Strong experience in developing optimization algorithms for a wide area of engineering applications like aerospace, locomotives, microgrids and renewable energy sector.Rich experience in applying machine learning methodologies to aerospace, locomotives and renewable energy sector.8 years of industrial experience, worked with General Electric Research, Airbus defence and space, European Space Agency, UK Space Agency.Applied machine learning methods to reduce the search space by characterizing it into safe and failure regions.Developed an optimization algorithm to minimize new road length to connect wind turbines in a farm.Applied hyper parameter optimization to reduce the model error that predicts the time series loads of a Wind Turbine.Applied machine learning technique to develop predictive models on the fly in order to increase convergence time of the optimization. Developed a real time optimization framework to optimally operate an isolated microgrids with aim to minimize solar spillage and green house gases.Applied machine learning techniques to predict and control the emissions of dual fuel locomotives.Developed Autotuning methodology for a Generic Rover Dynamic Model with the aim of improving the performance of Lunar and Mars ro ers on sand and rocky surfaces.As part of PhD project, developed Robustness analysis tool that employed Montecarlo, local, global and hybrid optimization methods for verification and validation of controllers.