Scholar
Xavier Gonzalez
Google Scholar ID: 5sj7cH8AAAAJ
Stanford University
Citations & Impact
Citations
28
H-index
2
i10-index
1
Publications
8
Co-authors
12
list available
Publications
Resume (English only)
Background
- Final-year PhD student in Artificial Intelligence at Stanford University.
- Research focuses on parallelizing dynamical systems traditionally considered inherently sequential, such as recurrent neural networks (RNNs) and Markov chain Monte Carlo (MCMC).
- Developed a family of parallelization techniques called 'ungulates'—including DEER and ELK.
- Deeply interested in artificial general intelligence (AGI) and the broader study of intelligence, both natural and artificial.
- Specific interests include: recurrent architectures as alternatives to Transformers for native reasoning, hardware-aware AI algorithms and novel hardware, neuro-inspired algorithms inspired by natural intelligence, and applying AI to educational technology.
