Miguel Liu-Schiaffini

I’m a first-year PhD student in the Computer Science Department at Stanford. Previously, I was a research intern in the Learning and Perception group at NVIDIA Research and a member of Anima Anandkumar’s AI + Science lab Lab at Caltech, where I did my undergraduate studies. I also spent time as a research intern at the University of Texas Institute for Geophysics.

My research has focused on the theory and applications of neural operators and operator learning, particularly in their application to solving partial differential equations (PDEs). For instance, I have worked on developing neural operators for forecasting in chaotic, non-stationary, and stochastic time-dependent systems. I am very passionate about the scientific applications of machine learning and have worked on using machine learning to forecast climate tipping points, estimating glacial ice thickness, and characterizing features of the Martian terrain.

I am grateful to be supported by the NSF Graduate Research Fellowship. During my undergraduate studies, I was supported by the Mellon Mays Undergraduate Fellowship and was honored to receive the 2024 Barry Goldwater scholarship.