We focus on the data analytics and inference challenges posed by modern medicine. These are caused by the increasing complexity of medical data, such as the high number of variables (e.g. blood measurements, genomic data) that can be measured for each patient, interactions between diseases, and the heterogeneity of diseases and patient groups (no two patients with the same disease respond in the same way to treatment). The challenges are also  prompted by the new ambition of medicine: to achieve personalized treatments. To use the vast amounts of new medical data to ensure for each patient the right individual treatment at the right time. 

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Dealing with the above challenges requires novel mathematical and statistical approaches. Our strategy is to combine the precision of Bayesian inference methods (so we always know exactly what we compute, and control all sources of uncertainty) with the power of mathematical techniques from theoretical physics (so that the computationally prohibitive Bayesian calculations become feasible in practice).