RESEARCH

Research Area : Bayesian data analysis, probabilistic machine learning.
Specific Interests : Bayesian non-parametrics, Inference algorithms, stochastic processes,
  survival analysis,  social media and text 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 Big data scenario.  I am also interested  in analysing event history data and temporal textual data. Current research applies Bayesian reasoning and probabilistic modelling to diverse problem domains such as optimization, numerical methods,  deep learning,   social networks, healthcare and astrophysics.