My research interests are primarily in software engineering (SE), with an emphasis on improving software engineers tools and environments through data mining, text analysis and machine learning. I am especially interested in mining software repositories at a large scale, extending data analytics solutions to transform the plethora of information available in software artifacts into actionable nuggets of knowledge and tools, useful for both software engineers and researchers. I am also broadly interested in empirical software engineering. Research Statement  Teaching Statement 

My dissertation is focused on analyzing developer chats, which are an important and previously unexplored resource in SE. I build tools based on evidence from quantitative and qualitative empirical studies, and adapting state-of-the-art techniques from the fields of Natural Language Processing and Machine Learning. Through my research, I intend to enable advances in areas including building/enhancing recommender systems for developers, information retrieval tasks from unstructured developer communications, and virtual assistants for software engineers.