I work on interpretability, evaluation, and (mis)generalization properties of machine learning systems. I wrote a guide to graphical tensor notation for mechanistic interpretability, did a machine learning/ neuroscience internship in 2020/2021, attended the Machine Learning for Alignment Bootcamp (MLAB) in Berkeley, 2022, and the ML Alignment & Theory Scholars (MATS) Program in 2024, supervised by Lee Sharkey and Dan Braun from Apollo Research. I'm now interning at the NTT Physics & Informatics (PHI) Laboratories under Jess Riedel, before another internship at Center for Human-Compatible Artificial Intelligence under Erik Jenner.I'm also finishing up my thesis for a PhD at the University of Queensland, Australia, under Ian McCulloch. I've been working on new "tensor network" algorithms, which can be used to simulate entangled quantum materials, quantum computers, or to perform machine learning. Contact me: jordantensor [at] gmail [dot] comhttps://sites.google.com/view/jordantensor/ 94% GPA, first-class honors, Australian student prize"Jordan's work in his internship has been exemplary, with a wide variety of techniques and approaches used to deal with a challenging problem, all in a relatively short time and almost entirely on his own. What's more, his work was presented to the team in a deceptively clear and comprehensible manner, showing that he not only has an excellent understanding of the problem and methods used to tackle it, but fantastic skills in boiling down deep technical spaghetti into an easy-to-understand story - a rare and valuable skill."- Maciej Trzaskowski, Head of research at Max Kelsen, where I applied machine learning to neuroscience research.