Mathematics has always fascinated me and truly comes alive for me especially with its applications in Machine Learning. I am a Physics and Computer Science Graduate with industry experience in Financial Services and Technology sectors as Analyst, Product Manager, Programmer and Technical Sales. However, with respect to my career interests it has always been my desire to be in research and academia. I recently got the rare opportunity to work with the MET Office on a deep-learning project for uncertainty estimation in ensemble weather forecast post-processing; proposing a model for bias-correction using data from UKMO 3-member ensemble which I found incredibly rewarding.

 

My EPSRC funded PhD focuses on Spatio-temporal statistical models to improve short term rain forecasts by combining statistical models like Latent Gaussian models with physical dynamical modelling. Efficient handling of large data sets through innovative methodology remains an active area of research which will be developed by exploring the concept that Space-Time Gaussian Process can be defined through Stochastic Partial Differential Equations (SPDEs) along with other proposed techniques like, sparse matrix methods and Laplace approximations through which calculations can be done efficiently for large data sets common in spatio-temporal applications.

 

Overall, I am interested in research problems that will have impact either in weather and climate, energy, or health. I am on a quest to build technical depth and pursue advanced research work which will extend Mathematics with applications in Machine Learning.