Jack Dunn is a co-founder and technology leader with 12 years of experience at the intersection of optimization, machine learning, and practical software engineering. He earned a PhD in Operations Research from MIT under Dimitris Bertsimas and translates cutting-edge optimization research—like optimal decision trees and robust classification—into production-ready, interpretable AI solutions. As Co-Founder of Interpretable AI and CTO at Ground Truth Analytics, he balances research rigor with product delivery, having previously led development of OpenSolver (230k+ downloads) and contributed to core Julia optimization tooling such as JuMP and Yggdrasil build automation. His hands-on background spans solver integration, build/release engineering, and mixed-integer optimization applied in industry and at Google internships, demonstrating both systems and algorithmic depth. Based in Cambridge, MA, he combines academic honors with a track record of shipping widely adopted open-source tools that make advanced optimization accessible to non-experts.
12 years of coding experience
10 years of employment as a software developer
NCEA, NCEA at Wellington College
Doctor of Philosophy (PhD) Operations Research, Doctor of Philosophy (PhD) Operations Research at Massachusetts Institute of Technology
Bachelor of Engineering (Honours) Engineering Science, Bachelor of Engineering (Honours) Engineering Science at The University of Auckland
Collection of builder repositories for BinaryBuilder.jl
Role in this project:
Automation Engineer / Build & Release Engineer
Contributions:2 reviews, 5 commits, 3 PRs in 2 years 6 months
Contributions summary:Jack primarily contributes to building and configuring automated build processes for multiple projects within the repository. Their work involves creating recipes for BinaryBuilder.jl, updating package versions, and making necessary configuration changes for different platforms, including Windows, Linux, and macOS. They also copy generated configuration files and adapt build scripts. The contributions demonstrate expertise in building and packaging tools.
Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
Role in this project:
Back-end Developer
Contributions:7 commits, 6 PRs, 21 comments in 7 months
Contributions summary:Jack primarily contributed to the testing infrastructure and the integration of external solvers within the mathematical optimization modeling language, Jump.jl. They focused on adding and updating support for AmplNLWriter, a tool that interfaces with various nonlinear solvers. The commits involve modifying test scripts and documentation related to the integration of different solvers. These modifications enhance the testing and solver options available within the Jump.jl ecosystem.
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