Welcome to The Salon!

The Salon is a group dedicated to the study of Statistics, Algorithms, Learning, and OptimizatioN. It is led by Professor Gautam Kamath at the Cheriton School of Computer Science at the University of Waterloo. Its members are also affiliated with the Vector Institute. Current emphases of the group include enriching our understanding of data privacy and robustness in statistics and machine learning.

The name of The Salon is in reference to the French practice from the 17th and 18th centuries, a central venue for exchange of some of the most important ideas of the era.


Recent Publications

Exploring the Limits of Indiscriminate Data Poisoning Attacks

Robustness Implies Privacy in Statistical Estimation

Challenges towards the Next Frontier in Privacy

Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance

Private GANs, Revisited


Le Salonneur


Gautam Kamath

Assistant Professor of Computer Science

Statistics, Machine Learning, Data Privacy, Robustness

Postdoctoral Researchers


Vikrant Singhal

Postdoctoral Fellow

Statistics, Machine Learning, Data Privacy

Graduate Researchers


Alex Bie

MMath Computer Science Student

Privacy, Fairness


Argyris Mouzakis

PhD Computer Science Student

Machine Learning Theory, Algorithmic Statistics, Privacy, Applied Probability


Matthew Regehr

MMath Computer Science Student

Machine Learning, Privacy, Reinforcement Learning


Sabrina Mokhtari

MMath Computer Science Student

Privacy and Fairness in Machine Learning, Computer Vision


Sara Kodeiri

MMath Computer Science Student

Privacy, Natural Language Processing

Undergraduate Researchers


Chris Trevisan

Bachelor of Computer Science

Design and Analysis of Algorithms, Graph Theory, Random Processes


Jack Douglas

Bachelor of Software Engineering Student

Machine Learning, Data Privacy, Statistics


Jimmy Di

BMath in Computer Science Student

Machine Learning, Privacy, Robustness

Affiliated Researchers


Shubhankar Mohapatra

PhD Computer Science Student

Data Privacy, Machine Learning, Federated Learning, Data Cleaning