Christopher Waites

I'm currently an M.S. in CS candidate at Stanford University, focusing on Artificial Intelligence. I did my bachelors at the Georgia Institute of Technology, advised by Rachel Cummings. I'm interested in a lot of things, mostly relating to deep learning, computer vision, reinforcement learning, generative modeling, and game theory.

I've also worked a couple of places in the past, including Two Sigma, Facebook, and Airbnb. If you ever want to chat, feel free to reach out!

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Differentially Private Mixed-Type Data Generation For Unsupervised Learning
Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva Rachel Cummings
arXiv, 2019

Improved methods for differentially private synthetic data generation via autoencoders.

PyVacy: Differentially Private Optimization Algorithms for PyTorch
Chris Waites,
Github, 2019

Implementation of differentially private stochastic gradient descent with privacy accounting utilities, sampling methods, etc.

The Efficacy of Reversible Flow Models for Improved Biometric Verification Systems
Chris Waites,
CS236: Deep Generative Models, 2019

Improving user verification and GMM-UBM via more expressive, reversible flow models.

Differentially Private Synthetic Data Generation
Two Sigma Investments, 2019

Topics covered the definition of differential privacy, differentially private stochastic gradient descent (DP-SGD), and generative adversarial networks.

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

Topics covered computability, deterministic and nondeterministic finite automata, regular languages, the pumping lemma, and Turing machines.

cs221 CS230: Deep Learning
Stanford University
Winter 2020
cs221 CS221: Artificial Intelligence
Stanford University
Autumn 2019
cs3600 CS7646: ML for Trading
Georgia Institute of Technology
Autumn 2018
cs3600 CS3600: Artificial Intelligence
Georgia Institute of Technology
Spring 2017
cs188 CS1331: OO Programming
Georgia Institute of Technology
Spring 2016
An Introduction to Differentially Private Deep Learning

What I've Been Reading
Keeping Top AI Talent in the United States

Deep Learning for Symbolic Mathematics

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

Counterfactual Regret Minimization

Why Be Random?
Deep Image Reconstruction from Human Brain Activity

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

The Linear Algebra Mapping Problem

Neural Turing Machines

Text Feature Selection for Causal Inference