My research revolves around targeted data subset selection for improving the performance of machine learning models in realistic dataset scenarios like class imbalance, redundancy and out-of-distribution data.

Another aspect of my research involves the use of techniques such as Active Learning and Submodular subset selection to train deep models on significantly less data, without compromising on their accuracies. I also work on visual data summarization which involves generating generic, query-focused or privacy preserving summaries of image collections or videos.