Hello! I’m a computer scientist who studies programming languages and compilers. I'm currently a Lecturer at the University of Kent. Previously, I completed my PhD at Indiana University under Prof. Ryan Newton.
In addition to doing research and hacking on Scheme and Haskell code, I enjoy reading science fiction novels and watching B-movies.
My CV is here.
Daniel Marshall, Michael Vollmer, Dominic Orchard. Linearity and Uniqueness: An Entente Cordiale. European Symposium on Programming (ESOP 2022). [PDF]
Chaitanya Koparkar, Mike Rainey, Michael Vollmer, Milind Kulkarni, and Ryan R. Newton. Efficient Tree-traversals: Reconciling Parallelism and Dense Data Representations. International Conference on Functional Programming (ICFP 2021). [PDF]
Michael Vollmer. A Language-based Approach to Programming with Serialized Data. (Dissertation 2021) [PDF]
Jack Hughes, Michael Vollmer, Dominic Orchard. Deriving Distributive Laws for Graded Linear Types. Workshop on Linearity & Trends in Linear Logic and Applications (Linearity&TLLA 2020). [PDF]
Michael Vollmer, Chaitanya Koparkar, Mike Rainey, Laith Sakka, Milind Kulkarni, and Ryan R. Newton. LoCal: A Language for Programs Operating on Serialized Data. Programming Language Design and Implementation (PLDI 2019). [PDF]
Michael Vollmer, Sarah Spall, Buddhika Chamith, Laith Sakka, Milind Kulkarni, Sam Tobin-Hochstadt, and Ryan R. Newton. Compiling Tree Transforms to Operate on Packed Representations. European Conference on Object-Oriented Programming (ECOOP 2017). [PDF]
Michael Vollmer, Ryan G. Scott, Madanlal Musuvathi, and Ryan R. Newton. SC-Haskell: Sequential Consistency in Languages That Minimize Mutable Shared Heap. Principles and Practice of Parallel Programming (PPoPP 2017). [PDF]
Michael Vollmer, Bo Joel Svensson, Eric Holk, and Ryan R. Newton. Meta-programming and Auto-tuning in the Search for High Performance GPU Code. Workshop on Functional High-Performance Computing (FHPC 2015). [PDF]
Bo Joel Svensson, Michael Vollmer, Eric Holk, Trevor L. McDonell, and Ryan R. Newton. Converting Data-parallelism to Task-parallelism by Rewrites: Purely Functional Programs Across Multiple GPUs. Workshop on Functional High-Performance Computing (FHPC 2015). [PDF]