I am interested in the applications of data science and machine learning for software engineering. Specifically I am working on the detection and prediction of defect/anomalous behaviour in software. This also requires using big data analysis in practice. Previously, I have worked on the detection of anomalous behaviour and emergent behaviour in MultiAgent Systems. Currently, we are analyzing software energy bugs in mobile applications from two perspectives: Software and Users. We analyze massive sets of data from GitHub for a personalized recommender system for GitHub users. The main analysis for this project comes from the similarity of software projects. Secondly, we are analyzing comments and reviews from social network platforms and other sites such as Twitter, and Google Play in addition to time series analysis of the users’ input to gain more insights about mobile applications’ energy bugs. This is combined with other application ranking input data from various resources such as Avast and Google Play to get a richer analysis for mobile applications. The corresponding visualization of the results will be published on the website in a few months.