I do research on data intensive software systems, to produce “Trustworthy and Secure Software Systems and Analytics” in the age of large-scale cyber software and crowd-shared knowledge.
My research analyzes vast amount of software repository and online data to produce reliable insights and analytics that can be used to facilitate the improvement and adoption (social/technical) of techniques for dependability (e.g., cyber security analysis) and documentation support of software systems. My work often combines Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Software Engineering (SE) techniques and applies those on the vast amount of code and textual software data. See DISA lab page for more details.
My research is influenced by my 10+ years of full-time professional experience at the industry, both as a data scientist and as a software developer. I was a Senior Data scientist at the Data and Statistics Office of Bank of Canada. I also worked at IBM Canada, both as a researcher and as a software engineer. At IBM, I was part of the IBM Watson Analytics team.
In my PhD, I leveraged Natural Language Processing and Machine Learning techniques on the vast amount data available in online software repositories. I created Opiner, an opinion search and summarization engine for APIs (Application Programming Interfaces) by automatically crawling online developer forum where usage of APIs is discussed by software developers. APIs are interfaces to reusable software components. Opiner website is available online [Please feel free to check out the website and share your opinions].
I have multiple openings for full-funded Masters and PhD students. I am looking for motivated and hard working students. Please see details about students here.