In our research we value explanations that are both mathematically rigorous and biologically plausible; 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.
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