The C&O department has 36 faculty members and 60 graduate students. We are intensely research oriented and hold a strong international reputation in each of our six major areas:
- Algebraic combinatorics
- Combinatorial optimization
- Continuous optimization
- Cryptography
- Graph theory
- Quantum computing
Read more about the department's research to learn of our contributions to the world of mathematics!

News
Three C&O faculty win Outstanding Performance Awards
The awards are given each year to faculty members across the University of Waterloo who demonstrate excellence in teaching and research.
Prof. Alfred Menezes is named Fellow of the International Association for Cryptologic Research
The Fellows program, which was established in 2004, is awarded to no more than 0.25% of the IACR’s 3000 members each year and recognizes “outstanding IACR members for technical and professional contributions to cryptologic research.”
C&O student Ava Pun receives Jessie W. H. Zou Memorial Award
She received the award in recognition of her research on simulating virtual training environments for autonomous vehicles, which she conducted at the start-up Waabi.
Events
Tutte colloquium-Xi He
Title:Accuracy Aware Minimally Invasive Data Exploration For Decision Support
Speaker: | Xi He |
Affiliation: | University of Waterloo |
Location: | MC 5501 |
Abstract: Decision-support (DS) applications, crucial for timely and informed decision-making, often analyze sensitive data, raising significant privacy concerns. While privacy-preserving randomized mechanisms can mitigate these concerns, they introduce the risk of both false positives and false negatives. Critically, in DS applications, the number of false negatives often needs to be strictly controlled. Existing privacy-preserving techniques like differential privacy, even when adapted, struggle to meet this requirement without substantial privacy leakage, particularly when data distributions are skewed. This talk introduces a novel approach to minimally invasive data exploration for decision support. Our method minimizes privacy loss while guaranteeing a bound on false negatives by dynamically adapting privacy levels based on the underlying data distribution. We further extend this approach to handle complex DS queries, which may involve multiple conditions on diverse aggregate statistics combined through logical disjunction and conjunction. Specifically, we define complex DS queries and their associated accuracy requirements, and present algorithms that strategically allocate a privacy budget to minimize overall privacy loss while satisfying the bounded accuracy guarantee.