Hi Squircle!

I mostly left academia after graduating, and am not actively following current research, although I still live in Cambridge and attend a fair number of research events. I can say that deep learning has taken over PL research, just like everything else. A good number of PL and synthesis people have shifted heavily into pure deep learning, and a large proportion of those that haven't are working either on applying deep learning and LLMs to solve PL problems, fusing PL and ML techniques (aka "neurosymbolic programming" -- the Scallop project from Mayur Naik's group is particularly exciting to me), or finding ways to use PL techniques to solve ML problems. For an example of the latter, I just flipped through this year's PLDI papers, and one caught my eye that sounds like it has nothing to do with AI, "Hashing Modulo Context-Sensitive Alpha Equivalence." (Decoding the jargon, that means "How to deal with an enormous set of programs that contain lambdas" -- something that comes up when doing search-based synthesis and superoptimization.) Its abstract ends: "We have employed the algorithm to obtain a large-scale, densely packed, interconnected graph of mathematical knowledge from the Coq proof assistant for machine learning purposes."

I think PL techniques do provide the key to overcoming a lot of the problems with LLMs. You want your LLM to have better correctness, reasoning, and goal-directed search -- researchers in programming languages and formal methods are expert at this. I am here to some extent conflating PL/FM techniques with traditional automated reasoning, but I think that's a fine conflation to make, because such techniques have been incubated by the PL/FM communities for the past 30 years since they were sidelined by AI. Case in point: very large fraction of computational logic and automated reasoning papers are motivated by problems in program analysis, verification, and synthesis.. https://easychair.org/smart-program/FLoC2022/index.html

As for what I have been up to: Trained a few hundred more software engineers, mirdin.com . While I mostly stayed away from AI stuff during my Ph. D., I've given in to the tides, and am now building a startup using my AI, PL, and pedagogy expertise to solve all problems related to codebase-learning (onboarding new hires, changing teams faster, etc).

At the very end of my Ph. D., I discovered a new program synthesis technique based on constrained tree automata, and used it to build a synthesizer which is 8x more performant on one benchmark than the previous SOTA while using 10x less code. https://www.jameskoppel.com/files/papers/ecta.pdf . So the research I've done since graduating has largely been follow-ups to that. See https://www.computer.org/csdl/proceedings-article/icst/2023/... , https://pldi22.sigplan.org/details/egraphs-2022-papers/4/E-G... . I'm currently on two collaborations. One is continuing to develop algorithms for new kinds of constrained tree automata that can synthesize more kinds of programs. The other is an outgrowth of my startup: an empirical study on existing tools for codebase learning.

Anyway, that's not a comprehensive answer on what to watch out for in the field, which I am not presently qualified to give if I ever was, but it's the things that have my attention.

Oh, but: watch Isil Dillig. Everything that comes out of her lab is good.