I am interested in machine learning, graphics programming, and theoretical computer science. If you're an employer, please take a look at my resume.
Here are some projects I've worked on. Most of them are open sourced and can be found on my github.
A WebGL library for creating interactive 2D/3D applications and animations with TypeScript. Supports PBR and other advanced rendering techniques.
A novel method of using transformers to learn the integral of sparse functional data for use in imputation and dimensionality reduction.
A tiny, pedagogical neural network library with a pytorch-like API and tensor-valued autodiff.
A ray tracing engine built from scratch in the Code.org JS environment, in part to demonstrate the versatility of the platform.
An open source JavaScript framework made in less than 10kb. Supports SPAs and state-based rendering.
A tool for businesses to give and receive feedback from their customers, used by ~100 businesses to collect and manage their customer feedback.
I sometimes use writing as a means of sharing interesting explanations of concepts or to simply document my own learning.
Some proofs for the theorems in Prof. Alexander Kurz's notes introducing Stone Duality.
A comprehensive article about neural networks and how they work (mathematically), complete with an implementation of neural nets in Python.
A description of how Taylor series so accurately approximate functions. A proof of Taylor's theorem is presented through a set of guiding exercises.
The proof of Cauchy's mean value theorem presented through a set of guiding exercises. The post is written with calculus students in mind.
A presentation of the proof that $\pi$ is irrational using techniques familiar to calculus students, with a formal introduction to rational/irrational numbers.
An introduction to decentralized applications (dApps) and blockchain technology, with emphasis on the technical details of dApp platforms.
Here are some resources or software which I would recommend.
A visualization of assembly language instructions on a basic Von-Neumann Computer.
An excellent resource (textbook) for learning about neural networks.
A GUI editor for TikZ graphs and diagrams.
A modern commutative diagram editor with support for exporting as LaTeX, link.
A free and well-written graduate analysis text written by Sheldon Axler, whose (not free) Linear Algebra textbook is also highly regarded.