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


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.

“Generating Transferable Adversarial Examples via Smooth Max Ensembling” (poster, 2017). With Yuchen Zhang, et al. NIPS 2017 Competition Workshop. Third Place Winner.

Open source

Here are my Gitlab (preferred) and Github profiles.

stanford-compendium: A compilation of notes in mathematics and CS from my time at Stanford (to date roughly 130 pages).

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

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

project-euler: Solved ~140 mathematical and algorithmic problems on Project Euler. Open source code forthcoming.


I have been involved with math Olympiads, and have written two problem books to guide student preparation.

108 Algebra Problems, with Dr. Titu Andreescu, published with XYZ Press.

109 Inequalities, with Dr. Titu Andreescu, published with XYZ Press.