Kieran Vlahakis

I’m an incoming Ph.D. student in Applied Mathematics at the University of Washington, working on numerical analysis, scientific computing, and machine learning. I received my B.S. in Applied and Computational Mathematics from Caltech.

My core training is in classical numerical methods, extending into numerical solvers for PDEs and approximation theory. In parallel, I’ve developed a strong background in deep learning and modern neural architectures, including LLMs and Neural Operators, through coursework and independent research.

I’m currently interested in how ideas from numerical analysis can inform the design of modern scientific machine learning tools such as Neural Operators, with an eye toward extending this machinery to problems of scientific and industrial relevance.