Yaman is a Postdoctoral Research Associate in the Department of Computing at Imperial College London. His research focuses on statistical signal processing and machine learning for complex dynamical systems, with particular emphasis on Bayesian modelling using non-Gaussian stochastic processes. He completed his PhD at the University of Cambridge, where his work studied the use of non-Gaussian process priors for learning functions with rapid temporal variation. His research lies at the intersection of learning, optimisation, and uncertainty quantification, with applications including risk management in finance and climate modelling, as well as target tracking in autonomous systems. He has also worked as a data scientist in finance and retail, developing forecasting and pricing models.