Rachel Lawrence
Graduate Student, UC Berkeley
Rachel is a second year PhD student in Computer Science at UC Berkeley working with Alistair Sinclair. She is interested in several areas intersecting theoretical computer science, including quadratic dynamical systems, graph algorithms, finite geometry, and privacy in machine learning. Previously, Rachel worked at Reservoir Labs, Inc. on efficient sparse tensor decomposition algorithms and compiler design, and at Pixar R&D on simulation tools. Rachel completed her undergraduate degree in Applied Mathematics at Yale University in 2016, where she was advised by Daniel Spielman and worked with Asaf Ferber on methods for counting hamiltonian cycles for her undergraduate thesis.