Christopher Waites

Computer Science @
Stanford University 🌲

Broadly interested in deep
learning, privacy, ethics,
and generative modeling

Fortunate to have been
advised by the excellent
Rachel Cummings

GitHub Profile


Normalizing Flows in JAX

Differentially Private Mixed-Type Data Generation

PyVacy: Differentially Private Optimization Algorithms for PyTorch

Reversible Flow Models for Improved Biometric Verification Systems


When Differential Privacy Might Be Most Useful

An Introduction to Differentially Private Deep Learning


CS231n: Convolutional Neural Networks for Visual Recognition
Stanford University, Spring 2020

CS230: Deep Learning
Stanford University, Winter 2020

CS221: Artificial Intelligence
Stanford University, Autumn 2019

CS7646: Machine Learning for Trading
Georgia Institute of Technology, Autumn 2018

CS3600: Artificial Intelligence
Georgia Institute of Technology, Autumn 2017

CS1331: Object-Oriented Programming
Georgia Institute of Technology, Summer 2016


Privacy-Preserving Deep Learning
CS271: AI in Healthcare, Stanford University, Winter 2020.

Differentially Private Synthetic Data Generation
Two Sigma Investments, Summer 2019.

Introductory Topics in Theoretical Computer Science
Facebook, Inc., Spring 2018.

What I’m Reading

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

Adversarial Examples Are Not Bugs, They Are Features

On the Measure of Intelligence

Privacy and Synthetic Datasets

Keeping Top AI Talent in the United States

Deep Learning for Symbolic Mathematics

Building Machines That Learn and Think Like People

Deep Image Reconstruction from Human Brain Activity

The Linear Algebra Mapping Problem

Neural Turing Machines