His main areas of research lie in multiscale statistical modelling and machine learning. In particular, Professor Fieguth concentrates on the theory development and understanding of hierarchical/scale recursive estimation algorithms for multi-resolution stochastic processes. Such algorithms use a statistically meaningful strategy to break large estimation problems into smaller pieces, leading to vast improvements in efficiency. His particular passion focuses on complex systems (that is, nonlinear dynamic systems and the consequences of such systems). He has a Springer textbook on this subject, and is always happy to hear about new, motivating, multi-disciplinary examples that could bring a deeper understanding of complex systems.