Aaron Geelon So
PhD Computer Science, University of California, San Diego
MS Computer Science, Columbia University
BS Mathematics, The University of Chicago
I’m an PhD student working on machine learning, hoping to help make knowledge and technology broadly accessible and democratic. Major challenges I’m excited to tackle include resource constraints, technological literacy, and privacy/security.
I’m interested in learning theory, theoretical computer science, and geometry; these fields are all related the design of algorithms that require less time, space, and data. My thesis focuses on active learning, a subfield of machine learning that aims to use interactivity to reduce the amount of data needed to learn. I also enjoy perspectives from algebra, functional analysis, algebraic geometry, and information theory.
Here is my MS thesis written under the supervision of Prof. Daniel Hsu at Columbia University.
Email: geelon.so [at] columbia [dot] edu