I am in the fifth and final year of my Ph.D. at MIT Mathematics, advised by Ankur Moitra and co-advised by Jon Kelner, and working at the interface of data science, machine learning, optimization, statistical physics, probability, and abstract algebra.

I design and study algorithms to analyze very noisy data. My work centers a family of noisy geometric problems arising in structural biology (cryo-EM), robotics, image processing, signals processing, community detection in networks, and more. I draw from random matrix theory and other theory of random structures; from statistical physics, the replica method, and the idea of phase transitions in data; from representation theory and invariant theory to exploit rich problem geometry and symmetry; and from convex optimization, semidefinite programming, and the sum of squares hierarchy as tools and as a perspective on algorithms in general.

Moving forward, I am excited to build on my work on cryo-EM by immersing myself further in the life sciences, and bringing data science to bear on further meaningful scientific problems.

I spent my undergraduate years at Keble College, Oxford, mainly thinking about algebraic topology, and building a background in pure mathematics that has since proven invaluable.

Send emails to ameliaperry at mit.