Research Area :  probabilistic machine learning, Bayesian learning, deep learning, 
Specific Interests : 
Bayesian deep learning, Bayesian non-parametrics, Stochastic processes, Gaussian processes, Neural Ordinary differential Equations, Inference algorithms.
Applications :    Computer vision, language processing,  social network analysis
My research interest lies in developing probabilistic machine learning and Bayesian data analysis techniques to solve  real world learning problems. I have developed techniques based on probabilistic methods such as Gaussian processes, Dirichlet processes and point processes, and kernel methods to solve problems in natural language processing, information retrieval and social networks.

Parametric and non-parametric Bayesian models allow the incorporation of prior information and domain knowledge.  Non-parametric Bayesian models additionally allow one to learn rich and flexible models due to their non-parametric nature and allow the model complexity to be determined by the data. This helps to overcome the problem of model selection to a great extent.  I am working on developing scalable non-parametric Bayesian models and efficient inference algorithms  for a variety of learning problems.  I am also interested  in analysing time series and textual data arising from various application domains. Current research applies Bayesian reasoning and probabilistic modelling to diverse problem domains such as numerical methods, 
optimisation,  deep learning, social networks, recommendation systems and astrophysical data. 

Kindly find more details of our work and group at  Bayesian Reasoning And INference (BRAIN)