Connor Wagaman

[mylastname] [at] bu.edu

I am a PhD student in computer science at Boston University, where I am advised by professors Adam Smith and Marco Gaboardi. I am interested in data privacy (e.g., differential privacy), especially privacy for network data.

I graduated with an AB and SM (advanced standing) in computer science from Harvard University, where I did research in differential privacy with Professor Salil Vadhan.

Research

Authors are listed alphabetically.

• Refereed Learning.

Ran Canetti, Ephraim Linder, Connor Wagaman.

Manuscript, October 2025.

• Time-Aware Projections: Truly Node-Private Graph Statistics under Continual Observation.

Palak Jain, Adam Smith, Connor Wagaman.

Presented at Oakland (IEEE S&P), May 2024.

• Widespread Underestimation of Sensitivity in Differentially Private Libraries and How to Fix It.

Sílvia Casacuberta, Michael Shoemate, Salil Vadhan, Connor Wagaman.

Presented at ACM CCS 2022 and TPDP 2022 (selected for a spotlight talk).

• Finite-Precision Arithmetic Isn’t Real: The Impact of Finite Data Types on Efforts to Fulfill Differential Privacy on Computers.

Connor Wagaman.

Senior thesis, Harvard University (highest honors). Advised by Professor Salil Vadhan.

Teaching assistantships

• Boston University CS 330 – Introduction to Analysis of Algorithms: Fall 2023, Spring 2026.

• Harvard CS 208 – Applied Privacy for Data Science: Spring 2022.

• Harvard CS 20 – Discrete Mathematics for Computer Science: Spring 2021, Spring 2020.