(Note: this compilation is incomplete and out of date.)



Stanford CS43: Functional Programming Abstractions. A class I teach at Stanford with Isaac Scheinfeld and previously Allan Jiang. Winter 2017, Winter 2018, Winter 2019, Winter 2020 quarters.


Editor, The Gradient. A digital publication focused on reporting on the state-of-the-art in machine learning.


“Variant Calling with Machine Learning” (2018). Filed as provisional patent. Adithya Ganesh, Kyle Beauchamp et al., with Counsyl, Inc.


“Deep recurrent neural networks for accurate variant calling in 21-hydroxylase-deficient congenital adrenal hyperplasia” (poster, 2018). With Kyle Beauchamp, et al. ASHG 2018.

Open source

Here is my Github profile.

torcs-autopilot: Deep reinforcement learning for simulated autonomous driving. Stanford CS229 final project.

project-euler: Solved ~140 mathematical and algorithmic problems on Project Euler.

entropy: Algorithmic information theory: notes and code.

ChromaNet: Deep learning for genomic chromatic profile prediction. Stanford CS273B final project.

meta-research: WIP (meta-research for AI, with The Gradient).


In a past life, I was involved with math contests. I have published two problem-books to guide student preparation: 108 Algebra Problems, and 109 Inequalities.