I am an extremely happy PhD student at the University of Edinburgh working with machine learning and programming languages. Currently, I am optimizing the Glasgow Haskell Compiler with reinforcement learning.
Investigating Magic Numbers: Improving the Inlining Heuristic in the Glasgow Haskell Compiler
Celeste Hollenbeck, Michael F.P. O'Boyle, and Michel Steuwer. 2022. Investigating Magic Numbers: Improving the Inlining Heuristic in the Glasgow Haskell Compiler. Haskell 2022: Proceedings of the 15th ACM SIGPLAN International Haskell Symposium (Haskell '22).
On the Impact of Programming Languages on Code Quality Emery D. Berger, Celeste Hollenbeck, Petr Maj, Olga Vitek, Jan Vitek. 2019. On the Impact of Programming Languages on Code Quality. ACM Transactions on Programming Languages (TOPLAS). ACM, Athens, Greece.
On the Complexity and Performance of Parsing with Derivatives Michael D. Adams, Celeste Hollenbeck, and Matthew Might. 2016. On the complexity and performance of parsing with derivatives. In Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI '16).
Building a Node.js Benchmark: Initial Steps Petr Maj, François Gauthier, Celeste Hollenbeck, Jan Vitek, Cristina Cifuentes. BenchWork track at ECOOP and ISSTA 2018 2018. Amsterdam, Netherlands.
Investigating Magic Numbers: An Evaluation of Inlining in the Glasgow Haskell Compiler Haskell Symposium, 2022.
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