My research aims to help developers improve the correctness, security, and performance of software systems. I am particularly interested in developing methods that are applicable to large, existing software systems, ranging from complex open-source projects to industrial-scale software. This leads to a broad array of research interests over software engineering and programming languages topics: test-input generation, specification mining, program synthesis. My interest in analyzing large software systems with fuzz testing touches the systems and security communities.

In May 2021, I received my Ph.D. at the University of California, Berkeley, where I was advised by Koushik Sen. My PhD research focused on automatic test-input generation, particularly fuzz testing, with the goal of producing inputs that induce correctness, security, and performance bugs in software. I also explored how the search techniques used to scale these input generation tools apply in the domain of program synthesis. My dissertation is available on the EECS department website.

From July 2021-2022, I was a postdoctoral researcher at Microsoft Research, NYC. There, I worked on methods to safeguard reinforcement learning and leverage deep learning for testing. In Summer 2018, I interned at Google, where I built large-scale static analysis to automatically generate fuzz targets. My team continued building on my work after my departure, resulting in the FUDGE tool. In Summer 2017 I was a research intern in the Tools for Software Engineers group at Microsoft, working on automating detection of anomolous errors in the distributed build system CloudBuild. In May 2016 I received my B.Sc. (in Combined Honours Computer Science and Mathematics) at the University of British Columbia, and was awarded the Governor General’s Silver Medal for highest standing in the graduating class of the Faculty of Science.