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.

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...

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...

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