This blog post is going to be much shorter than my last one, so I’ll get straight to the point: I’m leaving academia. Given (a) just how theoretical my work has been, with much of it focused on classical methods, and (b) that I have declined several Assistant Professor job offers, including ones with attractive terms, in order to do so, I think this choice is going to come as a big surprise to a lot of people. Therefore, please allow me to explain myself.

A different path

Eight years ago, in 2018, I returned to academia in order to work on original research in machine learning and artificial intelligence. My plan was to spend the first few years building fundamental skills—for me, this meant learning math, as I had a lot of confidence in my software engineering ability—and then use those skills to contribute as much as I could to the science. This plan yielded about twenty-five papers at the world’s best machine learning conferences, two best paper awards (or runner-up) at ICML and AISTATS—that is, from the full conferences, not workshops. My methods are now implemented in all major software packages in my area, including those used in production at the world’s biggest tech companies. Most people, using reasonable evaluation criteria, would agree that this is an excellent track record on paper—albeit one focused on niche topics rather than today’s biggest ones.

Within that same time period, the field of machine learning has changed to an almost-unrecognizable extent. As I wrote in my previous blog post, the most important and relevant topics of 2026 require a different skillset and style of work than those of 2018. Thus, while I could continue down the same academic path I set myself on in that era, and most people in my situation would probably do so, I am not convinced this is the right path for me.¹ I am instead convinced that now is the time to take a few risks, and follow the road less traveled. So, I will be shifting course and exploring something new.

What comes next?

For my next immediate steps, I’ll be leaving Cornell in August, and returning home to California. I have a sense of what I’d like to do in my next career stage when I arrive, but at the moment it’s rather abstract, and will take some time to develop. Much of what I want to do depends on the broader AI ecosystem, and I won’t be able to get started until I arrive. In the meantime, I will be taking the next six months to retool, resharpen my skills, and prepare for what comes ahead.

There are two parts of my track record I’d like to change before my next career stage begins. First: I’d like more direct hands-on experience with the under-the-hood machinery that powers LLMs, namely sequence-to-sequence distribution-matching via transformers. I have the feeling that there are a lot more things beyond language that one can build using this technology, and I think it is critical for me to understand how it works to a sufficient degree of technical mastery. This will be the focus of my last batch of Cornell-affiliated research projects.

Second: earlier in this post, I claimed to have a lot of confidence in my software engineering skills. Why do I believe this? From experience: before starting my PhD in 2018, I worked in industry on machine learning systems. This included work with both distributed systems—specifically, asynchronous Markov chain Monte Carlo algorithms used for adjusting for early-adopter effects in A/B testing at eBay, to give one example—and GPUs, in the form of writing low-level CUDA code in the pre-TensorFlow era. None of these systems were easy to build, but very little of this track record is public. I’d like you to be confident in my software engineering skills, too—both in terms of the fundamentals, and in my ability to leverage AI—all without needing to trust my opinions about my code. So, in the coming months, I plan on putting together a side project or two to showcase what I can build.

Longer-term, I’ll avoid speculating about exactly what I’ll work on, because I do not yet know. The clearest sense I have is in what inspires me: the conviction that algorithmic decision-making under uncertainty, of the kind I have been studying over the last half-decade, has got to have a central role to play in AI—for both research and building new kinds of products. Let’s see what the next steps in making my dreams a reality are going to be!

Footnotes