I'm a fourth year doctoral candidate at the MIT Operations Research Center. My primary research interests are optimization under uncertainty, the intersection of machine learning and optimization, and software tools for optimization.
I've taught analytics and related software tools to graduate students at MIT, MBAs and EMBAs at the MIT Sloan School of Management, and over 30,000 students around the world (and counting) as part of the EdX online class "The Analytics Edge".
I have created and contributed to many open source projects, many relating to the Julia programming language. I'm a co-founder of the JuliaOpt organization, which aims to make Julia the language of choice for optimization. Examples include JuMP, an algebraic modeling language for optimization, and JuMPeR, an extension of JuMP for robust optimization. I lead and maintain the Julia package ecosystem testing infrastructure.
I've previously worked as a quantitative analyst at Google (developed new algorithms for the search engine, tested by mashing some big data), and in various engineering and analyst intern roles in New Zealand. I obtained my undergraduate degree in engineering from the University of Auckland.
I am one of the founders of the JuliaOpt organization, a collection of optimization-related packages for Julia. I am a co-creator of JuMP, a modeling language for optimization problems. I've also been involved in creating and maintaining several of the solver wrappers. I have spoken about JuliaOpt at JuliaCon 2014, at UC Berkeley, and in the talks and papers about JuMP mentioned above.
I am the creator and maintainer for the official Julia package listing. The listing is automatically generated by PackageEvaluator.jl, which tests every registered package every day on both release and unstable Julia. Tests run in a Vagrant-launched VirtualBox Ubuntu VM. I'm submitted patchs to a large number of Julia packages to improve their stability, testing, and deployability. I talked about the package ecosystem at JuliaCon 2014.