Continuous Experimentation and Optimization

Modern user-facing software can be developed in short cycles with continuous user feedback from experiments. The project includes theorizing about and building tools that support this process and empirical evaluations of their use. The tools use machine learning, combinatorial optimization, and domain specific languages to specify software configurations that should be optimized with user data. The research must be evaluated in realistic environments involving real end user data at industry sites.