Miguel Liu-schiaffini is a computer scientist and PhD student at Stanford with five years of research experience applying deep learning to scientific problems, currently a Graduate Research Assistant after internships at NVIDIA Research and prolonged work at Caltech's Anandkumar Lab. He specializes in neural operators for solving PDEs and forecasting dynamics of chaotic, non-stationary systems, with papers at NeurIPS 2022 and ICML 2024 reflecting a strong theoretical-and-application blend. His earlier work at UT Austin’s Institute for Geophysics produced a TGRS paper on ice-bedrock interface detection from airborne radar and unsupervised methods for Mars surface characterization, bridging ML and geoscience. Miguel’s research emphasizes physically-guided, principled deep learning methods that incorporate scientific priors, and he has a track record of turning domain measurements into actionable models for Earth and planetary science. An oft-overlooked strength is his ability to span end-to-end projects—from proposing new problems to publishing and presenting results at major conferences—making him effective both as a solo researcher and in collaborative lab settings.
5 years of coding experience
4 years of employment as a software developer
California Institute of Technology
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Stanford University
Updating neuraloperator package with MNO code and examples.
Contributions:43 pushes, 3 branches in 1 year 5 months
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Miguel Liu-schiaffini - Graduate Research Assistant