David Palmer

David Palmer is completing a National Science Foundation postdoctoral fellowship at Harvard University.

As a doctoral student in computer science at MIT, Palmer’s research applied convex relaxation methods to geometric optimization problems that arise in applications like computer graphics and vision and computational engineering. Problems like surface reconstruction and hexahedral meshing feature topological barriers that lead to nonconvexity and initialization-dependence. By reframing our computational representation of the underlying geometry, we can relax these barriers to find more reliable algorithms.

David became obsessed with differential geometry at an early age, initially wanting to learn the language of general relativity. A side interest in computer generated art led him to explore the computational applications of differential geometry. At Harvard, David pursued a broad course of study in computer science and mathematics. He had the great opportunity to study geometry and computer graphics under Professor Steven Gortler. This led to a senior thesis on a path toward computing quasiconformal maps between surfaces via discrete measured foliations.

After earning his bachelor’s degree in computer science at Harvard, David went on to intern at Pixar Research, where he worked with Fernando de Goes on tools for manipulating discrete vector fields and on a fluid simulator based on power diagrams. In order to deepen his background in pure mathematics, David spent a year studying toward a master’s in math at the University of Cambridge, generously supported by a Herchel Smith fellowship. There he focused on differential geometry and topology.

David was born in Chicago, Illinois and spent his childhood in Deerfield, Illinois. He enjoys singing, baking sourdough bread, and keeping bees.