I earned my Ph.D. in Computer Science in 2023 from USC, advised by Aleksandra Korolova in the CS Theory Group.
My research explored privacy-preserving algorithms, focusing on making differential privacy practical for real-world use.

Prior to USC, I completed my undergraduate studies at Virginia Tech, where I earned my B.S. in Computer Science, B.S. in Mathematics, B.A. in Economics, and B.S. in Statistics. Among many other things at VT, I was an undergrad research assistant under T. M. Murali, where I explored hypergraph algorithms and their applications to biological networks. Most notably, I was the primary designer and implementer of halp, the hypergraph algorithms package.


Practice-Inspired Trust Models and Mechanisms for Differential Privacy
Brendan Avent
Ph.D. Dissertation. 2023. [paper]

Pushing the Boundaries of Private, Large-Scale Query Answering
Brendan Avent, Aleksandra Korolova
Workshop on Privacy-Preserving Artificial Intelligence (PPAI). 2023. [paper]

Advances and Open Problems in Federated Learning

Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G.L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Foundations and Trends in Machine Learning. 2021. [paper]

Automatic Discovery of Privacy-Utility Pareto Fronts
Brendan Avent, Javier Gonzalez, Tom Diethe, Andrei Paleyes, Borja Balle
Proceedings on Privacy Enhancing Technologies. 2020. [video] [code] [paper]
★★ Andreas Pfitzmann Best Student Paper Award  ★★
Presentation in Data Privacy: From Foundations to Applications Workshop @Simons Institute. 2019.
Poster in Privacy Preserving Machine Learning Workshop @CCS. 2019.

The Power of the Hybrid Model for Mean Estimation
Brendan Avent, Yatharth Dubey, Aleksandra Korolova
Proceedings on Privacy Enhancing Technologies. 2020. [video] [paper]
Presentation in Privacy in Machine Learning Workshop @NeurIPS. 2018.

BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model
Brendan Avent, Aleksandra Korolova, David Zeber, Torgeir Hovden, Benjamin Livshits
26th USENIX Security Symposium. 2017. [video] [paper]
Presentation in Private and Secure Machine Learning Workshop @ICML. 2017.
Presentation in 3rd Workshop on the Theory and Practice of Differential Privacy @CCS. 2017.

Pathway Analysis with Signaling Hypergraphs
Anna Ritz, Brendan Avent, T. M. Murali
ACM/IEEE Transactions on Computational Biology and Bioinformatics. 2015. [paper]


Hypergraphs: Algorithms, Implementations, and Applications
Brendan Avent, Anna Ritz, T. M. Murali
VTURCS Research Symposium. Poster. 2015. [code] [poster]

Machine Learning Models for Terrestrial Space Weather Forecasting
Brendan Avent, Nicholas Sharp, Dhruv Batra
SIAM Annual Meeting. Undergrad Presentation. 2014. [code]

Teaching Assistantships

Algorithm Analysis
CSCI 270: Spring 2018 and Fall 2017 with Aaron Cote
CS 4104: Spring 2014 with T. M. Murali
CS 4104: Fall 2013 with Lenwood Heath

Privacy in the World of Big Data
CSCI 599: Spring 2017 and Spring 2016 with Aleksandra Korolova

Intermediate Data Analytics & Machine Learning
CS/Stat 4654: Spring 2015 with Byron Smith

Theoretical Statistics II
Stat 4106: Spring 2015 with Bill Woodall