I'm a second-year PhD student in Computer Science at Georgia Tech interested in continuous optimization, numerical analysis, and online learning. I'm currently working with Jacob Abernethy.
Previously, I studied math at the University of Michigan, where I also helped with a machine learning class and reading group. Off-campus I like to bike, cook, and play piano. Maybe one day I'll make more games.
If you'd like to chat, feel free to write me at firstname.lastname@example.org.
- Fall 2018: I'm a teaching assistant for CS 4540: Advanced Algorithms, taught by Jacob Abernethy.
- May 2018: I spent two weeks visiting the Data Science & Online Markets group at Northwestern.
Many applications require the random sampling of matrices with prescribed structure for modeling, statistical, or aesthetic purposes. What does it mean for a random variable to be matrix-valued? What can we say about the eigenvalues of a random matrix? How can we design algorithms to sample from a target distribution on a group or manifold? More generally, what can we say deterministic algorithms with random inputs? Our study of random matrices will lead us to the subgroup algorithm (Diaconis 1987), which subsumes many familiar random sampling procedures.
In geometry, the hyperplane separation theorem is a theorem about disjoint convex sets in n-dimensional Euclidean space. There are several rather similar versions. In one version of the theorem, if both these sets are closed and at least one of them is compact, then there is a hyperplane in between them and even two parallel hyperplanes in between them separated by a gap. In another version, if both disjoint convex sets are open, then there is a hyperplane in between them, but not necessarily any gap. An axis which is orthogonal to a separating hyperplane is a separating axis, because the orthogonal projections of the convex bodies onto the axis are disjoint.
These notes provide a theoretical treatment of Expectation-Maximization, an iterative parameter estimation algorithm used to find local maxima of the likelihood function in the presence of hidden variables. Introductory textbooks (MLAPP, PRML) typically state the algorithm without explanation and expect students to work blindly through derivations. We find this approach to be unsatisfying, and instead choose to tackle the theory head-on, followed by plenty of examples. Following (Neal & Hinton 1998), we view expectation-maximization as coordinate ascent on the Evidence Lower Bound. This perspective takes much of the mystery out of the algorithm and allows us to easily derive variants like Hard EM and Variational Inference.
Simulation of two-dimensional incompressible flow with periodic boundary conditions, achieved by solving the Euler Equations in the frequency domain via the Fast Fourier Transform (FFT). Written in Java.