Kotlin is a multi-platform programming language with compiler support for JVM, JS and native targets. The language emphasizes static typing, null-safety and interoperability with Java and JavaScript. In this work, we present an algebraically grounded implementation of forward and reverse mode automatic differentiation written in pure Kotlin and a property-based test suite for soundness checking. Our approach enables users to target multiple platforms through a single codebase and receive compile-time static analysis. A working prototype is provided at: https://github.com/breandan/kotlingrad

I am a Master’s student at the University of Montreal under the supervision of Liam Paull and Michalis Famelis. My research interests include reinforcement learning, differentiable programming and software engineering. I have an applied background in machine learning, with experience in programming languages and developer tools. I am enthusiastic about AI as a tool for improving human potential.