About Me

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

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 algorithms and lower bounds for fundamental tasks such as high-dimensional mean estimation.

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.

Recent Publications

• Strong Memory Lower Bounds for Learning Natural Models.
  Gavin Brown, Mark Bun, and Adam Smith
  To appear at COLT 2022.

• Performative Prediction in a Stateful World.
  Gavin Brown, Iden Kalemaj, and Shlomi Hod.
  AISTATS 2022. Proceedings version.
  A preliminary version of this paper appeared at the NeurIPS Workshop on Consequential Decision Making in Dynamic Environments, 2020.

• Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
  Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, and Lydia Zakynthinou.
  NeurIPS 2021 Spotlight Presentation. Proceedings version.

• 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.

Presentations

• 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.

Program Committees

• ACM FAccT 2022

• Theory and Practice of Differential Privacy, 2022

Teaching Fellowships

• 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.