Floris Persiau

Visiting Postdoctoral Fellow

Bio

After finishing his engineering physics education at Ghent University, Floris started a Ph.D. at the Foundations Lab for imprecise probabilities. His Ph.D. research is situated in the intersection of two research fields: imprecise probabilities and algorithmic randomness. The field of algorithmic randomness, on the one hand, typically studies what it means for an infinite outcome sequence to be random for a probability measure; as a canonical example, consider the repeated flipping of a fair coin—so with probability 1/2 for heads—and consider what it means for a single infinite outcome sequence to be random for the induced Lebesgue measure. The field of imprecise probabilities, on the other hand, develops alternative and (even) more general uncertainty models that allow for reasoning in an informative and conservative way, even in those situations where it’s infeasible or inappropriate to specify a single probability (measure). These two fields come together in his Ph.D. research by allowing for more general so-called imprecise forecasting systems—which can be interpreted as a set of probability measures—in several classical randomness notions and by studying how the corresponding generalisations shine new light on our understanding of random sequences.

Floris is currently a postdoctoral BAEF fellow at CMU, and his research interests expand to the union of both research fields. He recently finished (i) a joint project with Francesca Zaffora Blando on the characterisation of several canonical precise randomness notions in terms of the stable satisfaction of various minimal randomness properties, and (ii) a joint project with Jasper De Bock and Alexander Erreygers on convergent limit-behaviour of imprecise Markov chains.