Jacob Mapel
Principal ML engineer and AI development leader at a major telecom company. I work on applied AI systems at the enterprise and run a small home lab for research on the side.
This site collects the longer-form things I write: research notes, project write-ups, and the occasional opinion piece. Everything here is my own work; nothing represents my employer.
Recent research
- A JEPA world model that learns Mario by playing itself We trained a self-supervised JEPA world model on NES Super Mario Bros and ran a closed-loop flywheel — collect, retrain the world model, train a policy in imagination, repeat — for fifteen iterations. The headline iter looked like a breakthrough; a five-seed re-run revealed it was a draw from a much wider distribution than we'd been measuring. This is the project so far, and the redesign that comes next.
- Sport-transfer SSL — what scaling up actually changed After the first writeup, three follow-up questions kept nagging. With more compute we tested them all — and one downstream task we hadn't measured before flipped the story.
- Sport-transfer self-supervised learning — what we learned A one-week investigation into whether self-supervised pretraining on in-domain video improves transfer to new sports tasks — and what those experiments taught us about how to actually measure SSL progress.