My interest lies in reasoning under uncertainty for analysis and prediction over heterogeneous and relational data using probabilistic models. Tools and techniques that I use include Parametric and Non-parametric Bayesian models that allow incorporation of prior information and domain knowledge, and Probabilistic Graphical Models that use factorizations and graph structures for efficient learning and inference. I am also interested in the design of scalable inference and learning algorithms over large data. I have explored a wide range of applications, including entity resolution and deduplication in databases, word-sense disambiguation, text mining, natural language processing, sentiment analysis and opinion mining, social interaction analysis, system log analysis, service mining from legacy software, and more recently, learning knowledge graphs from semi-structured data.

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