I am a researcher in software systems area. My research goal is to make it easier for software professionals to create correct, secure and efficient software. My research interests include static and dynamic analysis, software verification, type systems, domain-specific languages, program synthesis, computer-assisted debugging, and the use of machine learning ideas in software productivity.

In the past couple of years, I have focused on applying machine learning based methods to developer productivity tools at Facebook. I have worked on several successful projects in this space, including code search, automatic bug fixing, and predictive test selection.

As an experienced researcher in corporate labs, I wear two hats. I work on research projects, write papers, and collaborate with my academic colleagues. I have written over 50 papers, most of which appear in the top ACM SIGPLAN/SIGSOFT conferences. In the other role, I try to align my research to have business impact by way of new products or services, or by improving internal productivity. Many of my projects have resulted in successful tech transfer with significant business impact: bug-finding tools for Java, test automation for web applications, JavaScript performance tools, and most recently, the projects on using ML techniques for developer productivity.

I consider myself fortunate to have worked in many areas (programming languages, software engineering, high-performance computing, and others), in many industries within IT sector (software, IT services, consumer electronics, communication equipment), in five different geographical locations, in three different corporate cultures, and in a variety of organizational roles (individual researcher, manager, developer and strategist.)