In the spirit of Halloween, I decided to write about what the kids are scared of these days… advisors. I just finished my first year of my PhD. I went through A LOT this year (and wrote about it), but the thing I was surprised to be asked about the most, and that I caught myself giving advice about the most, was how I deal with feedback from my advisors in a chill way.
It’s Ph.D. application season! PLSE is recruiting Ph.D. students for the 2026-2027 cycle. We love working with students from all walks of life, united by a passion for programming languages, software engineering, and how they interact with the real world. If that sounds like you, we want you to apply!
A disclaimer: this post isn’t entirely serious; unfortunately I do not believe we should immediately refactor UW’s intro courses to only use Lean 4. It started as a joke talk at the 2026 PLSE kickoff, and it’s my own attempt to persuade you, dear reader, to switch your intro CS course to be taught in Lean. I won’t be giving the counterarguments justice. Here at UW, the wonderfully designed CSE 12X intro courses are taught in Java, so Java is the language I’ll use as a baseline for the rest of this post. I’m a PhD student, not an educator; I am interested in hearing your serious and unserious thoughts on this, especially if you’re more knowledgeable than me!
The Next 700 Programming Languages was
published in 1966 by Peter Landin.
In it were ideas and methodologies around designing and formalizing programming languages that
persist to this day.
If you’ve ever used let or where in your favourite programming language,
or if you think about a language in terms of its core calculus,
or if you separate syntax from semantics,
you’ve been programming in one of Landin’s next 700 languages all along.
Automatic differentiation (AD) is a process by which one program computing on numbers is transformed into a new program computing the derivative (in the sense of Calculus) of the original program. Over the last decade, AD has become central to modern machine learning systems: “backpropagation” through neural networks is simply a form1 of AD. I have been personally interested in automatic differentiation for years now as a way to simplify the writing of physical simulation and optimization programs.
namely “reverse-mode” automatic differentiation ↩
This blog post was written by Bryan Lu, a rising third-year graduate student in UW’s mathematics department studying algebraic combinatorics. Once upon a time, they caught the formal methods bug from their undergraduate studies at Cornell University, and learned a significant amount about proof assistants, programming languages, and compilers. As of recently, they have continued to scratch this particular interest by hanging out with the PLSE group in various capacities, and are excited to continue lurking around the lab.
PoCSci (pronounced “poxy”, etymologically derived from “Potentially Computer Science”), is a long-running computer science conference traditionally hosted by the Paul G. Allen School of Computer Science and Engineering. The venue is notable for its forward-thinking, experimental, and sometimes radical approaches to computer science. Chief among the most disruptive of the tenets held by PoCSci-goers was that every talk’s goal must be laughter, for what better gauge of absurdity and creativity could there be? At its nascence, a faction of opposing scholars stood in their austere fortresses and endured days of talks that won not a single smile; PoCSci came together each year to advance the front of laughter and make its peals echo within those bleak stone citadels.
In this blog post, I will focus on how JAX works (and where it does not work) for dynamical systems.
Modern ML training requires splitting work across clusters of GPUs to process data faster and fit model state into GPU memory. In ML systems, “parallelism” can mean several different things. Data and model parallelism distribute work across devices. Getting good performance also depends on intra-device parallelism: overlapping compute and communication operators on each individual GPU. This post gives an overview of both kinds of parallelism, then describes my work on abstractions for tuning inter- and intra-device parallelism strategies. This work is currently under submission to NSDI.
This blog post is about a paper under submission at OOPSLA 2026. If you are still reviewing for OOPSLA 2026, please stop reading now!