# 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).

[arXiv:1703.05441]

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

[arXiv:1603.07435]

**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).

[journal]
## 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.

[slides]

**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.

[slides]

(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.

[link]

(Note: this research was carried out as a class project for CS 229 at Stanford.)
## Teaching

Spring 2016: Math 53, Professor Auroux [quizzes]

Fall 2015: Math 1B, Professor Hald