Jean Yang interviews 15 programming languages researchers about their work, methods, motivations, and history.
Working through a programming exercise, we see that Lisp’s “Code is data” philosophy is a natural enabler for program synthesis.
The authors of POPL’s 2019 most influential paper reflect on lessons learned: (i) in research, ask daring questions far beyond current capabilities; (ii) develop compositional techniques, which confer important benefits that increase impact; (iii) work in PL theory: now is a great time for it!
POPL is the premiere conference on the theoretical foundations of programming languages. The PC Chair, General Chair, and Steering Committee Chair of POPL 2020 review this year’s event.
The wealth of code now available on-line is fertile ground to enable machine learning to be applied to programming tasks. This post is the second in a series on this topic, focusing on the tasks of semantically labeling and captioning code.
As quantum computers become more practical, there is a rich opportunity to advance the development of tools to assist in the process of programming them, both now and in the future. To encourage more PL-minded researchers to work in this exciting new area, we established the Workshop on Programming Languages for Quantum Computing (PLanQC).
The history of machine-checked proofs about programming languages offers valuable lessons for the future of programming languages research.
Will machine learning automate programming out of existence, as it is doing for many other professions?
Open Access publication models aim to make scientific results accessible to everyone. How will we pay for them?
An impressive number of transformations in both compilers and in ordinary programming are special cases of a transformation called “defunctionalization.” This post explains what it is and the many places it’s useful.