Michael Lindsey

Email: lastname at berkeley dot edu
Office: Evans 869

I am a second-year graduate student in Mathematics at Berkeley. I am interested in analysis, anywhere from around zero to two levels removed from application outside of math, especially PDEs, probability, and numerics. (To get a further sense of my interests, see what I've been reading.) Previously, I was an undergraduate at Stanford, where I majored in math. Here is my CV.

Currently I am researching analysis and numerics for electronic structure theory, as well as numerical methods for nonlinear elliptic PDEs. My advisor is Lin Lin.

Publications and preprints

Convergence of adaptive compression methods for Hartree-Fock-like equations (with Lin Lin), preprint (2017).

Optimal transport via a Monge-Ampère optimization problem (with Yanir Rubinstein), preprint (2016).

On discontinuity of planar optimal transport maps (with Otis Chodosh, Vishesh Jain, Lyuboslav Panchev, and Yanir Rubinstein), Journal of Topology and Analysis 07, 239 (2015).
[arXiv:1312.2929] [journal]
(Note: this research was largely carried out during SURIM 2012.)

Infrared imagery of streak formation in a breaking wave (with Robert Handler and Ivan Savelyev), Physics of Fluids 24, 121701 (2012).

Other research writings

Two views on optimal transport and its numerical solution (2015). My undergraduate thesis (supervised by Yanir Rubinstein and Rafe Mazzeo), which presented two new formulations of optimal transport problems leading to two corresponding methods for numerically solving them.
[link forthcoming]

Asymptotics of Hermite polynomials (2015). A largely expository paper (for a course on orthogonal polynomials) about asymptotics of Hermite polynomials and the Gaussian Unitary Ensemble (GUE). Presents a result about the stationary states for the quantum harmonic oscillator, which, though likely nothing new, I think is fairly cool.
[link forthcoming]

Spectral methods for neural computation (2013/14). A presentation for the Brains in Silicon lab outlining some ideas about how favorable Fourier-domain properties of certain neural tuning curves are naturally suited (in an idealized setting) for the computation of simple functions.

3D Shape Understanding Using Machine Learning (2013). A presentation about my work on using a deep learning framework to perform labeled segmentation of discrete surfaces and extract multiscale learned shape descriptors. To help with training, I introduced a new set of shape descriptors based on conformal maps.
(Note: This research was carried out during CURIS 2013 in Stanford's Geometric Computation Group.)

The k-discs algorithm and its kernelization (2012). Introduces and analyzes an extension of k-means allowing cluster centroids to be discs of arbitrary dimension, capable of recovering more diverse cluster geometries.
(Note: this research was carried out as a class project for CS 229 at Stanford.)


Spring 2016: Math 53, Professor Auroux [quizzes]
Fall 2015: Math 1B, Professor Hald