Chris Waites

I am a second year Master's student in Computer Science and Public Policy at Stanford University. My research is centered around trustworthy machine learning, spanning topics in privacy, fairness, interpretability and robustness.

In the past I've spent time doing machine learning research at Nuro, as well as engineering at Facebook, Two Sigma, and Airbnb.

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I'm interested in trustworthy machine learning (privacy, fairness, robustness, interpretability) and its implications on policy.

clean-usnob Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation
Chris Waites, Rachel Cummings
In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES21), 2021

Normalizing flow models have risen as a popular solution to the problem of density estimation, enabling high-quality synthetic data generation as well as exact probability density evaluation. However, in contexts where individuals are directly associated with...

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

We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data...

Stanford University

CS231n: Convolutional Neural Networks for Visual Recognition

CS224n: Natural Language Processing with Deep Learning

CS230: Deep Learning

CS221: Artificial Intelligence
Georgia Institute of Technology Georgia Institute of Technology

CS 7646: Machine Learning for Trading

CS 3600: Artificial Intelligence

CS 1331: Object-Oriented Programming

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