My research concentrates on machine learning methods and their application on different real-world problems. I have worked on various range of methods to address the problems such as time series clustering and forecasting, adaptive change-point detection, risk analysis and representation learning. I have recently extended my research domain to physic informed machine learning approaches or in other words Hybridisation of simulation and machine learning with the objective to improve the performance of physics solvers.