Computer science student @

🌲Stanford University 🌲

Broadly interested in deep learning,

generative modeling, privacy,

and adversarial ML.

JAX-Flows: 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

Definition of differential privacy, the Laplace mechanism, differentially private stochastic gradient descent (DP-SGD), and PATE.

*CS271: AI in Healthcare, Stanford University, Winter 2020.*

Differentially Private Synthetic Data Generation

Definition of differential privacy, differentially private stochastic gradient descent (DP-SGD), and generative adversarial networks.

*Two Sigma Investments, Summer 2019.*

Introductory Topics in Theoretical Computer Science

Computability, deterministic and nondeterministic finite automata, regular languages, the pumping lemma, and Turing machines.

*Facebook, Inc., Spring 2018.*

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