The increased availability of massive codebases (e.g., GitHub), a term referred to as ``Big Code'', creates an exciting opportunity for new kinds of programming tools based on probabilistic models. Enabled by these models, tomorrow’s tools will provide statistically likely solutions to programming tasks that are difficult or impossible to solve with traditional techniques

In this talk, I will present a new approach for building such probabilistic tools based on structured prediction with graphical models. As an example, I will discuss JSNice (http://jsnice.org), a now popular system that automatically de-minifies JavaScript programs. I will also touch on some of our latest results including a new probabilistic model which generalizes several existing efforts and enables creation of tools with precision and scalability not possible before.

I am originally from Sofia, Bulgaria where I was born and grew up. I am an Assistant Professor of Computer Science at ETH Zurich where I lead the Software Reliability Lab. Prior to ETH, I was a Research Staff Member at the IBM T.J. Watson Research Center in New York. I obtained my PhD from Cambridge University, England and my B.Sc. from Simon Fraser University. Before Canada, I studied at the Sofia Math High School in Sofia, Bulgaria. I am interested in program analysis, program synthesis, application of machine learning to programming languages, and concurrency.