John Kirchenbauer

I am motivated by the belief that attempting to teach machines to understand and generate natural language (and sometimes failing) is a good way to learn more about what the real building blocks of general intelligence are along the way. Currently, determining whether the impressive progress in language modeling over the past few years is simply the result of web-scale memorization in parameter space, or the actual emergence of analogs to reasoning and cognition, is one of the most pressing open questions for the field to try and answer. 

In Tom’s lab at the University of Maryland, I spent the first part of my PhD working on techniques discern whether the thing you’re currently reading or looking at was created by a human or generated by an AI system, because all of a sudden, that has become a real challenge. Generally, my research has explored various aspects of deep learning for discrete data like natural language and graphs.

Before starting my PhD at UMD, I worked at the Software Engineering Institute at Carnegie Mellon University as a research engineer (FFRDC). I completed my MS and BS in Computer Science at Washington University in St. Louis in 2020 and I received a diploma in Violin from Oberlin College and Conservatory of Music in 2017.