Ben uses Haskell in fields as diverse as applied machine learning, scientific data analysis, robotics control, and compiler engineering. He is an active contributor to the Glasgow Haskell Compiler with focus on code generation, Core optimization, and the runtime system, and has worked extensively towards bringing GHC to the ARM architecture.
He has experience implementing numerical methods for machine learning, with an eye towards leveraging Haskell’s strong type system to enforce correctness at compile-time. Ben has expertise developing high-performance distributed systems in a wide range of languages. Past projects include GPU-based modelling of high-energy particle interactions and an implementation of parallel machine learning algorithms for large-scale social network analysis.
He is completing a PhD in Physics at the University of Massachusetts and has publications spanning social network analysis, computational physics, and biophysics.