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.
Advice for those considering a PhD: make sure you find a good fit and you know what you're getting into. Ask everybody a lot of questions. I am more than happy to talk about my experiences with any of the universities, advisors, or groups that I've worked with—the University of Utah, Northeastern University, and the University of Edinburgh. See contact information below.
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). ACM, New York, NY, USA, 224-236.
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 Scottish Programming Languages Seminar, 2021.
Did you know I'm on LinkedIn? Well. I am.
Sometimes I say things on Twitter.