# Gavin Brown

grbrown (at) bu (dot) edu

Hello! I am a fifth-year PhD student in the Boston University Department of Computer Science. I am fortunate to be advised by Adam Smith. I'm originally from Ohio, and received a BS in Mathematics from Case Western Reserve University in 2015. My Senior Capstone project was advised by David Gurarie. Before coming to BU, I worked as a data analytics consultant for Mu Sigma.

I do research on the theory of data privacy and machine learning. I want to understand when and why machine learning models memorize large parts of their training data. I also work on differential privacy, and am interested in both creating algorithms for fundamental tasks and proving lower bounds.

I deeply enjoy teaching, both in and out of the classroom. In 2020, I received a Teaching Fellow Excellence Award from the Computer Science Department.

**Covariance-Aware Private Mean Estimation Without Private Covariance Estimation**

Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, and Lydia Zakynthinou.

*To appear as a Spotlight Presentation at NeurIPS 2021.***When is Memorization of Irrevelant Training Data Necessary for High-Accuracy Learning?**

Gavin Brown, Mark Bun, Vitaly Feldman, Adam Smith, and Kunal Talwar.

STOC 2021. Proceedings version.**Performative Prediction in a Stateful World.**

Gavin Brown, Iden Kalemaj, and Shlomi Hod.

NeurIPS Workshop on Consequential Decision Making in Dynamic Environments, 2020.

**When Is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?**

STOC 2021. Talk and poster presentation.**When Is Memorization of Entire Examples Necessary for High-Accuracy Learning?**

Penn State University Statistical Data Privacy Seminar. 2021. Talk.**When Is Memorization of Entire Examples Necessary for High-Accuracy Learning?**

Hebrew University Theory Seminar. 2021. Talk.**When Is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?**

Workshop on the Theory of Overparameterized Machine Learning 2021. Lightning talk.**When Is Memorization of Entire Examples Necessary for High-Accuracy Learning?**

Boston University Probability and Statistics Seminar. 2021. Talk.**When Is Memorization of Entire Examples Necessary for High-Accuracy Learning?**

Google Differential Privacy Workshop 2021. Poster presentation.

CS 537 - Randomness in Computing (Sofya Raskhodnikova). Graduate Class. Spring 2020.

CS 330 - Introduction to Algorithms (Adam Smith and Dora Erdos). Undergraduate Class. Fall 2019.

CS 542 - Machine Learning (Peter Chin). Graduate Class. Summer 2019, Session I.

CS 112 - Introduction to Computer Science II (Christine Papadakis-Kanaris). Undergraduate Class. Fall 2018.

CS 542 - Machine Learning (Peter Chin). Graduate Class. Spring 2018.