Xavier Gonzalez
I’m an AI researcher at Unconventional AI. We are building the next generation of AI hardware and algorithms to be 1000x more energy efficient than current workflows. Join us if you are interested in tackling this pressing problem with deep and fundamental research!
I am excited about the quest to develop artificial general intelligence (AGI), and more broadly the study of intelligence—both natural and artificial.
My interests include:
- developing recurrent architectures that can more natively reason
- developing hardware-aware AI algorithms, and novel hardware that can unlock novel AI algorithms
- drawing inspiration from natural intelligence, and designing and scaling up neuro-inspired algorithms and hardware
- applying AI to education technology to improve education and mentorship for the next generation
I graduated from my PhD at Stanford in March 2026. My advisor was Scott Linderman. My thesis was awarded the Ingram Olkin interdiscplinary dissertation award.
My PhD research focused on developing and studying methods to parallelize processes previously believed to be “inherently sequential.” Examples include recurrent neural networks (RNNs) and Markov chain Monte Carlo (MCMC). My work has helped to break the sequential bottlenecks these important AI methods used to suffer from.
This ability to parallelize over the sequence length may seem like time travel or magic, but it is just an elegant application of Newton’s method! I have proved under what conditions such parallelization techniques can yield dramatic speed-ups on GPUs over sequential evaluation, and developed scalable and stable parallelization techniques. I call these parallelization techniques the ungulates—large hoofed mammals like DEER and ELK.
My PhD Disssertation serves as an intro, quick-start guide that I recommend if you want to quickly learn what paralellizing sequential computation is all about!
If you are interested in learning more, see my publications on my Google Scholar page.
