I am a member of the newly formed Data Analytics and Intelligence Research (DAIR) Group. Earlier, I worked as a postdoctoral fellow with Raymond Mooney at the University of Texas at Austin.

My broad interests lie in the area of Machine Learning. Specifically, my reseach focus has been the newly developed area of Statistical Relational Learning (SRL) which aims to combine the power of logic and probability. I have extnesively worked on one such widely used model by the name Markov Logic. I was one of the developers of Alchemy, the first open source implementation of Markov Logic. The key to scaling up inference in SRL models is to exploit the underlying symmetry of the model for efficient inference and learning (referred to as lifted inference and learning). I have done some pioneering work on the problem of lifted inference in SRL models and my core research focus continues to be along the same lines. I am also interested in looking at efficient inference techniques for Computer Vision problems. Peripherally, I have done some work on applying machine learning techniques to problems in Social Network Analysis.